For many organizations, the contact center is a vital customer service hub, making it a prime target for fraudsters. As the first point of contact for many customer interactions, it’s where attackers often attempt to exploit weak authentication processes or impersonate legitimate users.

Thanks to AI-powered tools, fraud attacks are getting faster, more organized, and more convincing. Meanwhile, the defenses many contact centers rely on, like manual reviews or knowledge-based authentication (KBA), weren’t built to handle the volume or sophistication of today’s attacks.

If you’re still relying on security questions or reviewing suspicious calls after the fact, you’re likely to detect fraud when it’s too late and frustrate legitimate customers.

So, how do you modernize fraud detection without adding more friction to the customer experience?

Contact center fraud is more rampant than ever

Pindrop data indicates that in recent years, contact centers across industries have seen a surge in fraud:¹

Fraud now occurs in 1 in every 599 incoming calls on average

This represents a 26% increase in fraud over 2023, and a 100% increase compared to 2021

Today, fraud attempts occur approximately every 46 seconds within contact centers

Yet many organizations still rely on outdated tools that are reactive, not proactive:

53% of fraudsters passed KBAs (Knowledge-Based Authentication)

22% successfully bypassed OTPs (One-Time Passwords)

Legacy authentication systems may flag fraud after a claim is paid or a customer account is compromised, but by then, the damage is done.

Modern fraud detection for modern threats

To keep up, modern contact centers need fraud detection that’s:

Proactive: Detects fraud before it reaches a live agent

Real-time: Works instantly, without waiting for enrollment or call history

Frictionless: Minimizes extra steps for legitimate customers

Integrated: Fits easily into existing call flows and infrastructure

This is where fraud detection that incorporates voice analysis, device intelligence, and behavioral risk scoring can make a difference by flagging high-risk calls the moment they come in.

One agency’s journey to smarter fraud detection

“You always need the balance of keeping your legitimate customers happy with stopping your fraudulent customers. Pindrop allowed us to do both, and we could customize it to fit within our system. That was one big selling point for us when we implemented it”

– Charles Boyd, Supervisor at VEC

What does this shift look like in practice?

A state agency responsible for handling unemployment claims—one that serves nearly a million calls a year—faced exactly this challenge. After years of relying on legacy authentication tools, the team needed a way to:

Spot fraud during the call, not weeks later

Reduce strain on agents reviewing suspicious cases

Improve the experience for genuine claimants

They deployed Pindrop^® Protect, and in less than a year, they transformed their fraud strategy. The results: real-time detection, faster investigations, and nearly a million dollars saved from potential fraud losses.¹

Read the full case study here to see how they did it, and what your team can learn from their approach.

¹All data from Pindrop 2025 Voice Intelligence + Security Report

Pindrop was recently featured on CBS Mornings, showcasing how the rise of synthetic media is transforming the landscape of fraud and how we’re helping organizations fight back.

Synthetic voices. Altered faces. Real risks.

In a segment with correspondent Kelly O’Grady, we demonstrated just how sophisticated and accessible deepfake technology has become and how attackers are already using it to infiltrate high-stakes virtual meetings.

From voice impersonation to live face swaps, synthetic threats are evolving fast and eroding trust in every interaction. Maintain integrity in your virtual meetings with tools that detect deepfakes quickly.

AI voice + video makes way for fake job applicants

This wasn’t just a discussion. We showed deepfake technology in action:

Real-time face swap in a live Zoom meeting

Using publicly available software, we transformed Kelly O’Grady’s face in real time on a Zoom call, simulating how attackers can impersonate executives or colleagues to scam individuals and organizations.

AI voice clone with live dialogue

Next, we used a tool to create a synthetic voice clone of Kelly capable of holding a dynamic conversation, not just repeating pre-recorded lines. This is eerily similar to how fraudsters are now tricking teams into transferring funds or disclosing sensitive info.

Deepfake job applicants

Pindrop has uncovered real-world cases where fraudsters used synthetic audio and video to impersonate candidates during job interviews.

These aren’t future risks. They’re active threats, already live in meetings like yours.

Introducing Pindrop^® Pulse for meetings

To defend against these attacks, we created Pindrop^® Pulse for meetings, a real-time deepfake detection tool that integrates directly with meeting software like Zoom or Webex.

Pindrop^® Pulse is designed to act as a virtual security assistant, analyzing participants’ audio and video in real time to detect:

Introducing Pindrop^® Pulse for meetings

To defend against these attacks, we created Pindrop^® Pulse for meetings, a real-time deepfake detection tool that integrates directly with meeting software like Zoom or Webex.

Pindrop^® Pulse acts as a virtual security assistant, analyzing participants’ audio and video in real time to detect:

AI-generated voices and synthetic speech patterns

Face-swapped or altered video feeds

Behavioral anomalies suggesting impersonation

By embedding fraud detection directly into your live meetings, Pindrop® Pulse helps teams take immediate action before trust is compromised.

Fraud looks + sounds different

If you lead fraud, risk, or cybersecurity operations, you’re no longer just defending systems, but also every interaction.

Trust is the new attack surface

With just a few minutes of audio or video, attackers can convincingly impersonate:

Executives

Customers

Job applicants

Partners

How Pindrop^® Pulse can support your team

Extend your fraud defenses where they’re needed most: in real-time, human conversations. It’s ideal for:

Live fraud detection during high-value calls

Establishing call integrity during high-stakes discussions

Better protection during hiring by screening for deepfake job candidates

Unlike reactive tools, Pindrop^® Pulse delivers proactive defenses with analysis, liveness scoring, and minimal operational disruption.

It’s backed by the same deep audio analysis trusted by some of the biggest banks, insurers, and retailers.

“Who’s on the call?” isn’t enough anymore

Today, a familiar face or voice can be generated in seconds and used to deceive. Whether it’s a fraudulent job applicant or a face-swapped executive, deepfakes are designed to appear real. Pindrop^® Pulse brings live detection into your meetings, scanning for subtle signs of AI manipulation and helping your team separate real participants from synthetic threats.

Pindrop^® Pulse for meetings brings fraud detection into virtual meeting rooms, enabling your team to flag risks, verify identities, and better protect your business with confidence.

Watch the CBS Mornings segment to see Pindrop^® Pulse in action or talk to a Pindrop expert to explore how deepfake detection fits into your security stack.

Pindrop® Protect helped the Virginia Employment Commission (VEC) reduce fraudulent claims, potentially saving nearly $1M on an annualized basis. VEC, a 92-year-old organization, serves more than 900k in unemployment claims annually,¹ and was one of the first state agencies to adopt advanced fraud detection technology for unemployment insurance fraud reduction.

The Virginia Employment Commission (VEC), a crucial arm of the State of Virginia, provides employment optimization services to statewide employers and handles all of the state’s unemployment insurance claims processes. Concern at the VEC was growing about the high level of fraud activity in its call centers. The team was looking for a solution to help detect fraud and craft a strategy to mitigate future fraud risks.

Our Pindrop® Protect solution helped VEC detect over 800 potentially fraudulent calls in their call centers in one year, provide better service to their customers, and improve agent productivity by reducing the use of knowledge-based authentication (KBA) questions.

About the VEC

The VEC, formed in 1933, is a public employment service established to assist employers in finding qualified workers and to help employees find jobs. VEC administers the state’s unemployment insurance benefits program, providing financial assistance to unemployed individuals and supporting workers, families, and communities while stabilizing the state’s economy.

VEC is the largest single source of job candidates in Virginia, with nearly half a million job seekers registered annually.² In 2024 alone, VEC paid $259M in unemployment benefits for +126k initial claims and +928k continued claims.¹

VEC was concerned about the rise in fraud, particularly the growing number of fraudsters targeting the agency’s contact center with unemployment claims. The VEC team reached out to Pindrop to help modernize their contact center fraud detection framework and help protect the funds that serve the people of Virginia.

Challenges

Since the COVID-19 pandemic, VEC faced an increase in fraudulent activity because of the rise in unemployment levels, combined with large government payouts such as the Pandemic Emergency Unemployment Compensation (PEUC). Post-pandemic, VEC lost an estimated $350M to fraud in three years. This rise in fraud is consistent with Pindrop internal data, which shows a +39% increase in fraud activity across U.S. contact centers during the same period.³ VEC was concerned that their existing authentication system was not equipped to handle this elevated fraud risk. VEC needed a solution to help modernize their call center security framework, alert them to the fraud, and help reduce the authentication burden on their genuine customers.

Goals

VEC had three goals as they sought a new solution:

Detect fraudulent call center activity in real time

Build a larger threat response and case investigation mechanism around the new solution

Reduce usage of legacy authentication methods

With these goals in mind, VEC wanted to proactively detect and mitigate fraud instead of reacting to events on a passive, case-by-case basis. Additionally, VEC leadership wanted a more comprehensive view of risk across the call center.

Before Pindrop® Solutions

Before Pindrop, VEC’s fraud detection policies and actions relied on feedback from its fraud research department in Richmond, VA, which investigated cases and determined if fraud actually took place. Contact center leadership also leveraged tools like LexisNexis, which required the caller to verify their personal information through a “fraud quiz” that leveraged personal information pulled from public identity databases (street addresses, vehicles owned).

However, there were two major problems with the quiz:
1. Genuine customers were not able to verify the information and answer the quiz correctly, resulting in those customers being turned away and denied service.
2. Fraudsters were often successful at passing the quiz, which weakened its effectiveness as a security mechanism.

Pindrop internal data corroborates that Knowledge Based Authentication (KBAs) using personal information from public records is not effective as a security mechanism. After analyzing 2.5k fraud calls across 10 financial institutions, Pindrop found that fraudsters successfully passed KBA 53% of the time on average, with success rates ranging from 9% to as high as 90%.³

VEC’s existing methods were not catching fraud, nor were they conducive to improving customer experience.

VEC was ready for a change.

Why Pindrop

Pindrop provides a proactive vs. a reactive solution

VEC’s call center processes nearly 30k calls per month.⁴ In many instances, the callers opt for a callback due to wait times. Any callbacks that were considered suspicious by call center staff were flagged for leadership team review. This process was labor-intensive and delayed the unemployment insurance claims of legitimate claimants. Additionally, reviews happened after the fact, so they did not stop fraudsters or fraudulent claims in real time. This process resulted not only in monetary losses but also in negative customer feedback.

“We were seeing so many [fraud events] after the fact, so we wanted to get a product that would catch the fraud at the front end, instead of six months down the road.”

– Charles Boyd, Supervisor at VEC

VEC required a fraud detection system that could detect fraud early, not after the fact. They also required a solution that could seamlessly add to their existing call center infrastructure, help the call center and leadership teams differentiate between high-risk calls and genuine claimants, and make the fraud detection process smoother and more effective.

Pindrop® Protect was the real-time, multifactor solution with an industry-leading fraud detection capability that VEC was looking for. In addition to assisting in fraud investigations, the ability of Pindrop® Protect to safeguard their contact center at all stages of a phone call, from IVR to the agent leg, was a key factor in the team’s decision to choose Pindrop.

“At the end of the day, Pindrop fit best with what we needed to uncover and mitigate fraud, and it was an easier migration and integration with our system.”

– Charles Boyd, Supervisor at VEC

The VEC team also valued the ability to customize and seamlessly integrate the Pindrop® Protect solution into their existing infrastructure. Protect enabled the team to build their fraud detection policies and processes around real-time alert capabilities.

For example, the Protect solution made the fraud detection process easier for call center agents by adding color codes to their screens and providing screen pops when fraud was detected, with no disruption to the customer experience. Pindrop® Protect also helped VEC move away from fraud quizzes, lowering frustration for the claimants and improving satisfaction for the agents.

“You always need the balance of keeping your legitimate customers happy with stopping potential fraudsters. Pindrop allowed us to do both, and we could customize it to fit within our system. That was a big selling point for us when we implemented the Protect solution.”

– Charles Boyd, Supervisor at VEC

Why did VEC choose Pindrop®?

Multifactor fraud detection that alerts to risks in real time

Seamless integration with existing infrastructure

Balance of fraud detection with reduced friction for claimants

What ROI did Pindrop® Solutions deliver?

VEC deployed the Pindrop® Protect solution in their IVR as well as the agent leg of the contact center.

Better fraud tracking

With Pindrop® Protect, VEC is now in a better position to track fraudulent claims, spot fake employer accounts, and identify and prevent monetary loss. With better fraud tracking, the commission has prevented account takeovers and identity thefts and detected fraudulent account openings.

Fraud insights from Protect also helped VEC identify a fake employer case, which was leading to fraudulent claims. In this case, the fraudster set up fictitious employers and provided fake documentation about layoffs using stolen identities to steal money. Pindrop® Protect alerted the Commission to this high-risk activity, which led to an investigation and discovery of the fraudulent scheme.

“Pindrop not only identifies a lot [of fraud] for us, but also gives us robust tracking. It flags a call, gives us a score, and allows us to expand that out and do further investigations within it. This has helped us build the overall fraud detection system.”

– Charles Boyd, Supervisor at VEC

Fraud detection in the IVR

VEC’s call center has a wait time of +8 minutes,⁴ so they offer customers a callback rather than making them wait. This approach helps reduce the number of callers reaching an agent on their first call, but it creates the challenge of monitoring high-risk calls in the IVR, where customers spend their time.

VEC started using the ‘Risk API’ feature of the Pindrop® Protect solution to determine caller risk in the IVR. Risk API allows VEC to pull call risk scores in real time in the IVR, even when the caller is not speaking. This capability helped VEC classify IVR calls as ‘high-risk’ or ‘low-risk’. Instead of VEC calling back every caller, they can now call back only low-risk callers, redirecting high-risk calls to a fraud agent.

Pindrop® Protect account risk analysis capabilities helped VEC make an important change to their IVR security policies: it facilitated their ability to connect the dots between different fraudulent claimants with insight into signals which can indicate malicious activity, like whether an address on a claim matches the address on other claims, or if multiple claims are listed under the same address or phone number. With Pindrop® Protect, calls with these signals can be immediately stopped or redirected to fraud investigators before resulting in further account takeovers.

VEC used these insights to update its IVR call handling policies to flag additional suspicious claims that share these commonalities.

Richer fraud insights with ‘Custom Attributes’

‘Custom Attributes’ is a PindropⓇ Protect feature that provides the flexibility to define custom fields and gather specific metadata in the case investigation UI, and to get relevant and consistent context for enhanced call analysis.

Using Custom Attributes, VEC investigated fraud cases associated with accounts having a certain balance threshold, which helped VEC conduct more targeted and high-impact investigations. Prior to using Protect, the team did not have visibility into the resolution of the fraud cases because the cases took months to resolve. With Custom Attributes, VEC has better visibility into case investigations and can track them all the way to the end.

Fraud detection and loss avoidance

Pindrop® Protect was deployed by VEC in April 2024. Since then, Protect has detected potential fraud at the rate of 1 in every 2,529 calls to the call center, with potential savings of over half a million dollars ($560k) in fraud loss avoidance. On an annualized basis, Protect is on track to potentially save VEC almost $1M ($840k) in fraud losses.

Fast implementation and results

Pindrop® Protect leverages multiple factors, including analysis of a caller’s voice, calling device, behavior, account risk, and carrier risk. It also includes an add-on liveness detection module, which helps detect whether a caller’s voice indicates the presence of synthetic elements. Additionally, Protect customers benefit from the Pindrop Consortium, the call center industry’s largest database of confirmed fraudsters. Protect has the flexibility to analyze any of these available factors at different call stages and return a real-time risk score that can help call centers make quick decisions about the risk of the calls and their treatment.

“We were up and running really fast. Protect worked great, and it did what we wanted to do out of the box and was catching fraud on day one.”

– Charles Boyd, Supervisor at VEC

Protect customers do not need to wait to enroll callers’ voice profiles, or rely only on ANI (Automatic Number Identification) and voice blocklists to detect fraud. Instead, they can start detecting fraud on day one by leveraging a variety of factors with Pindrop® Protect.

¹ Virginia Works: Statewide Economic Analysis Report 2023-24
² https://www.vec.virginia.gov/about-vec
³ Pindrop, 2025 Voice Intelligence + Security Report
⁴ https://vec.virginia.gov/news/agency-monthly-performance-report-february-2022

Your contact center may already be under attack—from voices that aren’t real.

Pindrop’s 2025 Voice Intelligence + Security Report offers an in-depth, data-driven look at how AI is reshaping fraud across contact centers, and what to do about it.

Backed by data from over a billion calls and firsthand fraud cases, this report helps business leaders understand the true scope of synthetic voice attacks and how to defend against them.

What’s inside the report?

From the rise of deepfake fraud to the breakdown of outdated authentication methods, here’s a preview of what you’ll find:

Deepfake fraud in contact centers: 2025 trends and shocking stats

In 2024, voice fraud spiked, and fast. According to Pindrop’s analysis of over 1.2 billion calls:

680% rise in deepfake activity (year-over-year)

26% increase in fraud attempts, far exceeding predictions

475% increase in synthetic voice fraud in insurance

1 of every 127 calls to retail contact centers was fraudulent, on average

AI voice tools aren’t experimental—they’re already in use, and they’re working.

Agentic AI and voice deepfakes are redefining trust and identity

The question isn’t just “Are you who you say you are?”—it’s “Are you even human?”

Fraudsters now use AI-powered tools and real-time voice modulation to mimic real people with startling accuracy. Identity checks that once held the line are starting to fail.

This report shows how deepfakes are bypassing defenses—and what security teams need to do to stop them.

Authentication is under attack

Legacy methods like KBAs (Knowledge-Based Authentication) and OTPs (One-Time Passwords) are no longer reliable:

53% of fraudsters passed KBA checks

1 in 4 passed OTP challenges

Caller ID spoofing showed up in over 16% of confirmed fraud cases

Organizations should pivot to multifactor authentication, real-time liveness detection, and risk scoring to secure every interaction.

2025 fraud forecast: What’s next in voice fraud?

Based on analysis in the 2025 Voice Intelligence + Security Report, here are four key predictions to watch:

Deepfake fraud could rise +162% this year

Contact center fraud could reach $44.5B

Retail fraud may double again, reaching 1 in every 56 calls

Real-time communications platforms (e.g., Zoom, Teams) are the next deepfake frontier

The future of voice security starts now

The 2025 report also outlines clear actions security leaders can take now to get ahead of growing threats:

Understand how fraudsters are using synthetic voices today

Spot the signs of deepfake activity in your contact center

Evaluate your authentication stack and where it’s most exposed

Don’t wait for a breach to prove the risk is real.

Download the report and take back control of voice security.

^{*All data sourced from 2025 Voice Intelligence + Security Report, Pindrop}

Annual Report

2025 Voice Intelligence and Security Report

Contact centers are under siege—an estimated $12.5B lost to fraud in 2024*, driven by AI threats. With 2.6M fraud events reported*, deepfakes and synthetic voices are overwhelming legacy defenses. Learn how to better secure every conversation and protect trust in an AI-first world.

^{*Pindrop, 2025 Voice Intelligence + Security Report}

What’s in the report

Pinpoint ways audio deepfakes are slipping past voice authentication—and what detection layers still appear to hold up.
Track how AI fraud rings are mimicking human agents to launch real-time, scalable attacks on contact centers.
Break down the three biggest vulnerabilities in automated phone systems that attackers are exploiting today.
Explore how top contact centers are combining voice analysis, behavior, and intention modeling to block fraud.

ANNUAL REPORT

2024 Voice Intelligence and Security Report

Fraudsters have a new tool for gaining entry into contact centers and extracting private data: voice deepfake technology. Discover data-driven solutions to protect your business and customers from advanced fraud techniques.

Click here to download the guide.

Retailers have always faced challenges with returns. However, 2024 saw a notable surge in return and claim fraud, to the tune of $103 billion in losses, according to a consumer returns report by Appriss Retail and Deloitte. More findings include:

Fraudulent returns accounted for roughly 15.14% of the projected $685 billion in returns this year, adding significant pressure to an industry with already slim profit margins.

Total returns comprised 13.21% of overall retail sales, reaching $5.19 trillion, according to the report.

Throughout 2024, many retailers have tightened their returns policies to address surging financial losses. However, these measures can (and have) backfire by alienating legitimate customers.

As we’ll see in this article, return fraud can take many forms, from receipt fraud to “wardrobing.” We’ll dive into the most common tactics, highlight examples of top cases, and explore best practices for preventing these schemes in 2025 and beyond.

What is return fraud in retail?

Return fraud involves customers exploiting a retailer’s return policies for personal gain. While many returns are genuine, where a buyer returns an item that’s defective or unwanted, fraudsters often leverage policies or loopholes in store or online procedures to secure unmeritorious refunds.

This misconduct may involve stolen merchandise, falsified receipts, or other manipulations intended to unlawfully receive money or store credit.

Within the retail industry, there’s an ongoing tension between maintaining customer-friendly policies and preventing systemic abuse, and it can look like:

Some companies may opt for no-questions-asked returns to boost customer satisfaction, only to experience higher levels of fraud because the system is too lenient.

Others implement strict guidelines but risk frustrating honest buyers.

Striking the right balance is crucial for mitigating losses while preserving brand loyalty.

How fraudsters use return fraud

Despite an uptick in security measures, return fraud remains a significant concern for retailers. Appriss Retail’s research reveals the most common types of return fraud and abuse reported in 2024. Let’s examine the most prevalent tactics:

60% of retailers noted “wardrobing,” where consumers buy an item, use it, then return it for a full refund.

55% cited returns of items obtained through fraudulent or stolen tender, like stolen credit cards, counterfeit bills, or gift cards obtained via scams.

48% of retailers face incidents of stolen merchandise being returned as if it were legitimately purchased.

The data underlines how quickly criminals adapt to new store policies or e-commerce systems.

Receipt fraud

Receipt fraud typically involves creating fake or altered proof of purchase to claim refunds on products that were never legitimately bought. For instance, a fraudster might photocopy a real receipt and tweak the item or price.

Another variation is “receipt swapping,” where a con artist picks up a discarded receipt with a valid return date, locates the corresponding item on store shelves, and attempts a return.

Example: Someone finds (or steals) a receipt for a high-value gadget at the local electronics shop, then steals it in-store and proceeds to “return” it using the found or doctored receipt. The store loses both the inventory and the refunded money.

Wardrobing

As noted, “wardrobing” occurs when consumers purchase items—often apparel or electronics—use them briefly, then return them for a full refund while they are still in like-new condition.

It’s commonly seen with expensive attire worn once for a special event or high-end electronics used for a short-term project, only to be returned for a refund.

Example: A customer buys a designer dress for a wedding, wears it once, and then returns it with the tags reattached. The retailer can’t sell the item at full price if wear is detected, leading to financial losses.

Cross-retailer returns

In cross-retailer returns, the fraudster purchases or steals an item from one store, then returns it to a different store that sells a similar product line. This abuse is easier to achieve if the second retailer has a lax returns policy or no universal standard for verifying barcodes and SKU numbers.

Example: A fraudster purchases a mid-priced designer purse at a discount chain but returns it to a luxury store for a higher refund or store credit. By exploiting differences in brand pricing, they gain an unearned profit.

For more tactics that plague retailers, see our discussion of loss prevention in retail—an exploration of how many small and large businesses fight back against elaborate scams, inside theft, and other manipulative behaviors.

Top example cases of return fraud in 2024

While most return fraud incidents occur on a smaller scale, retailers continue to identify creative tactics that exploit systemic vulnerabilities.

Below are the typical schemes documented in 2024 industry reports and previous case analyses (like Retail Dive) related to return fraud.

Example #1: The electronics resale racket

Retailer surveys and reports describe a pattern in which high-end electronics—like laptops or tablets—are bought legitimately, then removed from the box and replaced with items of similar weight (e.g., clay, old batteries, or even random electronics parts). Unaware returns clerks accept the “sealed” box and process a full refund.

By the time inventory staff discover the discrepancy, the fraudster may have repeated the trick at multiple store locations. This scheme is similar to documented cases from prior years as well, where losses ranged from a few thousand dollars to more significant sums.

Example #2: Fraudulent gift card exchanges

According to several industry sources, like the Division of Financial Institutions, gift card return fraud often surges post-holiday season. Scammers may use fake or stolen receipts to “return” merchandise for store credit and convert that credit into gift cards that can be resold.

Even the Federal Trade Commission consumer advice says to check out gift cards before you buy them. After all, gift card fraud is the most common form of fraud, with 26.6% of victims indicating that money was taken using gift cards or reload cards, according to Capital One’s 2024 shopping research. They also found:

Target gift cards represent the highest reported losses to fraud, with victims reporting an average of $2,500 and 30% reporting losses of over $5,000.

The median reported losses from victims of Google Play gift card scams is $1,380, the second most common card used in gift card fraud cases.

Example #3: Social media–driven scam

Past years have seen viral social media challenges encouraging consumers to exploit generous return policies by, for example, purchasing items for one-time use or making other suggestions.

Social media and messaging apps-driven scams illustrate how quickly fraudulent behavior can spread when participants believe they’re gaming “rich corporations,” ignoring the potential legal consequences. It was also recently found that online returns fraud finds a home on Telegram, costing retailers billions, by exploiting retailers’ return programs in an organized crime way.

Example #4: Fake return apps

In previous fraud cases, unscrupulous developers created mobile apps that claimed to streamline returns, enticing users to scan valid receipts and then altering the details (for instance, swapping item codes or adjusting prices).

Retailers often discover clusters of overlapping addresses or phone numbers tied to suspicious returns, ultimately prompting a police investigation or collaborative efforts with corporate security teams.

How to combat return fraud in 2025 and beyond

Given the continued rise in fraudulent returns, companies must adapt policies and security measures beyond basic checklists. Below are some recommended strategies:

Stricter verification: Barcodes and item tags that match digital receipts are required. Staff should escalate the matter if the item doesn’t scan properly in the store’s system.

Advanced data analytics: Implement machine learning to detect patterns of suspicious behavior, such as serial returners or multiple returns across geographically distant stores.

Multifactor customer authentication: Tools like phone-based verification or identity checks can thwart criminals who rely on easy refunds over remote calls. For more information, see our article on how MFA can be used in retail.

Employee training: To spot red flags early, staff handling returns should be updated with common scams (receipt switching, wardrobing, cross-retailer returns).

For a detailed look at practical countermeasures, visit our article on combating return fraud. Ultimately, taking a balanced approach—reinforcing security without sacrificing customer service—can drastically reduce the prevalence of scam returns in your store or e-commerce platform.

Mitigate losses from return fraud with Pindrop^® Solutions

As we have learned, despite tightening policies, the retail industry struggles to stop fraudsters who continually evolve their tactics. One promising solution is voice analysis.

Merchants can intercept fraudulent refunds before approval by using technology that can catch anomalies, like a mismatch in the caller’s voice or device data.

Implementing voice analysis helps merchants:

Secure return processes: Combine voice biometric authentication with standard return procedures to flag potential scammers.

Reduce fraud costs: Identify suspicious callers and reduce the number of fraudulent refunds.

Build stronger relationships: Provide genuine customers with a smoother experience by minimizing repetitive, high-friction ID verifications.

Why Pindrop?

Pindrop^®solutions are designed for businesses seeking robust, user-friendly security tactics to:

Cut down authentication costs: Leverage voice analysis in the background, enabling a thorough risk analysis without occupying agent time on repetitive queries.

Reduce distractions: Agents can focus on genuine customers instead of navigating lengthy authentication scripts.

Enroll callers in seconds: Voice analysis can be used to verify returning enrolled customers quickly.

Save agents and customers time: Enhancing the customer experience doesn’t mean compromising security—it means removing friction where it’s unneeded.

Stop deepfake attacks early: Pindrop technology can detect AI-generated voices with 99.2% accuracy, detecting advanced threats quickly.

For more on Pindrop^® solutions, including advanced voice authentication, check out voice analysis and discover how we’re helping the retail industry tackle everything from return fraud to deepfake-based impersonations.

It’s well-known that healthcare organizations manage some of the most valuable and sensitive information available. Electronic health records (EHR), personal data, payment information, and private patient histories all traverse contact centers, patient portals, and telehealth platforms. This reality makes healthcare providers prime targets for fraudsters.

Unfortunately, many facilities rely on outdated or legacy security systems, such as Knowledge-Based Authentication (KBA) or passwords. Fraudsters can guess or obtain this information and use emerging tactics like deepfake technology to trick contact center agents and automated systems.

In this article, we’ll explore why secure authentication is vital for healthcare professionals, how it differs from traditional password-based approaches, and how adopting advanced solutions can reduce fraud, protect patient data, and strengthen trust.

Understanding secure authentication

Secure authentication refers to systems and processes that confirm a user’s identity with a higher degree of reliability than standard passwords or PINs. Rather than relying solely on something a user “knows,” secure authentication often blends multiple factors—like biometrics, device intelligence, or one-time passwords—and uses advanced analytics to detect anomalies.

In a healthcare setting, contact centers and telehealth solutions often require staff and patients to share highly personal data. A robust authentication framework ensures that the right person can access these records. This involves:

Confirming the user is who they claim to be.

Identifying suspicious behaviors or technologies (e.g., AI-generated voice) designed to fool the system.

Reducing reliance on manual verification, which is prone to error and often disrupts patient-provider interactions.

Some modern strategies for detection include multifactor authentication (MFA) and dynamic risk scoring, which helps handle real-time suspicious activity. Such an approach minimizes friction for legitimate users while blocking fraud attempts before they can harm patient data or an organization’s reputation.

The unique challenges of healthcare data security

The healthcare industry faces unique threats that are not typically found in other sectors. Research shows that stolen medical records can command a high price on the black market because they contain a wealth of personal and financial details.

Additionally, healthcare providers must adhere to stringent regulations—like HIPAA—that dictate how patient information must be protected. Coupled with the continuous need to provide fast, uninterrupted care, these realities present unique data security challenges:

Valuable data: Medical histories, insurance details, and personal information are prime targets for fraudsters.

Regulatory demands: Noncompliance with HIPAA or similar laws can result in hefty fines and legal repercussions.

Complex workflows: Telehealth, EHR systems, and contact centers handle data differently, complicating security measures.

Rapidly evolving fraud techniques: Attacks such as ransomware and AI-based impersonation keep healthcare organizations constantly alert.

In short, healthcare professionals need security that fits into busy schedules and urgent care demands without hindering patient experiences. Secure authentication can address these needs by automating verification, reducing manual checks, and providing a more reliable way to confirm identities.

Authentication over passwords: what’s the difference?

Passwords have been a mainstay in digital security for decades. Yet as identity-based attacks grow more sophisticated, healthcare institutions realize that relying solely on passwords leaves significant gaps. Here is a comparison that will help us understand these more:

Criteria	Password-only authentication	Multifactor authentication
Key vulnerabilities	Weak/reused passwords Susceptibility to phishing Time-consuming resets. Forgotten or locked-out passwords require support. Ease of theft and sharing. Can be written down, shared, or stolen.	While unlikely, determined attackers can sometimes access multiple factors. Could target the additional factor (e.g., intercepting SMS codes), though generally more complex than phishing a single password.
Security level	Low, especially in industries with high-value data (like healthcare), where one password is the sole barrier to entry.	Significantly higher. Even if one factor is compromised (e.g., a leaked password), an attacker typically needs the second or third factor (e.g., a phone, a biometric) to gain full access.
Pros	Familiarity with users: This is the most common logging method. Easy to implement: No specialized infrastructure or devices required. Fast to set up: Users can create passwords with minimal guidance.	Enhanced security: Lowers the risk of unauthorized access. Reduced phishing impact: A stolen password alone isn’t enough. Better compliance: May meet stricter data protection regulations, which are especially important for healthcare. More convenient: Biometrics or push notifications can be faster than recalling complex passwords.

Our case study on M+T Bank’s upgrade illustrates how attackers who obtain information from data breaches or social media can easily compromise legacy authentication methods, such as KBA questions.

Stronger access restrictions with authentication

Secure authentication solutions go beyond a single password. Often, they incorporate MFA, behavioral analytics, or biometric checks (like voice analysis). When a healthcare professional logs into an EHR or receives a patient call:

Multiple verification factors: In addition to a password, a code might be sent to the user’s mobile device, or a voice analysis may run in real time.

Contextual data: The system can analyze user login patterns, device type, or geolocation to spot red flags.

Automated risk scoring: The system provides a risk score based on voice, device, or behavior analysis, which contact center agents can use to decide whether to grant or deny access.

By layering security measures, healthcare professionals can reduce the risk of unauthorized access. This can also streamline workflows, as staff no longer need to remember multiple complex passwords or endure endless verification questions.

Benefits of secure authentication for healthcare professionals

Better protection of patient privacy

Secure authentication’s primary function is to prevent unauthorized individuals from accessing sensitive data. In healthcare, this means shielding patient histories, medication records, billing details, and more. If a fraudster attempts to access or manipulate these records, robust authentication flags suspicious activity before damage occurs.

Cybercriminals often pretend to be patients or insurance reps. With more vigorous authentication techniques, such impostors lose their edge, reducing potential data leaks and HIPAA violations. Learn more about this with our guide: How to use AI to combat healthcare fraud.

Compliance

Laws governing data protection in healthcare are strict. U.S. healthcare providers generally must follow HIPAA (Health Insurance Portability and Accountability Act) standards for safeguarding the privacy of personal health information, while international organizations may face additional regulations such as GDPR (General Data Protection Regulation)—noncompliance can result in hefty fines, legal disputes, and public backlash.

Secure authentication can help with compliance by:

Enforcing rigorous checks for anyone accessing the EHR.

Generating logs that show a clear audit trail of who accessed what and when.

Offering documented processes that may meet or exceed regulatory standards for data security.

Furthermore, advanced security measures often align with recognized industry best practices, helping reassure stakeholders that an organization is taking steps to protect sensitive data.

Enhanced trust between patients and providers

Stronger authentication systems encourage openness, as patients are more inclined to share health details if they trust that only authorized staff can view them.

From a provider’s perspective, agents save time by validating callers quickly, without resorting to manual Q&A. This more seamless flow strengthens the relationship between patients and healthcare professionals. In turn, it can boost patient satisfaction and adherence to treatment plans, since they experience fewer hurdles during each interaction.

Best practices for implementing secure authentication

Transitioning from a password-centric approach to more secure, advanced methods requires planning. Here are some recommended steps:

Conduct a security audit

Assess existing workflows, identify risk points (like contact center scripts or old login portals), and determine how fraudsters might exploit them.

Set clear guidelines

If you switch to a new solution, ensure staff understand which factors are mandatory for authentication (e.g., a token, voice analysis) and which factors are optional.

Phased rollout

Consider introducing MFA or biometric checks in stages. This approach gives employees time to adjust without overwhelming day-to-day operations.

Train employees thoroughly

Staff must recognize the value of enhanced security and learn how to handle exceptions or escalations. Provide real-world examples showing how attacks succeed when staff ignore security protocols.

Review and update regularly

Fraud tactics evolve. Continuous reviews and software updates help keep your authentication measures resilient.

Additional resources:

For more guidance specific to healthcare, check out resources on how to combat healthcare identity theft and suggestions for improving healthcare fraud protection with AI. Both discuss the importance of regularly updating security strategies to counter new threats.

Enhance your authentication process with Pindrop^® Solutions

At Pindrop, we have a clear perspective on the benefits of secure authentication in healthcare. Advanced authentication solutions can deliver both stronger security and smoother user experiences for healthcare professionals.

By integrating seamlessly into contact center workflows, Pindrop^® Solutions take minimal effort for end users while drastically reducing the likelihood of unauthorized access.

Healthcare providers who continue to use older security measures risk data breaches, reputational damage, and penalties for noncompliance. Embracing advanced authentication now can help build trust among patients and better protect the valuable data that powers modern healthcare.

Discover more on how Pindrop can modernize the patient experience without compromising security.

Written in collaboration with Kawsar Kamal, Senior Solutions Architect and Amit Gupta, Specialist Solutions Architect for Amazon Connect at Amazon Web Services.

Amazon Web Services (AWS) recently announced that Amazon Connect Voice ID will reach its end of support in May 2026. In light of this development, AWS has identified Pindrop as a preferred solution provider for customers transitioning from Voice ID. In response, Pindrop introduced two new Amazon Connect features and obtained an additional certification to facilitate a seamless migration for customers of Amazon Connect Voice ID.

Seamless integration with Pindrop^® Passport

Pindrop^® Passport offers a comprehensive alternative to Amazon Voice ID, delivering advanced, passive, multifactor authentication and fraud detection. This approach combines voice, device, network, and behavioral signals to verify users and detect risks, minimizing customer effort while enhancing security. Unlike legacy single-factor systems, which are increasingly vulnerable to synthetic voice and deepfake attacks, Pindrop’s in-house signal development provides broader and more sophisticated protection.

To support organizations transitioning from outdated voice biometric systems, Pindrop^® Passport’s new features for Amazon Connect are designed to be intuitive, fast to deploy, and easy to scale.

Pindrop^® Agent Screen Pop for real-time authentication

The Pindrop Agent Screen Pop is a newly released embeddable user interface widget within the Amazon Connect Agent Workspace. It displays real-time authentication and risk assessment data, derived from Pindrop solution’s multifactor analysis of inbound calls. The interface provides clear, color-coded guidance to agents, enabling rapid service to verified callers while identifying potential fraud with minimal disruption to the customer experience.

^{Pindrop+Amazon Connect customers can provide customized instructions for their agents based on the policies they associate with Pindrop risk scores. This allows the agent to service genuine inbound callers and stop fraud quickly.}

Organizations can customize the agent guidance based on their risk policies, streamlining operations and elevating service quality and security. Previously, these user interface tools required internal development. Now, they’re available out of the box, significantly reducing implementation time.

Leveraging existing voice profiles with “Bring Your Own Voice”

One of the major concerns during transitions from legacy systems is the loss of previously enrolled voice profiles. With Pindrop^® Passport’s “Bring Your Own Voice” feature, organizations can retain and reuse their existing Amazon Voice ID voiceprints. Leveraging existing Amazon Voice ID voiceprints for initial enrollment data enables organizations to benefit from enhanced authentication and fraud detection capabilities from day one, without requiring customers to repeat enrollment.

Amazon Connect Service Ready Accreditation

Pindrop has earned the Amazon Connect Service Ready designation, demonstrating that its core solutions—Passport, Protect, VeriCall, and Pulse—meet AWS’s stringent architectural standards. These solutions operate entirely in the cloud and are proven in production environments. This accreditation underscores Pindrop technology’s ability to deliver secure, scalable, and innovative authentication and fraud detection capabilities for Amazon Connect contact centers.

With the new Agent Screen Pop for real-time authentication and support for existing voice enrollment data, Pindrop is helping Amazon Connect users adopt stronger, more innovative authentication tools quickly and easily. These enhancements, combined with real-time risk analysis and deepfake detection, reinforce security while ensuring a seamless customer experience.

To learn more, contact Pindrop at: [email protected]

“We deal with dozens of vendors, and Pindrop is quite literally one of the greatest vendors that I’ve ever worked with.” – Interactive Voice Response (IVR) Product Owner

Projecting ~$1.7M return on investment in the first year*

This utilities organization is a Fortune 100 company with +$30B in revenue, and one of America’s leading energy holding companies that strives to keep customer reliability and value at the forefront as it builds a ‘smarter energy future’. This goal places customer service and experience at the forefront, which is why they sought a solution that could help authenticate genuine customers and filter out those that could be fraud.

About the energy company

The company ranked among the top 100 largest companies in the United States in 2024, is a Fortune 100 company with +$30B in revenue, and one of America’s leading energy holding companies. The organization services +8M electric and nearly 2M gas utility customers across the U.S., collectively owning ~+54K megawatts of energy capacity.

*from 11/2024-3/2025

Challenges

For a utility company, addressing fraud was somewhat rare, especially in comparison to an industry like banking that proactively seeks ways to reduce fraud. The team was addressing ~ 20 fraud issues per day, or ~1800 per month. Fraud issues that customers called in with were largely regarding direct scams; rarely did the team hear about cases where a fraudster called directly to access a customer’s account. While the team was aware of robotic voices attempting to access their IVR, it was difficult to decipher if this behavior was fraudulent or not. Fraud mitigation was a huge goal for the organization. Before Pindrop, this energy company did not have profile-building methods or matching mechanisms in place for customer authentication. Due to the lack of clarity on when or if fraud was happening, they suspected that their organization could be relatively ‘open’ to fraudulent behavior.

“Fraud mitigation was a huge goal for our organization, something that we viewed as an opportunity to further safeguard our customers.”

– IVR Product Owner

An additional complexity to their business is the +800K customers who do not have Social Security numbers. This posed a challenge for the organization, as they did not have a good way to service this set of customers directly in the IVR.

Before Pindrop, these customers could do only two things in the IVR:

Make payments

Report outages

These actions covered only a small percentage of the self-service options available to customers in their IVR.

Because of these challenges, the customer wanted a solution to help modernize their call center security framework, alert them to potential fraud, and help reduce the authentication burden on their genuine customers.

Goals

This customer had three initial goals in mind as they sought a new solution:

Identify and authenticate more customers in the IVR

Enable specialists to clearly understand the risk of a caller as they come through to agents, and reduce average handle time

Maintain an ‘industry-leading’ IVR that deploys the most cutting-edge tools to improve customer satisfaction

With these goals in mind, the customer wanted to proactively detect and mitigate fraud instead of reacting to events on a passive, case-by-case basis. Additionally, the IVR Product Owner and his team wanted a more comprehensive view of risk across the call center.

Before Pindrop^® Solutions

The organization was using an out-of-the-box solution for authentication that they wanted to build from to improve their customer experience, lower average handle time (AHT), and streamline the efficiency and efficacy of their authentication process overall. Before Pindrop, the customer had a 73% authentication rate in the IVR. Through the years, they were able to get this number up to approximately 76%. Their existing point solution provided some insight as to force transfers out of the IVR, payment success, and account balance success, but these details were limited. Additionally, the customer was tracking AHT on a daily basis, which was an important metric for them. For example, every minute an agent spent on the phone equated to +$1 in cost to the organization. Net Promoter Score (NPS), which measures customer loyalty by looking at their likelihood of recommending a given business, was another metric that they tracked—a lot of their growth solutions are focused on improving this metric.

“On average, 225K callers per month were asked for their Social Security number using the legacy authentication process. That was cut by 55% month-over-month after Pindrop.”

– IVR Product Owner

Before Pindrop, the customer relied on the agent getting at least 3 Knowledge-Based Authentication questions (KBA) questions from a customer. Every customer who came through the IVR to an agent had to be fully authenticated. There was no situation where a customer was partially authenticated to help the agent work faster. Therefore, authentication was happening twice, which was both a time-consuming and expensive process.

Pindrop internal data corroborates that KBA, a form of verification that uses personal information from public records, is not effective as a security mechanism. After analyzing 2.5K fraud calls across 10 financial institutions, Pindrop data conveys that fraudsters successfully pass KBA 53% of the time on average, with success rates ranging from 9% to as high as 90%.

The existing solution and authentication methods were lacking an efficient process for catching fraud or authenticating genuine customers quickly, and by extension, improving customer experience.

Why Pindrop

Timing

The Pindrop^® Passport solution came together at the right time with the organization’s natural language IVR. The natural language IVR was the perfect blend of voice authentication and other features that Passport provides, which made it a perfect fit. Pindrop^® Passport provides a multifactor authentication solution that helps contact centers quickly authenticate callers within their IVR system and at the agent, allowing agents to focus on serving genuine customers.

What did Pindrop^® Solutions deliver?

“The increase in authentication that Pindrop^® Passport provided blew us away. Pindrop^® Passport was able to add 2-4% points in different areas of the IVR that completely surprised us.” — IVR Product Owner

The customer went into the project with relatively low expectations; they were proud of their existing IVR and the capabilities it enabled, as well as the flexibility their team had to implement enhancements whenever necessary. Which is why when the Pindrop solution added 2-4% points of increased authentication with Passport, it blew them away.

In Q1 of 2025, the customer had leveraged the Passport solution to enroll 1.4M unique customers, authenticate 7.9M calls, and identify nearly 100K (88,000) calls as ‘high risk’. Additionally, the number of callers already enrolled who received a ‘green’ profile match was 3.1M, meaning those callers could bypass legacy authentication methods.

Assuming a conservative 1% lift in profile match in the IVR, this equates to approximately $9 in savings per contained call. Additionally, the AHT savings in profile match is estimated to be $728K, for a total cost savings of $1.7M in approximately six months. This is a substantial return on investment that directly impacts the customer’s bottom line while simultaneously improving their key metrics of customer satisfaction.

The team also leveraged Passport to allow for additional self-service options for callers who do not have an SSN or TIN, and then passed that information to an agent for faster handle time. This points to an increase in IVR containment of about 1.4%, or $702K in containment savings in just one quarter.

Additionally, the company’s NPS increased +1.7pts from Sept 2024 to Oct 2024, and another +3.4 points from Oct 2024 to Nov 2024. The organization’s Nov 2024 score of +65.2 was the highest it had been since July 2023.

Business impact of Pindrop^® Solutions

Enhanced customer experience

Once the Pindrop technology was implemented, the organization immediately saw their authentication rates shoot up by several percentages. For example, before Pindrop^® Passport, they had ~225K callers per month who were asked for their Social Security number. The Passport solution cut this by 55%, meaning 100K customers every month were now in a better position to leverage the IVR self-service capabilities, which made for shorter wait times, fewer callers for agents to service directly, and better customer satisfaction for customers who could now service themselves.

Operational efficiency

This transformation created a cascade of positive impacts:

Shorter wait times for customers

Reduced call volume for agents to handle directly

Higher customer satisfaction scores for customers who can service themselves

Projected Year 1 ROI of approximately $1.7M (on an annualized basis)

Expanded service capabilities

Most significantly, the customer leveraged Pindrop® Passport to enable additional self-service options for the more than 800,000 customers who do not have SSNs or TINs. This previously underserved segment went from having access to only two basic functions (making payments and reporting outages) to enjoying the organization’s full range of self-service capabilities, while maintaining high security standards.

Measurable results

The evidence points toward a first-quarter increase in IVR containment of about 1.4%, translating to a significant reduction in AHT. With every minute on the phone costing the customer more than $1, this efficiency gain delivers substantial ongoing savings while simultaneously improving their core metrics of customer satisfaction and NPS.

Security coupled with customer satisfaction

By implementing Passport, Pindrop’s multifactor authentication solution, this energy customer exceeded their aspirations for building an industry-leading IVR that both protects their customers and delivers an exceptional experience, proving that security and customer satisfaction can effectively go hand-in-hand.

*On an annualized basis

Download the report below

2023 Voice Intelligence + Security Report

Pindrop Labs analyzed call and fraud data from 2018 to 2022 and found a 40% increase in fraud call rates in contact centers, which is expected to continue in 2023. See how fraudsters are using advanced techniques to target contact centers and consumers, and discover why businesses need to re-imagine their fraud detection and authentication strategies by moving to the cloud and automating manual processes.

Click here to download the report to learn more.

Healthcare contact centers often serve as the frontline for patient support, scheduling appointments, confirming insurance details, and addressing a wide range of inquiries. These contact centers are vital for patient care; but the discussion of sensitive data makes them a target for fraudsters.

When fraudsters pose as patients, insurance companies, or government agencies, they can manipulate agents into revealing personal information, transferring funds, or altering patient records.

This can impact an organization’s bottom line, leading to potential fraud and legal liabilities. If personal or financial records are compromised, it can also erode the confidence of patients and healthcare centers.

Combined with emerging methods like deepfake attacks, detailed in deepfake attacks: what you need to know, the stakes for healthcare contact centers have never been higher.

This article examines four key strategies for verifying caller identities, better protecting patient information, and enhancing overall trust to counter these threats.

How healthcare scam calls work

Fraudsters often start by gathering details about a target organization or patient list. They might acquire stolen data, such as policy numbers or Medicare IDs, from data breaches, then call the contact center claiming to be a patient or an insurance representative.

By posing as the owner, the fraudster may try to persuade agents to divulge more information or change account details. Alternatively, they could impersonate an official from a government agency, leveraging caller ID spoofing to make the phone number appear legitimate.

Fraudsters can also use texts or automated robocalls to direct unsuspecting agents to dial a suspicious number or visit a website, effectively bypassing standard authentication protocols.

Modern fraudsters even apply deepfake technology to replicate voices and request urgent changes. These sophisticated calls require more advanced defenses than basic scripts or cursory identity checks.

1. Verify caller identity

Properly verifying caller identity is your first line of defense in combating healthcare scam calls. This extends beyond simply requesting a phone number or personal information.

Contact center agents should rely on multilayered verification methods, such as voice analysis or device intelligence, to confirm a caller is who they claim to be. Traditional knowledge-based questions and one-time passwords can be easily bypassed if criminals have already stolen the necessary data.

As our guide on how AI can improve healthcare fraud protection explains, advanced authentication solutions help detect anomalies in real time. This is especially relevant for highly regulated contexts involving health insurance or Medicare details.

Quick tip: Implement a step-by-step process for verifying identity. For instance, voice analysis can be used, and then a second factor may be required, such as a confirmation code sent to an authorized device.

If any discrepancy arises, agents should follow escalation protocols. Read about how you can better protect patient privacy with voice biometric authentication.

2. Recognize red flags

Well-trained agents can identify phone scams early by recognizing behaviors that deviate from standard caller patterns. Even so, training alone doesn’t always suffice. Fraudsters are no longer confined to traditional methods; they utilize advanced tools to appear more convincing.

Voice phishing, for example, is one of the most prevalent scam call tactics. Fraudsters use a deceptive technique to trick individuals into providing personal and sensitive information over the phone. They pressure for immediate action, claiming the matter is time-sensitive, such as a crucial insurance lapse or a “government agency” needing an immediate callback.

Today, it’s more essential than ever for agents to recognize red flags. Below are some common signs to watch for:

The caller claims an urgent deadline or severe consequences (e.g., insurance lapses) but refuses standard verification steps.

The caller asks for information that isn’t typically required, such as full Social Security numbers or complete credit card details.

The caller’s phone number or caller ID appears legitimate but doesn’t match official records, or the caller seems overly defensive when asked basic identity questions.

The caller’s voice sounds oddly inconsistent—either robotic in tone, unusually distorted, or noticeably different from verified previous calls.

Voice phishing and other deceptive tactics are rapidly increasing, driven by the widespread availability of AI deepfake technologies that enable fraudsters to expand their operations. Discover how voice security can combat deepfake AI.

3. Guard personal information

Frontline contact center agents and managers must understand the value of the data they handle. Any slip can provide fraudsters with an opening.

Limit data exposure: Ensure staff access or disclose only the minimal patient data necessary for each call. Store sensitive information—like Social Security numbers—in secure, access-controlled systems.

Establish clear protocols: Staff should never share or request unnecessary details, such as entire credit card numbers or fully spelled-out account information, unless absolutely needed to complete a transaction.

Training and policies: Outline how and when staff can disclose certain pieces of data. For a deeper dive, check out best practices in our healthcare identity theft resource.

4. Report and block scam calls

If your contact center repeatedly encounters suspicious calls, sharing that information with internal teams and relevant authorities can help you prevent future attacks. The faster you can detect and block scam attempts, the better you can protect your organization’s data and reputation.

Network-level blocking: Work with your telecom provider to blocklist known malicious phone numbers or IP addresses.

Escalation procedure: Clearly outline whom staff should notify—IT security, compliance officers, or supervisors—if a call raises suspicions.

Collaborative intelligence: Leverage industry forums that track healthcare scams. Sharing data can improve detection across the broader healthcare ecosystem.

Proactive reporting doesn’t just safeguard your operation—it also assists other organizations by identifying shared threat patterns, including phony claims and caller IDs. Now you may be wondering, how can you catch and stop these calls in the first place?

Use Pindrop® Protect for comprehensive call risk assessment

While network blocking and escalation policies are crucial, voice analysis and AI-powered tools can offer immediate insights into suspicious callers. Pindrop® Protect provides a single, real-time risk score for each call, available from the Interactive Voice Response (IVR) stage through to the agent interaction.

By combining multiple data signals—device characteristics, behavior, and potential anomalies—Pindrop® Protect helps your organization:

Identify fraud more quickly: Near real-time alerts on high-risk calls allow staff to intervene or terminate the interaction before data is compromised.

Reduce reliance on manual checks: An automated, data-driven risk score can help you avoid asking lengthy security questions that inconvenience genuine callers.

Build trust: Show stakeholders that you’re proactively managing fraud risks with an advanced approach.

Learn more about Pindrop® Protect and how it can help safeguard your contact center from old vulnerabilities and new threats, such as deepfake voices. Plus, learn how you can modernize the patient experience without compromising security.

As AI technologies advance, so do the tactics of cybercriminals. Deepfake fraud, once a novelty, has become a significant threat to businesses worldwide. Leveraging sophisticated AI, attackers can now create highly convincing synthetic voices, enabling them to bypass traditional security measures and exploit vulnerabilities across multiple communication channels.

Deepfake fraud is scaling infographic Download

The Escalating Threat Landscape

Recent analyses reveal a concerning surge in deepfake-related incidents:

6.8x increase in deepfake call fraud year-over-year in 2024.

+475% rise in deepfake voice attacks within the insurance sector.

+149% growth in similar attacks targeting banking institutions

These statistics highlight the rapid evolution and adoption of deepfake technologies by malicious actors.

Contact Centers: The New Frontline

Contact centers, handling vast volumes of customer interactions, have become prime targets:

Businesses faced an average of $343,000 in deepfake fraud exposure per contact center in 2024.

40% of cybercriminals are now utilizing platforms like Microsoft Teams and Zoom as part of multi-channel attack strategies.

The convergence of voice and video channels presents new challenges in verifying the authenticity of interactions

Rethinking Security Measures

Traditional authentication methods, such as knowledge-based questions and one-time passwords, are increasingly inadequate. Attackers equipped with AI tools can easily mimic voices and manipulate conversations in real-time, rendering these measures obsolete.

To combat this evolving threat, organizations must adopt advanced security solutions that can:

Detect synthetic voices and anomalies in speech patterns.

Analyze behavioral cues and contextual inconsistencies.

Implement multi-factor authentication mechanisms that are resilient against AI-driven attacks.

Proactive Steps Forward

Understanding the depth and breadth of the deepfake threat is the first step toward mitigation. Equip your organization with the knowledge and tools necessary to stay ahead:

Download the infographic

Download the Deepfake Threat Playbook guide

Get in touch with an expert to learn about the future of voice security

By staying informed and adopting cutting-edge security measures, your organization can effectively counteract the growing threat of deepfake fraud.

Deepfake voice scams powered by agentic AI is no longer a theoretical threat—it’s happening right now, including in contact centers across the UK. As synthetic voices and video become indistinguishable from real interactions, fraudsters are scaling attacks in customer service environments—and eroding the human trust at the root of these interactions.

From impersonating executives to cloning the voices of loved ones, agentic AI allows fraud to happen faster, smarter, and with devastating financial consequences. And increasingly, UK businesses are finding themselves on the front lines.

A closer look at the AI-driven fraud spike

To understand just how fast deepfake fraud is scaling, view the full infographic below:

Deepfake fraud is scaling Download

Back-to-back AI voice attacks expose UK security gaps

Agentic AI is being weaponized to defraud UK businesses through call-based deception, highlighting just how fast traditional security is being outpaced.

£27M AI contact center scam targets crypto investors

Operating out of Georgia, fraudsters used agentic AI to generate deepfake voices and videos, contacting thousands, including over 600 people in the UK. They posed as celebrity investors and financial advisors via outbound contact center operations. Victims were guided to fake platforms like AdmiralsFX and scammed out of more than £27 million.

1 in 4 UK residents targeted by deepfake scam calls

TechRadar cited a 2024 survey which shows that 26% of UK residents had received calls with deepfake voices. cited a 2024 survey which shows that 26% of UK residents had received calls with deepfake voices. Of those, 40% were successfully scammed, often through impersonations of financial institutions, HMRC agents, or family members. These calls are increasingly generated and delivered at scale—hallmarks of fraud-as-a-service operations.

How Pindrop^® Solutions detect deepfake audio

Traditional fraud defenses weren’t built for synthetic voice threats. That’s why businesses across finance, insurance, and telecom rely on Pindrop^® technology to help detect, mitigate, and stop deepfake-enabled attacks in real time.

Catch voice impersonation scams

Reveal fraudsters using AI-cloned voices to pose as executives, customers, or partners.

Safeguard high-value transactions

Verify identity in sensitive calls—without adding friction for legitimate customers.

Stop real-time social engineering

Detect deepfake audio before employees are manipulated into transferring money or credentials.

Preserve customer trust

Stop customers from falling victim to synthetic voice scams that erode confidence and loyalty.

Stay ahead of evolving AI fraud

Identify new attack patterns and fortify resilience as agentic AI tools evolve.

With deepfakes scaling fast, companies trust Pindrop^® technology to help them protect revenue, reputation, and every voice interaction.

Get the guide: The Deepfake Threat Playbook

Want to better understand how agentic AI is shaping the future of fraud?

Download the Deepfake Threat Playbook to explore how synthetic identities are being weaponized and what your business can do to stay ahead.

While Artificial Intelligence (AI) can be at the root of some misleading content, that same AI technology can also help researchers and tech companies catch and neutralize misleading or harmful content. Understanding this interplay between “good AI” and “bad AI” is critical in preserving truth and trust in digital interactions.

This article explores AI’s evolving role in detecting misinformation, focusing on how emerging tools, best practices, and ethical considerations shape a more trustworthy digital future.

AI technologies for detecting misinformation

As AI-generated content becomes more prevalent, so do methods for identifying manipulated text, audio, and video. Various tools combine natural language processing, image recognition, and acoustic analysis to flag potential disinformation in real time. Below are a few potential techniques:

Pattern recognition: Large-scale analytics that detect unusual linguistic or visual traits, like repeated text blocks from known AI tools.

Acoustic or visual watermarking: Embedding hidden signals in legitimate content to confirm authenticity. This is especially important in voice-based media, where an algorithm can parse subtle anomalies.

Contextual analysis: Systems cross-referencing suspicious content against reputable sources or official statements. If significant discrepancies appear, the content is flagged as potentially fake.

Advanced solutions sometimes rely on deep learning—a subset of AI that trains on large datasets to differentiate real from manipulated material. For example, solutions like Pindrop^Ⓡ voice analysis can detect anomalies in speech patterns that might indicate a voice has been artificially generated.

AI-powered fact-checking systems

AI-driven fact-checking takes many forms, but the goal remains consistent: to assess the accuracy of statements in an automated way. News organizations, academics, and social media platforms are collaborating to refine these capabilities.

Automated fact-checking tools

Traditional fact-checkers rely heavily on manual research. Automated systems expand this process by using large language models to parse claims, compare them with established databases, and search for contradictory evidence. They can quickly scan thousands of news articles, official documents, and verified sources in a fraction of the time it would take a human.

However, no system is foolproof. Automated tools often struggle with nuanced language or cultural references, leading to false positives or missed misinformation.

Real-time verification of news articles

Increasingly, AI systems are deployed to provide real-time alerts on suspicious stories. If an article’s content deviates from known facts, fact-checking APIs can flag the text for human review. This real-time aspect is crucial because false information can “go viral” swiftly, shaping public opinion before corrections appear.

These solutions heavily rely on robust partnerships. Tech companies might supply the AI infrastructure, while media organizations and researchers provide validated data sets for cross-referencing.

When integrated into editorial workflows, these AI-powered systems can reduce the time between the release of fake news and its debunking.

Content authenticity and provenance

One of the most direct ways to combat misinformation is by verifying the source and lineage of any piece of media or text. This involves tracking content from its creation (or initial publishing) to its final distribution channels. AI can assist in multiple ways:

Metadata analysis: Embedding metadata in legitimate images, videos, or articles to confirm authenticity.

Blockchains and distributed ledgers: Some proponents suggest blockchain-based solutions that log each step of content creation and editing, making tampering easier to detect.

Reverse image search: This technique, often boosted by AI, helps confirm whether a photo purporting to show a recent event is from years ago or a different location.

AI in content moderation

As we all know, social media platforms host billions of daily posts, making manual moderation “unrealistic” or challenging for these big corporations. AI tools can fill this gap, scanning text, images, and videos for misinformation, hate speech, or incitements to violence. For instance, a platform might automatically remove suspicious links or flag posts replicating known disinformation patterns.

However, AI moderation is not without controversy. Some critics argue that algorithms can inadvertently censor legitimate speech or fail to recognize nuanced contexts. Others say that improvements in AI-generated deception outpace the platform’s detection algorithms.

While AI content moderation can be a powerful filter, a purely automated approach often risks overreaching or underreaching. Human expertise remains essential for edge cases that defy an algorithm’s binary logic.

For more context on how AI and deepfake technology complicate content moderation, consider our article on how voice security can combat deepfake AI and how real-time voice analysis is evolving to meet these challenges.

Ethical considerations and challenges

The use of AI to address misinformation inevitably raises ethical questions. Some revolve around free expression: how do we balance legitimate content with the imperative to remove harmful, potentially AI-generated false information? Others center on privacy: content scanning requires some level of data collection. Key issues include:

Algorithmic bias: AI detection tools trained on specific languages or cultural norms might struggle to interpret content from diverse backgrounds.

Transparency: Some entities may struggle with how to disclose precisely how their detection algorithms work, citing intellectual property or fear of enabling adversaries to circumvent the system.

Potential overreach: Automated takedowns of borderline content can silence valid discussions or hamper investigative journalism referencing controversial material.

Limitations of AI with misinformation

Even the most advanced deep-learning models can be fooled by sophisticated “adversarial examples.” Attackers might deliberately distort images or craft text that circumvents known detection patterns. Some limitations include:

Contextual understanding: AI might miss sarcasm or cultural references.

Speed vs. accuracy: Quickly scanning billions of posts can lead to many false positives or neglected genuine threats.

Evolving threats: The creativity of disinformation actors often outpaces the static training data an algorithm relies on.

For instance, a deepfake might incorporate realistic voice elements, as seen in a deepfake of Elon Musk, which exposed the dangers of AI-generated fraud. Over time, AI must continually retrain on new forms of manipulation to remain effective.

Future directions

Despite these challenges, AI is poised to advance in ways that might tip the scales against disinformation. Two promising frontiers include more robust detection algorithms and deeper collaboration between AI systems and human analysts.

Tools that combine audio, visual, and textual cues can also better detect cross-media hoaxes, such as a manipulated video with an AI-generated voice track. For example, consider the multi-layer approach described in testing voice biometric authentication systems against AI deepfakes, where voice analysis integrates with advanced algorithms to highlight suspicious changes in speech patterns.

Hybrid models, where advanced AI flags potential hoaxes for skilled human evaluators, show promise. Professionals can interpret nuances, weigh context, and confirm whether flagged content is truly misleading.

If carefully orchestrated, this partnership can drastically shorten the time it takes to identify and debunk fake news or AI-driven deception. Collaborative systems, like those used in contact center security, which combine humans and machines in risk analysis, illustrate how joint AI-human workflows can outperform either method alone.

Effectively combat misinformation with Pindrop® Technology

As the arms race between legitimate and nefarious uses of AI intensifies, organizations across the media, political, and corporate sectors need advanced tools to verify the authenticity of audio, video, and text. That’s where Pindrop^Ⓡ Pulse can make a difference.

Detect AI deepfakes with unmatched precision.

Pindrop^ⓇPulse enables you to verify questionable audio quickly. Users receive fast and detailed feedback on whether specific audio segments might be artificially generated by uploading files via a web application or API.

99% accuracy rate: Pindrop^Ⓡ Pulse sifts through vast amounts of speech data to spot synthetic elements with minimal false positives.

Powered by over 20 million statements: Having tested more than 370 TTS engines, Pindrop can catch an array of deepfake or voice-conversion attacks.

Near real-time analysis: The system examines calls or audio segments every four seconds, flagging suspected content swiftly so you can respond before false information circulates widely.

Combined with a broader strategy—like robust content moderation, multifactor fact-checking, and direct collaboration with human experts—Pindrop^Ⓡ Pulse can be a critical puzzle piece in curbing the spread of manipulated content.

Agentic AI + the New Era of Voice Fraud

Synthetic voice attacks are no longer a future threat—they’re already infiltrating contact centers across industries. In this first session of Pindrop’s four-part webinar series, we explore the growing capabilities of Agentic AI, which enables machines to sound convincingly human, act independently, and scale impersonation fraud like never before. Featuring exclusive insights from our 2025 Voice Intelligence + Security Report, this webinar reveals why deepfake fraud is projected to surge by 162% in 2025 and what that means for enterprises. Discover how real-time liveness detection can proactively safeguard your business and customers from synthetic deepfakes, and get a look at what’s ahead in voice security.

Agentic AI + the New Era of Voice Fraud:

Insights into the 2025 VISR Report

Synthetic voice attacks aren’t on the horizon anymore – they’re showing up in contact centers across every industry.

Agentic AI enables machines to sound human, act autonomously and scale impersonation fraud attacks like never before. In this first installment of Pindrop’s four-part webinar series focusing on the release of our 2025 Voice Intelligence & Security Report, we’ll dive into to the key insights from our new guide, What Agentic AI Means for the Future of Fraud.

In this webinar, our deepfake leaders will break down:

The state of AI fraud: +162% deepfake fraud increase expected in 2025

How to proactively detect synthetic deepfakes with real time liveness detection and protect your business and consumers

Exclusive sneak peak into Pindrop’s upcoming 2025 Voice Intelligence & Security Report

Your expert hosts

Amit Gupta

VP, Deepfake Detection, Pindrop®

Mo Merchant

Director, Research & Dev, Pindrop®

by Clarissa Cerda, Chief Legal Officer at Pindrop

The recent flood of over 10,000 public comments to the White House’s Request for Information on the AI Action Plan signals a historic turning point for artificial intelligence in America. At Pindrop, we were proud to contribute our perspective as a leader in deepfake detection, urging policymakers to address the very real and immediate risks posed by generative AI and synthetic media. Having served in the White House, shaped legal strategy at LifeLock (NYSE: LOCK), and spent the past eight years helping Pindrop advance the frontiers of AI security, I can say unequivocally: trust, transparency, and verification have never been more essential.

When I began my career in public service, trust was the bedrock of effective governance and innovation. At LifeLock, a proactive identity theft prevention company, I helped build legal and compliance frameworks that brought enterprise-grade identity protection to everyday consumers, recognizing early on that the digital threat landscape was evolving rapidly. That mission-to safeguard identity-has only grown more urgent as I have witnessed firsthand the acceleration of AI’s capabilities and the new vulnerabilities it creates.

Today at Pindrop, our focus is clear: defending the authenticity of human identity in a world where voices, images, and behaviors can be convincingly faked. The challenge now goes far beyond traditional data protection. It is about ensuring that what we see and hear online is real. Why? Because the cost of getting it wrong is measured in lost trust, compromised security, and societal harm.

Pindrop stands at the intersection of innovation and security, developing technologies that verify not just who is on the other end of a call or interaction, but whether that “who” is even human. In an era when AI can convincingly mimic anyone, verification is not a luxury, it is the foundation of trust in our digital society.

The White House’s AI Action Plan is more than a policy proposal; it is a call to action for industry, policymakers, and the public to build systems rooted in accountability, fairness, and resilience.

The overwhelming engagement with the RFI demonstrates that Americans care deeply about how AI will shape their lives and are demanding robust safeguards. Trust in AI must be earned through systems that are transparent, verifiable, and resilient against even the most sophisticated threats. At Pindrop, we have spent the last decade preparing for this moment, continually raising the bar for authentication and security.

Looking forward, I am confident that those who prioritize trust, verification, and responsibility will define not just the technical future of AI, but its societal impact as well. This is a future in which we all have a stake. At Pindrop, we are honored to help lead the way.

Agentic AI is no longer theoretical—it’s here and already on the phone with your business.

Agentic AI refers to artificial intelligence systems that can act independently. These systems can initiate actions like calling a contact center, responding in real time, and adapting their behavior based on your input. Unlike traditional bots that follow scripts, agentic AI operates with autonomy—making decisions on the fly, without human oversight.

What makes this especially concerning is the rise of AI impersonation. Voice bots powered by agentic AI can now mimic real people with stunning accuracy. These systems don’t just read text aloud. They can compute context, modulate tone, and handle complex tasks like account changes, wire transfers, and even one-time password (OTP) challenges.

We’ve entered an era where fraud is being carried out not just by individuals but by machines acting with near-human precision.

Fraud at machine speed: Why synthetic voice attacks are scaling faster than ever

In the past, deepfake fraud was rare, slow, and technically difficult. Today, it’s none of those things.

Deepfake call activity exploded by +1,337% in 2024, climbing from one per month to seven per day by the end of the year¹. Much of this growth is due to the adoption of agentic AI tools, which allow fraudsters to launch high-volume impersonation attacks with minimal effort.

It’s not just the volume that’s alarming; it’s the scale. By late 2024, 1 in every 106 calls to contact centers was synthetic, nearly 1% of all voice interactions. Synthetic voice fraud is now a mainstream threat.

The technology behind it is evolving fast. Tools can now recreate human emotion in real time, allowing AI voice models to sound angry, empathetic, or panicked—whatever the situation calls for. Combined with natural language models, these systems can carry out conversations that feel remarkably human.

3 deepfake detection tactics that work

While synthetic voices are getting better, they still leave behind subtle traces if you know where to look. Here are three proven ways to detect AI impersonation:

1. Audio inconsistencies

Synthetic voices often produce audio with unnatural pauses, robotic timing, or missing background noise. These flaws can be detected by advanced liveness detection systems, which analyze acoustic patterns for signs of manipulation.

2. Lack of contextual awareness

Agentic AI can handle scripted dialogue, but it struggles with unpredictable and off-script moments. Listen for vague responses, overly formal phrasing, or dialogue that seems “too perfect.” These are often signs you’re dealing with a machine, not a person.

3. Subtle delays in speech

Even the most advanced tools introduce millisecond-level delays when processing speech in real time. These micropauses may be hard to catch with the human ear but can be flagged by systems trained to identify them.

What enterprises can do right now

AI-driven voice fraud isn’t a future problem; it’s happening now. Luckily, there are concrete steps your organization can take today to detect and disrupt deepfake activity:

Train staff to identify red flags

Frontline contact center teams are your first line of defense. Educate them to spot key signals like robotic cadence, unnatural emotion, delayed responses, and suspicious metadata in caller profiles. Frequent training can help make this second nature.

Deploy real-time liveness detection like Pindrop^® Pulse

Pindrop^® Pulse analyzes over 500 text-to-speech (TTS) engines, tracing audio back to known AI models and identifying manipulation with a high degree of precision. It works in real time, allowing businesses to flag synthetic calls before damage is done.

This kind of technology doesn’t just detect fraud—it helps confirm what’s human, which is becoming just as important.

Why AI voice cloning detection is the new standard

AI will only continue to improve. Open-source models are giving fraudsters highly sophisticated tools to create more convincing deepfakes, faster and at scale.

The bigger challenge may not be spotting a fake, but proving what’s real. In an age where anything can be synthesized, verifying authenticity is critical. Traditional identity verification methods like caller ID or voice recognition are often no longer sufficient on their own.

That’s why AI-powered liveness detection is becoming a new baseline for fraud detection. It empowers organizations to verify that a caller is not only who they say they are, but that they’re also human in the first place.

Detect deepfakes before they infiltrate your business

For a more in-depth look at the latest research, detection strategies, and what’s to come in the year ahead, download The Deepfake Fraud Playbook: What Agentic AI Means for the Future of Fraud.

Today, we’re announcing the beta of our new deepfake detection product, Pindrop^® Pulse for meetings, available for Zoom, Microsoft Teams, and Webex, with support for additional platforms coming soon. Built on the Pindrop^® Pulse multifactor “real human” platform, our Pulse for meetings solution brings deepfake detection directly to your meetings by analyzing audio and video content in real time to detect AI-generated manipulation. With this beta launch, Pindrop extends our deepfake detection capabilities – already trusted by some of the largest enterprises to analyze 130M phone calls, into video meetings.

Virtual Meetings: A New, Unprotected Attack Surface

Virtual meetings have become a cornerstone of modern business, facilitating high-stakes interactions like financial transactions, hiring decisions, and executive discussions. With nearly 1B daily active users across Zoom, Microsoft Teams, and Webex, organizations now rely on these platforms more than ever. Yet, video conferencing remains an unprotected entry point for cyber threats. While CISOs have dedicated significant resources to secure email, data networks, and cloud infrastructure, most organizations lack safeguards against deepfake-driven deception in video meetings.

Cybercriminals are exploiting this gap, using AI-generated voices, avatars, and face swaps to manipulate live conversations, gain unauthorized access, and execute fraud. Attackers have successfully impersonated executives, deceived employees into transferring funds, and even infiltrated hiring processes with deepfake job candidates.

Real-world incidents highlight this growing threat. In January of 2024, a finance worker at Arup paid out $25M after being deceived by a deepfake CFO on a video call. Fraudsters have also used deepfake technology to impersonate job candidates, gaining access to sensitive enterprise environments under false identities. In July 2024, KnowBe4 reported a case where a North Korean created a deepfake to get through an interview process, landing a job, and attempting to execute unauthorized software within minutes of receiving a laptop. Additionally, in September of last year, a U.S. Senator was targeted by a deepfake impersonating a Ukrainian official attempting to exfiltrate information about military movement between Ukraine and Russia. These cases demonstrate the urgent need for advanced safeguards in meetings.

As these threats escalate, securing video meetings must become a core pillar of enterprise cybersecurity strategies.

Types of Deepfake Threats in Your Meetings

We’ve identified four primary attack vectors used to manipulate participants in video meetings:

Real-Time Voice Modulation

Alters voice characteristics, such as pitch or accent, to change your perceived voice (e.g., male to female, foreign speaker to native-English speaker).

Real-Time Voice Conversion

Uses generative AI to transform one person’s voice into another’s, enabling more realistic impersonation.

Real-Time Face Avatars

Employs AI-generated avatars with synthetic voices and visuals, often controlled by language models.

Real-Time Face Swaps

Replaces a person’s face with another in real-time.

These techniques enable a range of attacks targeting enterprises, both large and small:

Financial Fraud

Attackers impersonate executives or high net-worth clients to deceive individuals into authorizing large money transfers.

Recruiting Impersonation

Fraudsters use deepfakes to pose as job candidates, gaining access to enterprise environments.

Corporate Espionage

Deepfake impersonations used to infiltrate sensitive meetings and extract confidential information.

We’ve already had significant success in tackling audio-based deepfake threats with the Pindrop Pulse liveness detection technology. Now, to address the next wave of challenges, we’re introducing a powerful solution for detecting video deepfakes.

Pindrop^® Pulse “Real Human” Platform for Video Meetings

We’ve helped secure the world’s largest banks, insurers, and contact centers with tech backed by 12+ years of proprietary and industry data. Now, we’re extending that same expertise to video. Pindrop Pulse’s Real-Human Platform is built to answer a simple but increasingly urgent question: Is the person in this video meeting a real human or a machine?

Our platform uses real-time audio and video deepfake detection to evaluate a participant’s authenticity – whether they’re speaking or silent, on-camera or off. Organizations can choose which detection signals to enable based on their security and privacy needs. It’s the same multifactor approach that has helped enterprises secure millions of phone-based interactions, now applied to virtual meetings. With the addition of video analysis, Pindrop Pulse for meetings expands our deepfake detection portfolio and brings comprehensive protection to a space that has quickly become a target for AI-driven deception.

Our new video detection engine analyzes content in three key ways:

Still Image Analysis

By breaking video into individual frames, the solution detects artifacts such as unusual lighting, misplaced facial features, and distortions. This analysis also uncovers hidden patterns across the frequency spectrum, revealing signs of AI-generated content.

Frame-to-Frame Temporal Analysis

Our models examine video frames over time to identify inconsistencies like facial feature drift and unnatural lip movements, both of which signal potential manipulation.

Audio-Visual Cross-Checking

Deepfake videos often fail to perfectly sync audio and visuals. Our model detects mismatches between speech and lip movements, uncovering signs of synthetic generation.

With the launch of our video deepfake detection model, we’ve also taken the opportunity to strengthen our audio detection pipeline – because audio remains the most convincing of deepfake modalities. Our latest model is specifically trained on the signal post-processing used by major conferencing platforms, enabling more accurate detection in real-world meeting environments. Audio provides over 16,000 samples per second – far more than the 30 frames per second in video – making it rich with detail. That density helps our system surface subtle artifacts in sound that can reveal AI-generated manipulation.

By combining high-fidelity audio analysis with advanced video detection, Pindrop Pulse’s Real-Human Platform delivers a strong defense against AI-generated deception in virtual meetings.

Announcing a Beta of Pindrop^® Pulse for Meetings

We’re now combining both our audio and video deepfake detection models into a comprehensive solution for video conferencing. Pindrop^® Pulse for meetings offers real-time deepfake detection with an intuitive in-app experience, available for installation directly from the Zoom, Microsoft Teams, and Webex app stores. IT administrators can deploy the application to users in just minutes. Once installed, users can log in, invite a security assistant, and start receiving real-time alerts immediately. This powerful tool enhances security by:

Inviting a Security Assistant

A security assistant bot can be invited ad-hoc or through calendar invitation to actively analyze live meetings for deepfake voices, avatars, and face swaps.

Automatically Discovering and Analyzing All Participants

Once invited to a meeting, Pindrop Pulse for meetings automatically discovers all participants and analyzes every participant’s individual audio and video media streams. Participants are continuously analyzed as live human or synthetic based on 6 seconds of speech or live camera feed.

Providing Customizable Alerts

Organizations receive real-time alerts when a participant is flagged as AI-generated. Notifications can be tailored for in-meeting display or routed to security teams for out-of-band response.

Offering Post-Meeting Analysis

Security teams can review flagged moments in recorded meetings to further investigate potential deepfake activity.

The Next Generation of Security for Virtual Collaboration

As the line between real and synthetic continues to blur, protecting digital interactions is no longer optional – it’s essential. With Pindrop Pulse for meetings, organizations can confidently navigate the future of virtual communication, knowing they have the latest technology to combat deepfake manipulation in real time.

Want to see Pindrop^® Pulse for meetings in action? Contact us to learn more about integrating real-time deepfake detection into your virtual conferencing workflows.

Guide

What Agentic AI Means for the Future of Fraud: The Deepfake Threat Playbook

Synthetic voice fraud is no longer on the horizon—it’s already here. Learn how deepfakes are reshaping fraud tactics, where legacy defenses are falling short, and what you need to know to protect your business.

What’s in the guide

Discover how:

How fraudsters use synthetic voice to bypass both human and automated defenses
Why agentic AI is fueling fully autonomous attacks at scale
What red flags and technical anomalies effective detection strategies look for in real time

WAITLIST CONFIRMATION

You’re on the waitlist!

You’ll receive the 2025 Voice Intelligence and Security Report in your email when it’s published. Ready to explore data-driven insights on fraud and voice security now?

Download last year’s Voice Intelligence and Security Report.

Contact centers have become prime targets for fraud in our increasingly digital, AI-driven world. Attackers exploit weaknesses to bypass security and access customer accounts.

Legacy authentication methods like knowledge-based questions and one-time passwords (OTP) are no longer reliable defenses. Fraudsters can harvest personal data from breaches and are adept at answering security questions correctly.

Another game-changer is the rise of deepfake-enabled fraud. Advances in generative AI allow scammers to create impersonations that may look and sound nearly indistinguishable from the actual person.

All of this begs the question: How can agents trust who’s on the line if a customer’s voice can be faked? This evolving threat environment underscores why contact center security is more critical than ever.

The importance of contact center security + statistics

As we mentioned in the introduction, the fraud industry targeting contact centers is evolving rapidly, with a surge in fraudulent attacks in recent years, especially in the finance and retail sectors.

In a recent survey, 90% of financial service firms reported increased fraud attacks on their contact centers, with some seeing an 80%+ spike compared to the prior year (TransUnion Report).

Our Voice Intelligence and Security Report found that cybercriminals passed contact center KBAs 80% of the time, while genuine callers only passed 46%. OTPs are likewise vulnerable; fraudsters have tools to intercept or “phish” OTP codes. This weakness exposes contact centers to account takeover scams and social engineering.

Using AI, attackers can clone a person’s voice to impersonate customers or even executives over the phone. A UK energy firm learned this the hard way when a fraudster used an AI-generated voice of the CEO to trick an employee into transferring $243,000.

To counter modern fraud threats, contact centers should implement multiple layers of defense. Here are five effective methods to bolster your contact center security:

1. Use multifactor authentication (MFA)

The first step is to strengthen caller authentication by requiring multiple verification forms. Multifactor authentication (MFA) means a caller must prove their identity in two or more ways—for example, by providing something they know (e.g., a PIN or answer to a personal question), something they have (e.g., a code sent to their device), or something they are (e.g., their voice).

This layered approach vastly improves security because an imposter would still lack the others even if one factor is compromised. No single factor is infallible, so combining factors dramatically raises the bar for fraudsters.

Modern MFA solutions for contact centers often work behind the scenes to minimize customer friction. Instead of lengthy Q&A, the system might automatically check the caller’s voice against an enrolled voice profile and analyze device signals, only escalating to an agent if something doesn’t match. This not only improves security but also speeds up service for legitimate callers.

For example, Michigan State University Federal Credit Union recently implemented a passive voice MFA system and cut their caller authentication time by 50% – from 90 seconds to 45 seconds. Learn how they achieved this. In about 40% of calls where the caller’s voice profile fully matched, authentication dropped to just 12 seconds, an 86% reduction.

Upgrading legacy authentication to MFA is a crucial foundation for contact center security. Layering verification methods helps ensure you’re speaking to the right person and thus stops many fraud attempts at the front door.

For another real-world example, see how M&T Bank transitioned from legacy authentication to a cloud-based MFA solution to protect its callers.

2. Implement fraud detection software

Even with strong authentication, some fraudulent calls will slip through, so a real-time fraud detection system is essential. Fraud detection software for contact centers continuously monitors calls and caller behavior for any red flags or anomalies that could indicate a scam in progress.

These solutions use advanced analytics and AI to examine factors a human agent can’t easily track, such as vocal characteristics, calling patterns, device identifiers, and historical fraud data. Suspicious activity is automatically flagged so the call can be further verified or terminated before more damage occurs.

In one case study, a large e-commerce retailer deployed voice fraud detection and discovered that a single fraudster had made five separate calls from different phone numbers using various aliases to scam agents.

The software identified the perpetrator by matching the voice across those calls and produced a negative voice profile, allowing the retailer to shut down the fraudster’s attempts. Here are some key results and steps taken:

Over just a few weeks, this system uncovered 86 repeat fraudsters, placing over 8,900 fraudulent calls from over 6,000 device identifiers.

Armed with that insight, the company moved to close thousands of compromised accounts and prevent fraud loss, and is now on track to save nearly $10M in fraud losses.

The Pindrop® solution detected 22% more fraud than the closest comparable solution, reducing the false positive rate to less than 5%.

The result is stronger security with less disruption to genuine customers. See the detailed case study.

3. Deploy deepfake detection

Deepfake detection tools are designed to distinguish human voices from synthetic or recorded ones, adding a critical layer of defense on top of voice analysis. Traditional voice authentication might be fooled if a fraudster uses a high-quality recording or clone of the victim’s voice.

Deepfake detection algorithms address this by analyzing subtle characteristics of live human speech – for example, natural vocal tremors, breathing sounds, cadence, and microphone noise – to ensure the audio comes from real humans in real time. You can also proactively protect your business with deepfake audits.

Integrating deepfake detection into your contact center means that even if an imposter has passed initial authentication, the call will be scored for “liveness.” Deepfake detection acts as a safety net against such scenarios, verifying that the voice in question belongs to a live human, not AI.

Deploying this technology prepares your contact center for the rising wave of AI-driven fraud. It’s a forward-looking investment. To learn more, check out our insightful webinar, Voice Theft: How Audio Deepfakes Are Compromising Security

4. Employee training and awareness

While technology is vital, frontline employees remain one of the most important defenses in a contact center. Well-trained, vigilant agents can often sniff out fraud attempts that slip past automated checks, especially social engineering ploys.

Here are 5 best practices:

Employees should be trained to identify common tactics used by fraudsters, such as social engineering, and to stick to established verification scripts no matter how insistent or emotional a caller may be. This includes verifying caller identities strictly according to policy and never being coaxed into “making an exception.”

Regular coaching can include reviewing recent fraud incidents (within the company and in industry news) to illustrate how scams play out and how they could be prevented.

Role-playing exercises or simulated fraud calls can effectively give agents hands-on practice in spotting suspicious behavior. The goal is to build an instinct for skepticism.

For example, an agent might notice that a caller is oddly hesitant when asked for basic account info or pushes to change an address and send a large payment on the same call. Rather than accommodating such requests quickly (as scammers pressure them to do), a trained agent will know to slow down and follow security steps, even if the caller tries to create a sense of urgency.

Ensure employees understand the latest threats, such as deepfakes or IVR hacking. If agents know that callers could be impersonated via AI, they can be extra cautious when a voice sounds “off” or if a usually calm customer suddenly speaks inconsistently.

In summary, technology + human vigilance is the winning combination. Even the best security software works better when agents are knowledgeable and alert.

5. Encrypt and secure communications

Even if you authenticate callers and train staff, you must also ensure that the information exchanged is protected from eavesdropping or theft.

Start with strong encryption for data at rest and in transit. Data at rest (stored in databases, CRM systems, call recording archives, etc.) should be encrypted using robust algorithms. Even if an attacker gains access to the storage, the data is unreadable without the decryption keys.

This can involve disk-level encryption on servers and databases, and encrypting backups. Equally important is encrypting data in transit, meaning voice conversations and any data passing over your networks or the Internet.

Beyond encryption, securing communications involves locking down the contact center infrastructure. This includes using firewalls to shield your telephony systems, intrusion detection systems (IDS) to monitor for any unauthorized access attempts, and network segmentation to isolate your contact center network from other corporate systems.

By segmenting and controlling network access, an attacker can’t move laterally into other sensitive areas, even if one part is breached. Moreover, ensure that all software and systems are updated with security patches, as vulnerabilities in outdated software are common entry points for attackers.

Another best practice is to encrypt call recordings and purge sensitive recordings regularly if they’re no longer needed. Many contact centers handle credit card payments or personal data over the phone, which may bring additional regulatory requirements to protect that data. Encryption and secure protocols are fundamental to help prevent data breaches that could leak thousands of call records.

How to implement security measures effectively

Knowing what security measures to adopt is half the battle – the other half is implementing them thoughtfully to minimize operational impact. Here are some tips on rolling out these enhancements effectively in your contact center:

Phasing implementation

It’s often neither practical nor wise to revamp all your security processes overnight. A phased implementation approach allows you to improve security step by step without overwhelming your systems or staff.

Start by prioritizing the measures that address your most critical vulnerabilities or the most significant fraud pain points. Develop a roadmap that introduces new tools and policies over weeks or months. This way, you can pilot each change, gather feedback, and fine-tune before the next phase.

Begin with a small-scale pilot or a controlled rollout. You might first enable the new authentication workflow with a subset of agents or a specific call queue. Monitor the results:

Are calls being authenticated faster?

Is there any customer confusion or agent difficulties?

Use these insights to adjust your processes or provide additional training.

Once you are confident, expand the rollout to the entire contact center. Taking it in phases also helps with change management; agents and customers adapt gradually rather than facing a drastic change simultaneously.

Communicate clearly with your team about each upcoming change, its benefits, and any new steps they need to follow. For instance, deepfake detection can be introduced to agents in training sessions so they understand what a “liveness failed” alert means and how to handle such calls.

After one phase is live and stable, move to the following priority—enhancing encryption and network security, for example. This iterative approach ensures that security upgrades integrate smoothly into daily operations and that any kinks are worked out in one phase before the next begins.

Ultimately, a phased implementation strikes a balance: you steadily strengthen security without causing major service disruptions or overloading your IT team and agents with too much change at once.

Integrating seamlessly with existing systems

Adding new security solutions to your contact center must play nicely with your existing systems. With minimal friction, the best security tools can integrate into your current telephony, CRM, and agent desktop workflows. To achieve seamless integration, involve your technology team early to evaluate compatibility.

Look for solutions (like IVR fraud detection or voice analysis software) that offer out-of-the-box connectors or APIs for popular contact center platforms. Many leading security providers partner with the major contact center technology vendors. For example, PindropⓇ solutions can plug directly into platforms such as Google Cloud, Amazon Connect, Genesys, and Five9. Learn about our partnerships.

Seamless integration is also essential for agent adoption. If agents have to juggle another screen or manually copy data between systems, this can lead to errors and frustration. Instead, integrated solutions can display risk scores, caller authentication status, or alerts within the existing agent desktop.

Boost contact center security with Pindrop® solutions

Enhancing your contact center security may sound complex, but you don’t have to do it alone. Pindrop is a leader in contact center security, offering solutions that support comprehensive protection against fraud while enabling fast, frictionless customer service.

With years of audio analysis and phone channel fraud expertise, Pindrop solutions deliver a multilayered approach that addresses our customers’ challenges, from caller authentication to real-time fraud detection and deepfake defense.

Pindrop solutions are designed to seamlessly integrate into your contact center workflows, working with existing telephony systems and partner software to deploy quickly.

Our solutions meet the voice security needs of contact centers in various industries, taking a comprehensive approach to fraud detection, deepfake detection, and authentication. Get your demo.

THANKS FOR DOWNLOADING

Download the guide below

Discover how deepfakes are invading virtual meetings on platforms like Zoom and Microsoft Teams. Download the guide to learn how liveness detection leads the fight against these attacks.

Click here to download the guide.

Mayday! How Economic Downturns Create a Perfect Storm for Fraud

Economic instability brings more than just financial adversity – it opens the door for fraud to flourish. During the three month recession in 2020, the GDP fell 32.9%, leading to +8% growth in fraud in the U.S. Since 1988, every major recession has resulted in an increase in fraudulent attacks on both consumers and businesses. Historical data shows us a consistent pattern: as the economy declines, fraud rates rise. This is an ominous trend since global brokerages recently raised the odds of a recession taking place in 2025 to 60%.

Mayday! How Economic Downturns Create a Perfect Storm for Fraud

The history of fraud in times of recession/economic downturns

How financial institutions can evaluate infrastructure vulnerabilities

How to identify and mitigate areas of risk

How to protect the trusted relationship with your customers during times of economic uncertainty and market volatility

Your expert hosts

Lee E. Ohanian

Professor of Economics, UCLA & Sr. Fellow, Stanford University

Rahul Sood

Chief Product Officer & Fraud Expert, Pindrop®

THANKS FOR DOWNLOADING

Download the guide below

Discover how deepfakes are invading virtual meetings on platforms like Zoom and Microsoft Teams. Download the guide to learn how liveness detection leads the fight against these attacks.

Click here to download the guide.

Guide

Catch fraudsters in their newest playground: meetings

Discover how deepfakes are invading virtual meetings on platforms like Zoom and Microsoft Teams. Download the guide to learn how liveness detection leads the fight against these attacks.

What’s in the guide

Discover how:

Fraudsters are leveraging deepfake technology to infiltrate virtual meetings, and recent cases have cost millions in fraud losses
Deepfake detection works, specifically for audio, still images, and audio-visual scenarios
Pindrop® Pulse brings liveness detection to real-time virtual meetings and detects AI-generated participants

Fraud is becoming an urgent and complex challenge for UK Building Societies. In the first six months of 2024 alone, fraud losses in the UK exceeded £571 million—a 19% increase from the same period in 2023. As fraudsters grow more sophisticated, the stakes are especially high for UK Building Societies, which are member-owned and trusted to protect personal finances.

Unlike traditional banks, Building Societies prioritize community values, ethical banking, and personal service, but that commitment to trust and accessibility can also expose them to unique fraud risks.

Common types of fraud for Building Societies

Increased sophistication in audio deepfakes

The digital transformation of financial services has brought convenience, but has also created more opportunities for fraud. Here are the most common forms of fraud affecting UK Building Societies today:

Authorised push payment (APP) fraud

Criminals impersonate trusted figures like banks, police, or Building Society staff to manipulate members into transferring money to fraudulent accounts. Because these transactions are initiated by the customer, stopping them in real-time is challenging.

Account takeover and impersonation fraud

Fraudsters use phishing emails, leaked credentials, or malware to access a member’s account. Once inside, they may change security settings, reroute funds, or pose as the account holder. These attacks are hard to detect, especially if the fraudster mimics legitimate behavior.

First-party fraud

First-party fraud involves a customer intentionally misrepresenting their financial situation to secure loans, credit, or benefits—often with no intention of repayment. The accounts may appear entirely legitimate at first, making these cases difficult to catch early.

The growing threat of fraud for Banking Societies

Today’s fraudsters are using AI, spoofed caller IDs, deepfake voices, and social engineering to bypass traditional security measures. Building Societies are being targeted through both digital and voice channels, and smaller institutions often don’t have access to the advanced fraud detection tools used by larger banks.

The latest fraud tactics are designed to exploit human trust and system vulnerabilities so it is critical for Building Societies to rethink their approach to fraud detection. Delays in fraud detection can lead to financial losses, member dissatisfaction, and reputational damage.

5 effective fraud detection methods for Building Societies

Building Societies need proactive, tech-forward strategies to keep fraud at bay. Here are five methods worth prioritizing:

1. Implement advanced voice analysis

Unique characteristics in a person’s voice—such as pitch, tone, and speech patterns—can be analyzed to verify identity. Unlike passwords or PINs, voices are difficult to perfectly replicate. That’s why voice analysis can be a powerful tool for verifying identities, especially when used alongside other authentication factors.

With solutions like Pindrop® Passport, Building Societies can:

Authenticate members passively during phone calls
Reduce call times by eliminating manual verification
Flag anomalies when voice patterns don’t match the enrolled voice

Voice analysis not only improves security but also makes the process faster and more user-friendly for members.

2. Raise awareness of emerging threats

Education is a powerful defense against fraud. Building Society members and staff need regular updates about the latest scams, especially those using social engineering or deepfake audio.

Launch fraud awareness campaigns via email, website banners, and branch signage
Offer training sessions for employees
Use case studies and real-life scenarios to help members recognize red flags

Proactive education empowers members to pause and question suspicious requests to help detect fraud before it starts.

3. Strengthen online security with multifactor authentication

Passwords alone are often no longer enough. Multifactor authentication (MFA) requires users to provide two or more pieces of evidence, like a password or a one-time code, to access their accounts.

Pindrop offers multifactor authentication tools that Building Societies can use to add extra layers of protection to online and mobile banking, detect unauthorized access attempts even if login credentials are compromised, and provide a seamless user experience.

4. Use systems that can help detect fraud in real time

Fraud monitoring tools like Pindrop® Protect can help identify suspicious activity as it happens by analyzing:

Call metadata (e.g., origin, device, routing patterns)
Voice anomalies (e.g., deepfake indicators, tone shifts)
Behavioral patterns (e.g., frequency, timing, access points)

These insights contribute to the risk analysis in Pindrop® Protect, which can help banking institutions detect fraud early and respond quickly, minimize financial losses, and reduce false positives that block legitimate member access.

5. Collaborate with law enforcement and industry partners

Fraudsters often operate across networks. Sharing information is vital so that organizations across the industry can spread the latest information on threats and response strategies, and gain access to fraud databases. It is vital for organizations across the banking industry to share information on the latest fraud threats and response strategies as a shield from fraudsters operating across different networks.

Partnering with UK fraud initiatives, joining a fraud-prevention collective (like the benefits offered to Pindrop fraud consortium members) or financial crime task force, and working closely with law enforcement when fraud occurs are great ways to help protect your Building Society from repeat fraud attacks.

The role of AI in fraud detection

Artificial intelligence offers a powerful toolkit for detecting fraud before it causes damage to your organization.

Leveraging AI for pattern recognition

AI can process thousands of data points across multiple channels to detect anomalies in near real-time. For Building Societies, AI can learn what typical behavior looks like for members and flag deviations like unusual login times, unfamiliar devices, or rapid money movement.

These systems become more accurate over time to reduce false positives, remove friction for legitimate members, and improve fraud detection rates.

Deepfake audio detection for Building Societies

Fraudsters are increasingly using deepfake technology to impersonate members or staff over the phone. This kind of fraud is difficult for humans to detect, but Pindrop’s deepfake detection tools can help mitigate risk by:

Identifying signs of synthetic or manipulated speech
Detecting inconsistencies in audio waveforms
Alerting agents to suspicious calls in real-time

Future-proofing fraud detection strategies

Fraud tactics are evolving rapidly and detection strategies that work today may not be enough tomorrow. Continuous improvement is key to protecting both your institution and your members from fraud. To stay ahead, Building Societies should:

Regularly audit and update their security infrastructure
Invest in AI and machine learning technologies that evolve and improve over time
Create feedback loops between fraud detection teams, IT, and customer service

Enhance your fraud detection strategy with Pindrop solutions

As fraudsters become more creative, Building Societies need innovative tools to match. Pindrop voice security and fraud detection solutions can help protect members, reduce fraud losses, and streamline authentication processes without sacrificing your commitment to personal and ethical banking.

Want to see what smarter fraud detection looks like? Talk with an expert.

¹ https://www.ukfinance.org.uk/system/files/2024-10/Half%20Year%20Fraud%20Report%202024.pdf

By Pindrop’s Chief People Officer, Christine Kaszubski Aldrich

With over 25 years of experience in Human Resources, I’ve encountered countless challenges in talent acquisition—but nothing quite like the emerging threat we face today. The rise of fraudulent profiles and deepfake candidates is reshaping the hiring landscape in ways many never anticipated. Although people often perceive HR professionals as working behind the scenes, we’re actually on the front lines, protecting our organizations from this new wave of deception. As AI-driven technology advances, so does the sophistication of these fraudulent applicants, making it more critical than ever to adapt and safeguard the integrity of our hiring processes.

What are deepfake candidates?

Deepfake candidates are job applicants either completely generated by AI or having their appearance significantly altered using deepfake technology. This technology creates highly realistic fake videos, images, or audio. These candidates appear legitimate at first glance, with polished resumes, LinkedIn profiles, and even video interview capabilities that can fool recruiters and hiring managers.

Here’s the thing—many of us don’t even realize they exist, which is a threat in itself.

How are deepfake candidates created?

Advancements in artificial intelligence have made it easier than ever to generate convincing fake candidates.

Some of the most common techniques include:

Fake LinkedIn Profiles: AI can generate fake profile pictures and engagement history, making it difficult to detect fraudulent identities.
AI-Generated Resumes & Cover Letters: Using ChatGPT or other AI models, applicants can fabricate entire work histories with compelling descriptions and industry jargon.
Fabricated Work History & References: Fraudsters may list fake companies or provide references who are part of the deception.
Deepfake Video Interviews: AI can manipulate real-time video feeds, allowing imposters to mimic another person’s face and voice during virtual interviews.

The impact on companies

Fraudulent job applicants are not just a concern for large corporations—they present a significant risk to small and mid-sized businesses, which often lack the resources and infrastructure to detect and mitigate more sophisticated forms of hiring fraud. For example, the FTC reported that imposter scams are the No. 1 ranked fraud category, totaling $2.9B in losses¹. This trend weakens hiring integrity and threatens a Chief HR Officer’s (CHRO) mandate to secure top talent—a key growth driver amid labor shortages and skills gaps. CHROs must employ enhanced vetting, cybersecurity strategies, and tools to protect our talent pipelines.

Hiring a deepfake candidate can have severe and far-reaching consequences beyond simple deception and pose elevated financial, operational, security, and reputational risks, making it a critical concern for organizations across industries.

Here’s a closer look at the potential impact:

Ransom + Extortion

Once inside a company, deepfake employees can hold systems hostage, locking critical files and demanding ransom payments. This could result in millions in losses, not just from the ransom but also from system downtime, recovery efforts, and legal fees.

Foreign Currency + Payroll Fraud

Deepfake candidates may exploit payroll systems to collect salaries while leveraging access to foreign currencies for illicit financial operations. Bad actors from sanctioned nations could use fraudulent employment to bypass financial restrictions and fund unauthorized activities.

Data Breaches + Intellectual Property Theft

These individuals can access confidential data, trade secrets, and client information, putting a company at risk of regulatory penalties, lawsuits, and loss of competitive advantage.

Customer Trust + Brand Damage

If the public discovers that a company hired a fraudulent employee, customer confidence could erode, especially in industries where trust is paramount (e.g., banking, legal, consulting). Competitors can use this to gain market share, further compounding the loss.

Stock Price Decline

Publicly traded companies may negatively impact stock prices if news breaks that they unknowingly employed a fraudulent individual. Investors may perceive the incident as a failure in internal controls and governance, leading to devaluation and potential shareholder lawsuits.

Rehiring Costs + Lost Productivity

When a company discovers a deepfake candidate, it will have already wasted resources on onboarding, salary payments, and training. The costs of re-hiring lost productivity, and potential team turnover due to the incident can be staggering.

A disturbing reality: deepfake candidates are already here

At Pindrop, we specialize in identifying and helping our customers mitigate deepfake threats, and we’ve even faced attempts to target our hiring process. For one job posting alone, we received over 800 applications in a matter of days. When we conducted a deeper analysis of 300 candidate profiles, over one-third were fraudulent. These weren’t just candidates exaggerating their experience—these were entirely fabricated identities, many leveraging AI-generated resumes, manipulated credentials, and, most concerning, deepfake technology to simulate live interviews.

But it didn’t stop there.

Recognizing this as a serious and growing threat, we saw an opportunity to investigate further. As a company dedicated to securing real-time communications, we recognized the challenge of deepfakes long before many others. However, we never expected fraudsters to be bold enough to test our award-winning technology. Yet, they did. And when they did, we were ready. What we uncovered was even more alarming than anticipated, reinforcing the urgency for organizations to take proactive measures against deepfake infiltration before it’s too late.

The Curious Case of “Ivan X”

It started with an application for a Senior Backend Engineer position. The candidate, “Ivan X,” appeared well-qualified on paper, but during the video interview, several red flags immediately stood out:

Unnatural Facial Movements: His facial expressions seemed slightly out of sync with his words, a telltale sign of deepfake video manipulation.
Audio-Visual Lag: His voice occasionally dropped out or did not align perfectly with his lip movements.
Inability to Adapt: When the interviewer asked an unexpected technical question, the candidate paused unnaturally, as if processing the response before playback.

The evidence was undeniable—our technology, Pindrop® Pulse, confirmed what we suspected: we were face-to-face with a deepfake candidate.

Pindrop Pulse for meetings

We’re extending our deepfake detection technology beyond contact centers and into video meetings. With Pulse for Meetings, organizations can safeguard virtual conversations by detecting AI-generated voices, face swaps, and synthetic avatars in real time.

Below is a teaser of the interview and our technology running in it. What you’re seeing is a bounding box around the face as we track movement across frames, analyzing for AI-generated artifacts hidden beneath. To learn more, reach out to us at [email protected].

Déjà Vu: When “Ivan X” applied again

Eight days later, Ivan X resurfaced, this time applying through a different recruiter. We had already flagged the original as fraudulent, so we decided to let the interview proceed to observe any variations.

The results were startling:

This time, the person who joined visually appeared different, yet he carried the same identity and credentials as the previous “Ivan X.”
Within moments, he encountered connection issues, dropped from the call, and rejoined—a tactic we suspect he used to recalibrate the deepfake software.
When he returned, the same audio-visual lag and facial inconsistencies were present, mirroring our previous interaction. However, the deepfake appeared to have been improved, highlighting how quickly these bad actors can improve their use of technology.

This validation reinforced our suspicions—what we encountered was not an isolated incident but a deliberate and coordinated attempt to infiltrate our hiring process using deepfake technology and synthetic identities.

Why this matters

Deepfake candidates are no longer a futuristic concern—they are an active and sophisticated attack vector infiltrating businesses today. Gartner predicts that by 2028, one in four candidate profiles worldwide will be fake². The US Bureau of Labor Statistics reports that employers hired an average of 5 million people per month in 2024³. Assuming 3-6 interviews per hire, US hiring managers could face 45-90 million deepfake candidate profiles this year. This hyperscaling of deepfake attacks on hiring and HR practices is an unprecedented risk.

The rise of deepfake candidates isn’t just about falsified identities—it’s a direct threat to cybersecurity, corporate espionage, and data protection. What critical systems would “Ivan X” have compromised if the company had hired him? How many organizations have already unknowingly welcomed deepfake candidates into their workforce? These real-world cases highlight the growing threat:

WSJ: Deepfakes, Fraudsters, and Hackers Are Coming for Cybersecurity Jobs
BBC: Firm hacked after accidentally hiring a North Korean cybercriminal
TechTarget: KnowBe4 catches North Korean hacker posing as an IT employee

While our advanced technology confirmed our findings, the unsettling truth is that most companies remain unaware of this growing threat or assume it could never happen to them. In reality, no organization is immune—especially those operating in remote-first or globally distributed environments. Fraudsters actively exploit hiring vulnerabilities in engineering, IT, finance, and beyond, seeking access to sensitive systems, proprietary data, and financial assets. Organizations lack the necessary tools and strategies to handle these risks. Many organizations depend on the vigilance of HR managers to catch fraudsters based on visual cues. Still, research shows that human intuition is not an effective and reliable method of identifying deepfakes⁴.

The implications are massive, and organizations must adapt now—because deepfake applicants aren’t just a possibility; they’re already here. The question is no longer whether attackers will target your company but when.

Are you prepared to detect and stop them before it’s too late?

Stay tuned for Part II of our blog post, “Think You Won’t Be Targeted by Deepfake Candidates? Think Again”, where we’ll explore how you and your organization can detect and verify deepfake candidates – protecting your business from critical threats that go far beyond hiring the wrong person. To learn more, reach out to us at [email protected].

Sources + Citations

¹ FTC Consumer Sentinel Network Databook, 2025

² Gartner: Predicts 2025: AI Revamps Recruitment Processes and Skills Management, 2025

³ https://www.bls.gov/news.release/pdf/jolts.pdf

⁴ https://synthical.com/article/c51439ac-a6ad-4b8d-82ed-13cf98040c7e

Guide

How UK Building Societies Can Detect Fraud Without Compromising Member Experience

This guide explores the growing fraud threats facing UK Building Societies, including authorized push payment scams, account takeovers, and impersonation fraud, and how they put members and institutions at risk. Learn about the limitations of traditional security measures and how advanced fraud detection and authentication technologies can help prevent financial losses while maintaining a seamless member experience.

What’s in the guide?

How fraudsters exploit vulnerabilities in phone and digital banking and the financial impact on UK Building Societies
How technologies like voice authentication, call metadata analysis, and fraud intelligence networks help identify fraudsters in real time and prevent financial losses
How modern authentication methods can reduce fraud while making banking interactions faster and more seamless for members

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Learn about the limitations of traditional security measures and how advanced fraud detection and authentication technologies can help prevent financial losses while maintaining a seamless member experience.

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Recent innovations in deepfake technology and AI-driven applications have brought synthetic media to the forefront of cybersecurity concerns. From artificial intelligence-generated videos that replicate a public figure’s likeness to audio-based deepfakes capable of mimicking a familiar voice, the technology behind these fakes is advancing rapidly.

The implications are significant: misinformation, social engineering, and identity fraud are just a few of the outcomes that might result from these convincing simulations.

In this article, we discuss the top deepfake trends that will dominate conversations about cybersecurity in 2025. We focus on the rise of voice-based deepfakes, new detection methods, legal developments, ethical AI, and social engineering exploits.

We’ll also highlight how businesses can respond to these changes, strategies, tools, best practices, and more.

Rise of voice-based deepfakes

Increased sophistication in audio deepfakes

Voice-based deepfakes are no longer novel internet experiments. Generative AI tools, spurred by leaps in deep learning and advanced TTS (text-to-speech) capabilities, allow fraudsters to replicate voices with remarkable accuracy.

Cybercriminals can capture voice samples from interviews, podcasts, or social media clips. Once they feed these recordings into neural network–based models, they can generate AI voices that closely resemble the original speaker’s pitch, cadence, and unique mannerisms.

The potential for abuse is high. Real-time impersonation of executives, family members, or customer service representatives can lead to unauthorized transactions, data breaches, or social engineering schemes.

A McAfee survey revealed that one in four adults had experienced or known someone affected by an AI voice cloning scam, and 70% were unsure of their ability to distinguish cloned voices.

Additionally, recent studies by Synthical found that, on average, people struggle to distinguish between synthetic and authentic media, with the mean detection performance close to a chance level of 50%. They also found that accuracy rates worsen when the stimuli contain any degree of synthetic content, feature foreign languages, and the media type is a single modality.

A coordinated attacker might deploy a voice-based deepfake to manipulate or trick contact center agents, especially if the attacker has stolen personal information from data breaches. These developments illustrate why it’s becoming so urgent for organizations to recognize and address deepfake trends.

Voice phishing (vishing) becoming more prevalent

As voice deepfakes become more accurate, so do targeted attacks involving “vishing.” According to a 2024 APWG report, vishing and smishing attacks rose 30% (in Q1 2024) compared to the previous quarter (Q4 2023). Criminals use AI-generated calls to pose as familiar voices—like a high-level executive within a company—and request urgent financial transfers or sensitive internal data.

Security teams are already facing challenges from these kinds of impersonation, and the trend, driven by affordable AI-as-a-service platforms, is expected to increase in the coming years. After all, the AI-as-a-service market was estimated at $16.08 billion in 2024 and is projected to grow at a CAGR of 36.1% from 2025 to 2030.

It’s worth exploring how infiltration might occur through contact center interactions. Traditional verification questions are less effective against criminals who blend plausible personal details with an AI-synthesized voice.

For a deeper overview of how these threats can spill over into different settings like healthcare, see our article: Understanding the threat of deepfakes in healthcare.

Advancements in deepfake detection technology

Development of AI-powered tools to identify deepfakes

On the positive side, businesses, researchers, and government agencies are dedicating considerable time to improving deepfake detection. AI algorithms can help isolate imperceptible artifacts or inconsistencies within synthetic audio.

For example, some models zero in on tonal shifts, background static, or timing anomalies that might not be obvious to human listeners. Voice-based detection solutions increasingly rely on machine learning training sets that compare thousands of human vs. synthetic voice samples.

Focusing on liveness detection helps identify the specific markers of synthetic speech and determine if it has suspicious voice patterns. Rather than relying on static checks, these solutions examine the audio stream in real time, searching for signs of robotic or artificially-generated content.

Integration of deepfake detection in cybersecurity systems

Deepfake detection is also becoming a standard consideration for modern cybersecurity frameworks. For instance, organizations that rely on multifactor authentication may add a layer of voice-based checks. If the system suspects AI-generated audio, it could require an extra step, such as confirming a known device.

Retailers, in particular, face unique challenges because voice-based impersonations can compromise everything from loyalty accounts to credit card data over the phone. Learn how retail contact centers can integrate advanced detection within a broader security system and more.

For more on best practices in multifactor approaches, check out our multifactor authentication solution, which is designed to authenticate callers without disruption.

Ethical AI and responsible deepfake development

The rise of deepfakes has also sparked a discussion around ethical AI. Researchers, non-profit organizations, and technology leaders push for transparency and accountability in AI model development.

Responsible AI frameworks encourage thorough risk assessments, data privacy considerations, and ethics boards to review how new models could be misused. Some social media platforms experiment with content labels or watermarking solutions to flag AI-generated media.

This discourse isn’t limited to theoretical debates. Media outlets and political actors are also exploring how to stop the spread of false information. Some of our resources expand more on this topic:

Demonstrating Pindrop technology’s role in election integrity

How deepfakes are impacting public trust in media

Deepfakes and the escalation of political conflict

Deepfakes in social engineering

Social engineering often relies on manipulating human judgment, and deepfakes are poised to take these schemes to new levels. AI-generated voice or video can be added to email or phone-based phishing attempts to lend an air of legitimacy.

Picture an employee in the finance department receiving what appears to be a video call from a known executive instructing them to authorize a bank transfer immediately.

The threat extends beyond single interactions. Attackers may plan multi-step campaigns, with each layer adding authenticity to the ruse. As explained in our deepfake attacks in 2024 article, fraudsters might gather personal details through low-level intrusion attempts and then escalate to more targeted attacks once they’ve compiled enough data.

Development of new strategies

Fraudsters consistently evolve. Organizations may face unpredictable and more aggressive strategies as they experiment with combining deepfakes, data scraping, and older techniques like spear phishing.

In some incidents, deepfake audio can be paired with spoofed emails, making the scam appear verbally and textually legitimate. Combining synthetic voice with well-researched personal information can spell trouble for organizations.

Attackers can now mimic real-time conversations, further confusing human call-handling staff. For this reason, experts strongly recommend adopting advanced authentication and deepfake detection solutions as a protective measure.

Protect yourself from deepfake attacks with Pindrop^® solutions

While new technology can be daunting, solutions exist to help mitigate these evolving threats. Liveness detection is an essential approach: it pinpoints key markers in audio or video that indicate whether an actual living human or AI generates content.

Liveness detection can spot audio anomalies—areas where the voice’s tonality, breath, or resonance doesn’t match typical human patterns.

Pindrop^® Pulse refines these steps by alerting your contact center agents when synthetic voices are detected—providing critical defense against emerging threats caused by the rise of AI-generated content. Learn more about Pindrop^® Pulse.

Companies also benefit from a multifaceted security strategy, blending voice detection with other tools. For instance, robust identity validation processes can make it harder for attackers to rely on voice impersonation alone. Tools like multifactor authentication can prompt additional verification steps, forcing criminals to contend with multiple defenses simultaneously.

Ready to take the next step? Check out Pindrop’s deepfake detection technology.

Voice phishing is a deceptive technique that cybercriminals employ to trick individuals into giving personal and sensitive information over the phone. But did you know that voice phishing is evolving rapidly with technology?

Cybercriminals are no longer restricted to traditional methods; they now use advanced tools to scale their attacks. With AI voice scamming, fraudsters can bypass recognition systems, impersonate trusted organizations, and trick victims into revealing sensitive information.

What is voice phishing?

Voice phishing is a form of social engineering in which fraudsters use phone calls to impersonate trusted entities, such as banks, government agencies, or even technical support teams.

The ultimate goal is manipulating the caller into sharing sensitive information, like account credentials, Social Security numbers, or financial details. These fraudsters are skilled at creating a sense of urgency or fear, pressuring victims into acting hastily.

According to a 2023 Federal Trade Commission report, imposter scams were the leading fraud category, with reported losses reaching $2.7 billion. These scams frequently involve perpetrators posing as a bank’s fraud department, government representatives, or even distressed relatives.

Voice phishing vs. vishing vs. smishing

While these terms are often used interchangeably, they refer to distinct methods of social engineering scams. Understanding the differences can help you recognize and protect against these threats.

Here’s a quick breakdown of the three for better understanding:

	Definition	Delivery method	Typical tactics	Examples
Voice phishing	A phone-based scam in which fraudsters manipulate victims into sharing personal or financial information.	Phone calls	Impersonating trusted entities, such as banks or government agencies, using caller ID spoofing to seem legitimate.	A caller claims to be your bank, warns of unauthorized charges, and requests account details to “secure” your funds.
Vishing	A subset of voice phishing, often emphasizing voicemail-based scams.	Voicemail or phone calls	Leaving urgent messages prompting victims to call back.	A voicemail claims unpaid taxes and threatens legal action unless you call the provided number.
Smishing	Text-based phishing scams are designed to trick victims into clicking malicious links or sharing personal data.	SMS or messaging apps	Sending links disguised as legitimate services, such as delivery updates or urgent account-related messages.	A text claims your account is locked and includes a link to “verify” your details.

Why voice phishing attacks are a growing threat

With technological advancements, these scams are becoming more sophisticated. For example, AI tools can simulate realistic human voices, automate conversations, and even tailor scams based on the target’s responses, increasing success rates across all channels.

Voice phishing is rising rapidly, fueled by widely available AI technologies that allow fraudsters to scale their operations. Tools like open-source text-to-speech (TTS) enable fraudsters to generate synthetic voices indistinguishable from real ones.

According to a 2024 APWG report, vishing and smishing attacks rose 30% [in Q1 2024] compared to the previous quarter [Q4 2023].
A McAfee survey revealed that one in four adults had experienced or knew someone affected by an AI voice cloning scam, with 70% unsure of their ability to distinguish cloned voices.

How does a voice phishing scam work?

Voice phishing typically unfolds in these stages:

Initiating the call: Voice phishing scams often begin with a well-crafted call that seems genuine. The scammer may present as a bank representative, IRS agent, or tech support specialist. They can employ tactics such as Caller ID spoofing to make the call appear legitimate.
Creating urgency: Once they have your attention, they will develop a sense of urgency. They might claim your account is compromised, your taxes are overdue, or your computer is infected with a virus. This urgency is designed to make you drop your guard.
Extracting information: Scammers may ask for personal details, like your Social Security number, birth date, or account passwords. They might even request a financial transaction to “secure” your account.
AI-driven tactics: With the advancements in AI technology, they are also implementing tactics such as:
- Synthetic voices are used to perform account reconnaissance or bypass Interactive Voice Response IVR authentication.
- Provide accurate responses to security questions and one-time passwords (OTPs).
- Deploy voice bots to impersonate targeted individuals or organizational IVA agents, gathering internal information and bypassing fraud detection systems.

Common voice phishing techniques

Impersonation scams

These scams involve fraudsters posing as trusted institutions, like banks or government agencies, to lure victims into disclosing personal information. They may claim your account has been compromised and request your account details to “secure” it. Always verify the caller’s identity independently before sharing any information.

Tech support scams

Scammers pretending to be technical support agents claim that your computer is infected or experiencing issues. They will request remote access to your system or demand payment for their “services.” Never grant remote access to unknown callers or make payments to resolve issues you weren’t aware of.

Bank scams

Voice phishing scammers may claim to be from your bank and tell you that there is a problem with your account. They may then ask you for your account number, PIN, or Social Security number. For example, a scammer might tell you that your debit card has been compromised and that you must immediately provide your new PIN.

Government agency scams

Vishing scammers may claim to be from a government agency like the IRS or the Social Security Administration. They may tell you that you owe money or that your identity has been stolen. For example, a scammer may say to you that you owe back taxes and that they will garnish wages if you don’t pay immediately.

How voice phishing scammers might use the stolen information

Voice phishing scammers can use the stolen information in various malicious ways. Here are some common scenarios:

Identity theft: With access to your Social Security number and personal details, scammers can commit identity theft. They may open credit accounts in your name or engage in other criminal activities using your identity.
Financial fraud: Scammers can use your financial information, such as credit card details, to make unauthorized purchases, withdraw funds from your accounts, or transfer money to themselves.
Account takeover: If scammers obtain your login credentials for online banking, email, or other accounts, they can take control of these accounts. This can lead to further exploitation and privacy invasion.
Phishing: Scammers may use the stolen information to craft convincing phishing emails or text messages. By impersonating trusted organizations or contacts, they can trick you into revealing even more sensitive information or clicking on malicious links.
Blackmail: Scammers might threaten to reveal embarrassing or compromising information they have acquired, coercing you into paying them to keep it secret.
Romance scams: If scammers have gathered personal details about your life, they may use this information to build trust in romance scams and manipulate you emotionally.
Social engineering: The stolen information can be used for further voice phishing attempts. Scammers can contact you again, armed with more details, to make their calls even more convincing.

Voice phishing red flags to watch for:

Unsolicited calls asking for sensitive information: Legitimate organizations rarely request such details out of the blue.
Pressure tactics and urgent requests: Scammers often create a false sense of urgency.
Requests for payment via unusual methods: Beware of demands for payment through gift cards or wire transfers.
Poor audio quality or background noise: This can indicate a scam call.
Inconsistent information: Verify details that seem contradictory.
Unusual caller behavior: Scammers may act overly aggressive or evasive.

How to protect yourself from voice phishing scams

1. Be suspicious of unsolicited phone calls from trusted organizations

Be cautious of any unsolicited phone call claiming to be from a trusted organization. Legitimate entities typically do not make unexpected calls to ask for your personal information.

2. Never give out personal information over the phone

If you are unsure about the legitimacy of a call, hang up and call the organization back at a known phone number.

3. Be wary of urgency and scare tactics

Legitimate organizations will not pressure you to make a decision immediately, and they will not threaten you with legal action or financial losses if you do not comply with their demands.

4. Notice unusual caller behavior

Be alert to inconsistencies in the caller’s behavior, such as evasive answers, overly aggressive tactics, or reluctance to verify.

5. Beware of calls from unfamiliar phone numbers

If you don’t recognize the number, let the call go to voicemail and verify its legitimacy before responding.

6. Do not call back phone numbers left on your voicemail

Scammers often leave voicemail messages that contain a callback number. You may be connected to a scammer if you call back this number.

7. Avoid clicking on links in text messages or emails

Links claiming to be from trusted organizations can lead to phishing websites designed to steal your personal information.

8. Use call blocking and caller ID features

Leverage call-blocking technology and caller ID to filter unknown or potentially fraudulent calls.

9. Regularly update your security software

Ensure your security software is up-to-date to help detect and block phishing attempts and malware.

Leverage technology to combat voice phishing

Fraudsters can successfully scam a caller and then use the information they gather to attack contact centers, attempting to complete unauthorized transactions that can be costly.

Today, technology can combat the consequences of voice phishing, especially as it evolves. By pairing voice analysis with additional factors like liveness detection, organizations can enhance fraud detection in contact centers–helping catch suspicious behavior early. These tools help distinguish between human and synthetic or machine-generated voices, providing a stronger defense against sophisticated scams.Additional technologies that strengthen security include multifactor authentication (MFA), fraud detection, and deepfake detection software. These solutions are especially critical for industries like banking, which are the primary targets.

Additional technologies that strengthen security include multifactor authentication (MFA), fraud detection, and deepfake detection software. These solutions are especially critical for industries like banking, which are the primary targets.

Defend against voice phishing and protect your organization with Pindrop^Ⓡ solutions

In the fight against voice phishing, Pindrop provides cutting-edge tools to safeguard sensitive interactions in contact centers. Pindrop® Pulse leverages advanced liveness detection software, analyzing vocal features to distinguish human voices from synthetic ones. These solutions integrate seamlessly with existing Pindrop solutions, providing enhanced authentication and fraud detection.

By using Pindrop® Passport, organizations can implement multifactor authentication, pairing voice analysis with other security measures for unparalleled accuracy.

Take the next step in securing your operations—request a demo today and discover how Pindrop can help protect organizations from voice phishing scams.

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Contact centers are under significant pressure to manage calls efficiently, especially as volumes begin to rise. Customer authentication is obviously a big priority and is critical to ensuring the security of the contact center.

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A proper call center audit can help determine where your company needs to focus on providing higher security. The audit looks at each stage of security for call center success. With the speed of AI, the ultimate goal is always to help your company get ahead of these trends to stop fraud in your call center before it happens.

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Retail organizations face significant fraud losses yearly, with fraudsters continually finding ways to bypass conventional security. Contact centers, in particular, can become vulnerable when they rely on outdated methods to verify a caller’s identity.

While knowledge-based questions or one-time passwords (OTPs) can deter some unauthorized attempts, these techniques are no longer robust enough to withstand sophisticated attacks.

This is where multifactor authentication (MFA) in retail—especially voice-based approaches—can help companies strengthen security, maintain customer trust, and prioritize overall business resilience.

Voice call interactions remain critical in retail for customer service and high-stakes transactions such as refunds or major account updates. Although organizations may spend considerable time and resources confirming callers’ identities, these efforts can still fall short.

Multifactor authentication incorporating voice analysis can enhance customer verification and give time back to contact center teams.

Introduction to multifactor authentication in retail

Multifactor authentication is a security approach that requires users to present two or more verification forms before granting access to an account, transaction, or service.

Traditionally, these factors include something a user knows (e.g., a password or PIN) and a user has (e.g., a device or token). Voice-based authentication extends this framework even further, where voice analysis functions as an additional factor.

In retail, contact center agents often manage a high volume of sensitive requests, such as order cancellations, credit card updates, or loyalty account changes. Fraudsters can exploit weak layers of security—like knowledge-based authentication—to impersonate legitimate customers.

For instance, if a criminal knows basic account details or has intercepted an OTP, they can access an account and make unauthorized changes. MFA is pivotal for companies that want to close these security gaps.

Voice analysis in retail MFA

Voice analysis involves determining if a voice on the call matches the enrolled voice profile. When retail contact centers integrate MFA with voice analysis, they gain an additional authentication factor that criminals find challenging to compromise.

Upholding a smooth customer experience and reducing fraud losses is possible when security methods move beyond knowledge-based questions and OTPs.

Voice-based authentication methods

Voice-based authentication methods generally fall into two categories:

Text-dependent authentication
- Involves callers speaking a specific phrase (e.g., “My voice is my password”).
- The system compares the spoken phrase to a voiceprint template and checks whether the words’ content and vocal characteristics match those of the enrolled user.
- Useful for organizations that want a straightforward user experience. Callers can recite a single phrase, which the system quickly verifies.
Text-independent authentication
- Callers can speak naturally on any topic briefly (e.g., everyday conversation with an agent or IVR).
- The system performs a voice analysis in the background, comparing the voice to the enrolled voice profile—there is no need for a specific phrase.
- This is ideal for busy contact centers that want minimal disruption to the typical call flow. You can authenticate customers while they discuss their issue or request.

The choice between these two customer verification methods depends on your organization’s operational needs and desired level of reducing friction.

Benefits of voice authentication

Retailers stand to gain multiple benefits by incorporating voice authentication into their MFA strategy:

Reduced fraud losses: Fraudsters can sometimes guess answers to security questions or intercept OTPs, but mimicking a voice is far more complex. A voice-based system can cut potential losses by catching suspicious calls early.
Better customer data protection: Voice authentication makes it more difficult for attackers to impersonate legitimate customers, which helps protect sensitive information like saved credit cards or loyalty program details.
Time savings: Voice analysis can automate the initial verification phase, shortening authentication steps. Agents can then spend less time probing callers with security questions and more time resolving their requests.
Enhanced brand reputation: Companies that adopt advanced data protection measures often gain a competitive edge. By prioritizing voice call security, retailers can show customers they value privacy and reliability, strengthening brand loyalty.
Flexible integration: Retailers can integrate text-dependent or text-independent voice solutions into existing Interactive Voice Response (IVR) systems or customer support platforms. This modular approach allows contact centers to adapt without overhauling their technology stack.

Securing customer transactions with MFA

A retail contact center fielding credit card changes or large refund requests can integrate voice authentication into its verification flow. If the system flags inconsistencies (for instance, the voice doesn’t match the enrolled voice profile or the device signature appears risky), it can route the call to a higher-level review. This approach helps protect customer accounts and spares legitimate callers from overly intrusive questioning.

Combining MFA and passwords can also add an extra confirmation level. By monitoring call details alongside voice analysis, you can strengthen your ability to validate the caller.

Customer experience considerations: Balancing security and convenience

Striking a balance between user convenience and robust security can be a challenge. Customers often have limited patience for lengthy verification steps and expect quick interactions with your contact center.

A solution that incorporates multiple factors—like voice, behavior, and device analysis—allows you to validate a caller without forcing them to remember additional passwords or endure repeated prompts. Text-independent voice authentication, for example, can verify someone’s voice during a normal conversation, creating a more pleasant experience.

That said, if the system flags anomalies, giving agents a clear path for escalating calls is critical. If your processes are too rigid, genuine customers might be treated with unnecessary suspicion, damaging their impression of your brand.

Safeguard your customer’s data with Pindrop® solutions

Securing data is no longer optional for retail contact centers. As fraudsters become adept at bypassing knowledge-based questions or seizing one-time passwords, retailers need new methods to strengthen data security without sacrificing ease of use. That’s where Pindrop® Solutions can help.

Pindrop’s entire product suite bolsters contact center defenses by combining voice security, device analysis, and advanced risk detection. Here’s how each product supports MFA in retail:

Pindrop® Passport
- Reduce operational costs and lower average handle time (AHT) with multifactor authentication that allows you to accelerate call routing for authenticated callers while maintaining contact center security. Discover more about Pindrop® Passport.
Pindrop® Protect
- With near real-time fraud risk assessments, Pindrop® Protect can catch fraud early—better protecting customer accounts. It provides a comprehensive risk score for each call, starting in the IVR and continuing through the agent call stage. Learn more about Pindrop® Protect.
Pindrop® Pulse
- Enable your contact center agents to identify synthetic voices. This will provide critical protection against emerging threats caused by the rise of AI-generated content. Explore liveness detection with Pindrop® Pulse.

By adopting multifactor authentication—which may include voice analysis, device checks, or additional verification steps—your retail contact center can strengthen its security framework, benefiting both customers and agents. A layered approach also improves call handling times, lowers the risk of fraud, and bolsters customer confidence.

Ready for more details?

For more insights into the importance of advanced authentication, we invite you to see how other organizations have transitioned from legacy to modern authentication and learn why industry leaders are switching to Pindrop solutions by viewing The legacy letdown: Why industry leaders are moving to Pindrop.

Explore multifactor authentication to learn about our full range of solutions for retail contact centers.

Deepfake voice detection has emerged as a critical line of defense for businesses or individuals grappling with advanced forms of fraud.

Traditionally, organizations relied on manual processes to verify who was on the other end of the line. However, these methods are no longer sufficient in a world where artificial intelligence (AI) can replicate voices with startling accuracy.

The problem is apparent: AI-generated speech can fool people into sharing confidential information or authorizing unauthorized transactions. Symptoms include account takeovers, synthetic account reconnaissance, and social engineering attacks, all of which can devastate an organization’s finances and reputation.

The solution? A modern approach known as deepfake voice detection, bolstered by machine learning and robust identity verification strategies, is designed to stay one step ahead of fraudsters.

What is deepfake voice detection?

Deepfake voice detection refers to technology that can identify artificially generated, cloned, or other synthetic voices.

Deepfake voice is typically created using AI algorithms—often advanced Text-to-Speech (TTS) systems—that can replicate a target individual’s tone, speech patterns, and more.

For instance, a fraudster might clone a CEO’s voice, contact employees with urgent, plausible requests, or pose as a contact center customer to reset account access.

The hallmark of deepfake voice detection is its ability to analyze subtle acoustic and behavioral traits that may seem normal to the human ear but reveal mechanical signatures of synthetic generation.

Before they escalate, you can block scams from detected deepfakes. When combined with other advanced security layers, such as multifactor authentication, knowledge-based verification, and device analysis, voice deepfake detection creates a strong defense against identity fraud.

The increasing sophistication of TTS systems and cost-effective AI platforms means deepfake scams are no longer limited to well-funded fraudsters. They’re accessible to almost anyone and are affecting many industries, including but not limited to:

Financial services: Fraudsters employ deepfake voices to trick contact center agents into granting account access. Learn more about banking + financial fraud detection.
Healthcare: Insurance or patient record scams using voice impersonations to gather personal data. Learn about fraud detection for healthcare institutions.
Media and politics: Spreading disinformation or propaganda by cloning public figures. Learn about deepfakes and the escalation of political conflict, or how deepfakes increase media distrust.

How is a voice deepfake created?

Creating a voice deepfake is surprisingly straightforward, thanks to modern and accessible TTS tools. Fraudsters gather audio samples of the target victim, often from social media, interviews, or any publicly available source.

The more extensive and precise the sample set, the more realistic the resulting synthetic voice will be.

For more real-world insights into this type of fraud, see our article on preventing biometric spoofing with deepfake detection.

Why traditional voice authentication needs deepfake detection

According to a study by Synthical, humans are only 54% accurate in detecting audio deepfakes. This means there is a good chance that a realistic AI voice can fool human ears. However, this accuracy may decline even further as AI technology advances.

Another related concern is the growing ease with which personal data can be obtained from the dark web. Armed with this data, criminals can train generative models (like “FraudGPT”) to produce realistic voice content with credible personal details.

Additionally, many organizations still rely on conventional voice authentication methods. With deepfake technology maturing, these methods have become dangerously inadequate. Let’s learn about them.

Static voice profiles

A voice profile is like a digital signature of a person’s voice, often created during an enrollment phase. While useful in controlled scenarios, static voice profiles struggle against deepfakes that mimic an enrolled voice closely. If a deepfake is close enough, the system might fail to differentiate the real from the synthetic.

Limited analysis

Older voice authentication solutions often focus on a narrow range of acoustic features. This limited analysis is insufficient to detect advanced spoofing attempts incorporating various vocal traits, such as pitch, tone, and more. Sophisticated TTS clones can replicate most of these attributes, sidestepping detection.

Vulnerability to spoofing

Conventional systems cannot handle elaborate impersonation attempts. Fraudsters can easily combine stolen data (such as Social Security numbers or account details) with a cloned voice.

If the deepfake is similar enough, the system might grant access. Consider a scenario of synthetic account reconnaissance, where attackers gather account details using a manipulated voice to pass security checks in the IVR.

Lack of adaptability

Fraudsters evolve quickly, but many older authentication methods don’t keep up. Once fraudsters learn a system’s weaknesses, they can replicate attacks across multiple victims.

Fraudsters use these static processes to scale their operations, particularly in contact centers that handle large call volumes.

Susceptibility to social engineering

Highly realistic, AI-generated voices can trick human operators, especially if they seem to have all the correct answers. Data from the dark web can inform the content of the speech, further making it credible. Agents may unknowingly provide sensitive details, enabling more sophisticated attacks.

Benefits of deepfake voice detection for businesses

As fraudsters adopt AI-driven tactics, organizations must upgrade their security measures. Below are a few ways voice deepfake detection technology can help:

Fraud detection: Analyzes multiple vocal features and background signals to detect synthetic audio.
Regulatory compliance: Demonstrates adherence to data protection regulations by adding layers of identity verification.
Customer trust: Reduces the risk of unauthorized account access, bolstering customer confidence.
Scalability: Many deepfake detection solutions integrate seamlessly into existing systems, including IVRs and contact centers.
Real-time alerts: This enables organizations to flag suspicious calls and prompt additional security checks immediately.

Pindrop^® Solutions helps banking, insurance, healthcare, and retail organizations experience these benefits and reduce the potential for significant fraud losses.

For a deeper look at how advanced audio deepfake detection can safeguard against identity spoofing, check out our solution overview: audio deepfake detection.

Understanding how voice detection works for deepfakes

For the sake of simplicity, we’ll break down the detection process into key steps. Keep in mind that, in reality, advanced machine learning algorithms are used, and ongoing development refines these models as new threats appear.

Step 1: User enrollment (one-time setup)

A caller enrolls in voice authentication. The system creates a voice profile reflecting various acoustic features (tone, pitch, speaking speed, etc.). This profile is sometimes referred to as a baseline.

Example scenario: A bank’s call center enrolls a customer by having them speak a few specific phrases to capture voice data.

Step 2: User authentication (every login attempt)

When the user calls again, the system compares the live input to the stored profile. Beyond matching static characteristics, modern solutions cross-reference additional signals like device details or geolocation metadata, further refining the verification process.

Example scenario: The user calls the bank to reset a password. The authentication system checks if the caller’s current voice analysis signature matches their enrolled voice profile and if their device ID is recognized.

Step 3: Real-time voice analysis

At this stage, liveness detection technology analyzes the caller’s voice for anomalies indicative of deepfake or machine synthesis. These include unnatural fluctuations, digital artifacts, or suspicious time-frequency patterns. Additionally, the system might check for consistency in background noise or breathing patterns.

Example scenario: A fraudster tries to pass AI-generated speech as accurate. The liveness detection system identifies the synthetic markers in the audio, flags the call as high-risk, and triggers a secondary verification.

Step 4: Decision and response

Based on the analysis, the company’s system or policies determine whether to confirm, challenge, or deny the caller’s identity. For example, if a potential deepfake is detected, the company system can alert the relevant security personnel or automatically route the call for manual review.

Example scenario: If the voice analysis is inconclusive, the company’s system might prompt the caller with extra security questions or route the call to a specialized fraud team.

Step 5: Continuous learning and improvement

Voice deepfake detection solutions often employ machine learning models that retrain regularly to keep pace with evolving fraud techniques.

Pindrop^® solutions, for instance, analyze new data from real-world attempts and incorporate these insights into updated detection algorithms.

Example scenario: Once the fraud department confirms that a call was indeed synthetic, the system learns from this instance and refines its detection model to be more accurate in the future.

Technologies behind deepfake voice detection

AI and deep learning models

Deep learning is central to both creating and detecting deepfakes. Many solutions use convolutional neural networks (CNNs), recurrent neural networks (RNNs), or transformers to model vocal patterns.

The same underlying AI that clones voices can also help identify them. In fact, AI can catch nuances that even the most trained human ear might miss, as shown in our article on how Pindrop^® tech detects deepfakes better than humans.

Statistical analysis

Detection often includes statistical methods to detect anomalies at the signal-processing level. For instance, certain spectral features might appear when speech is artificially generated.

Detailed analysis of background noise, pitch transitions, or even micro-pauses can give the system enough data to alert a voice as likely synthetic.

For more insight into this technology, explore Pindrop^® Pulse™ Tech, which offers a 99% accuracy rate and can detect deepfake audio in just two seconds, among other benefits.

The future of deepfake voice detection

Industry experts predict that deepfake technology will only become more realistic. According to a Gartner press release, 30% of enterprises may consider their identity verification solutions unreliable in isolation by 2026 because of deepfakes.

Several developments are on the horizon:

Cross-industry collaboration: Tech giants, financial institutions, and security vendors are forming alliances to share threat intelligence and best practices.
Integration with cybersecurity platforms: Expect more solutions that plug directly into existing cybersecurity frameworks, offering real-time alerts and automatic threat mitigation.
Emerging use cases: The political and social implications of deepfakes, such as fake campaign robocalls, are enormous. For instance, deepfake detection software uncovered manipulated videos of political figures, such as the deepfake of VP Kamala Harris and the Biden robocall.

For an in-depth analysis of how deepfake detection tools are evolving, see our pieces on:

Safeguard your organization with deepfake voice detection

As we have learned, enabling deepfake voice detection is no longer optional—especially for industries where large-scale financial transactions or sensitive data are handled over the phone.

Solutions like Pindrop® Pulse™ Tech use advanced machine learning to distinguish human voices from AI-generated audio.

Our article on Pindrop ^® Pulse for audio deepfake detection offers a closer look at how we can help you fight deepfake fraud.

Securing your business starts with acknowledging the growing threat of AI-powered voice impersonations and implementing robust detection measures.

If you’re looking for an immediate next step, get a demo of the future of voice security.

As security and user experience become more essential, businesses increasingly rely on Interactive Voice Response (IVR) and Intelligent Virtual Agent (IVA) technologies for caller authentication and self-service. Understanding the nuances of IVR authentication is critical when choosing between IVA and IVR solutions.

According to a Gartner study, 38% of Gen Z and millennial customers are likely to abandon interactions that can’t be resolved independently. However, only 14% of service issues are fully resolved via self-service, emphasizing the ongoing importance of phone channels for complex problems.

As a result, IVR and IVA systems are at the center of automated customer service, enabling seamless caller authentication and improved routing. Ensuring secure and efficient IVR authentication is crucial, particularly in highly regulated industries such as banking and finance and contact centers with high call volumes.

Understanding authentication, IVA, and IVR

Authenticating callers is a critical first step, opening the door to a personalized experience, self-service authentication, and customized routing opportunities.

A well-designed call flow, with thoughtful authentication options, can balance security with customer satisfaction, increase containment, and improve overall operational efficiencies.

The primary goal of any modern, robust self-service IVR/IVA platform is to identify and authenticate the caller as quickly as possible with as little friction as possible. If the caller can quickly and easily authenticate, they’re more likely to engage with the platform instead of requesting assistance from an agent.

Higher levels of trust and engagement also expand the types of self-service transactions offered through the platform. This kind of customer experience automation is specifically relevant for enterprises looking to handle large volumes of calls more efficiently and implement intelligent call routing measures.

What is an IVA system?

An IVA uses virtual agent technology, conversational AI, and NLP (Natural Language Processing) to understand natural speech and engage callers in a more human-like interaction.

Unlike traditional IVRs, which rely on strict menu options, IVAs can interpret open-ended questions, offer omnichannel customer support, and handle complex tasks with minimal agent transfer.

By using voice-based authentication methods with IVA systems, contact centers can reduce caller frustration, shorten resolution times, and improve overall security and compliance.

What is an IVR system?

An IVR system is a more traditional solution. It uses pre-recorded messages and keypad or simple voice prompts to guide callers through options. IVRs are well-suited for predictable, straightforward tasks, such as handling basic checks with PINs or simple KBA (knowledge-based authentication) methods.

IVRs are less flexible than IVAs and offer a limited experience for complex needs. However, they are helpful due to their established presence, lower investment, and fit for predictable call flows.

5 Key differences between IVA and IVR

1. Technology and capabilities

Technology is a major differentiator between IVR and IVA. IVAs can handle more nuanced calls by leveraging advanced Conversational AI, Voice User Interface (VUI), and NLP. They can recognize intent, respond contextually, and integrate with back-end systems.

While reliable, IVRs generally use static menus and are less adaptive. This difference affects how effectively each system can handle caller verification, fraud detection, and voice analysis.

2. User interaction

User interaction in IVAs often feels more natural. Callers can speak in their own words, and the IVA can understand and respond intelligently.

IVRs, on the other hand, follow predefined paths. This can sometimes lead to higher caller frustration if the required information isn’t readily available or the user’s request does not match the IVR’s menu structure.

3. Integration with other systems

IVAs can integrate seamlessly with CRMs, security databases, and voice assistant technology, creating opportunities for intelligent call routing and reducing manual intervention.

IVRs can also integrate but often require additional customization and may not handle complex scenarios as elegantly.

4. Scalability and future-proofing

IVAs can adapt and scale as new authentication methods emerge or existing methods evolve. For instance, if new voice-based authentication systems become available, an IVA can integrate these technologies more easily, keeping pace with changes in regulations, user expectations, and contact center threats. IVRs can be updated, but often at a slower pace.

5. Cost considerations

Although implementing an IVA may have higher initial costs, the return on investment can be significant through reductions in operational expenses, improved call deflection (transferring routine requests to self-service), and better authentication accuracy.

IVRs may be less expensive upfront, but the ongoing costs of maintaining legacy systems and addressing fraud risks can accumulate over time. If you are concerned about the bottom line, evaluating solutions like Pindrop® Passport or Pindrop® Protect can demonstrate how improved security reduces long-term costs.

Authentication methods in IVA and IVR systems

Choosing the appropriate authentication method is crucial as organizations must balance the contact center’s needs, compliance requirements, security standards, and customer experience preferences. Authentication methods available for self-service IVR/IVA applications include:

Knowledge-Based Authentication (KBA)
Passwords
Personal Identification Number (PIN)
One-Time Passcodes (OTP)
Biometrics
Multifactor Authentication (MFA)

Solutions like multifactor authentication can help your organization leverage an optimal mix of tools and strategies. These include optimizing IVR and agent productivity and identifying and mitigating authentication risks. Let’s learn more about these IVR/IVA authentication methods.

Knowledge-based authentication (KBA)

KBA questions are the most commonly used mechanism in traditional IVR and agent-based authentication. To identify and authenticate the caller, prompts for Social Security number, account number, member number, date of birth, or phone number might occur.

KBAs are commonly used because the caller is expected to know this information when calling the contact center. Unfortunately, fraudsters also know this information, as it is widely available across the dark web due to phishing, social engineering, and data breaches. Fraudsters understand the typical identity verification procedures financial institutions use and are equipped to answer them accurately.

Advantages

The use of KBA is a relatively low-cost and easy-to-implement method as it only requires the technology to validate the information provided by the caller.
Callers are expected to know the information and should be able to quickly provide it.

Disadvantages

This presents a significant security risk as the information may be easily accessible or guessed by fraudsters.
Information on file may be inaccurate or outdated, leading to caller frustration.
Question types are limited in an automated IVR system due to speech recognition limitations
The National Institute of Standards and Technology (NIST) now advises against using just personal questions as the only form of authentication.

Password and PIN authentication

Traditional alphanumeric identifiers and passwords work well for online and mobile applications. However, this method is not often employed in a traditional IVR/IVA application. Voice verification for passwords and PINs finds it difficult to correctly interpret a caller’s utterance due to the significant phonetic overlap in sounds.

Think “A,” “H,” and “eight,” “B,” “V,” and “D,” “P,” “C,” and “T”. Although this technology has come a long way, solutions for unconstrained alphanumeric sequences remain challenging.

Advantages

Most callers are familiar with creating and remembering simple passwords.
Password-based authentication is relatively low-cost and easy to implement.

Disadvantages

Secure passwords are complex, oftentimes unable to be spoken in recognizable words.
The increased frequency of data breaches forces consumers to change passwords regularly, making them difficult to remember.
A significant degree of phonetic overlap in sounds may impact speech recognition of passwords and increase callers’ frustration and friction when they speak their passwords character-by-character.
In DTMF-based applications (no speech recognition), password entry via the keypad is challenging, degrading the customer experience.

A PIN is a commonly used way to authenticate a caller in self-service IVR/IVAs, specifically within the financial vertical, as most accounts have an existing PIN for transactional purposes. This is implemented by simply prompting the caller to say or enter their 4 or 6-digit PIN. There are both positive and negative impacts to PIN-based authentication.

Advantages

PINs are more convenient than a traditional password.
PINs are typically short and easy to remember.
PINs can be more cost-effective than using other forms of authentication.

Disadvantages

PINs pose a significant security risk as they are typically short and weak, making them easier to guess or crack.
Using a PIN alone (single-factor authentication) is limited and may not provide sufficient security when allowing someone to gain full access to an account.
PINs are also subject to the same data breach risks as KBAs and passwords and are often sold on the dark web as a package deal for monetization by criminals.

One-time password (OTP) authentication

OTP has existed as an authentication mechanism for over 40 years. It is a hardware token that generates random codes for entry into a computer application. Over time, this evolved to sending a soft token to an email address on file.

With the explosion of mobile phones, SMS-based OTP quickly gained widespread use, as it required only phones and not hardware tokens. Again, the primary use case for either SMS-based or email-based OTP was digital experiences.

As businesses, particularly financial institutions, take action to modernize their IVR and self-service capabilities, it has become increasingly necessary to find more secure ways of verifying the identity of callers to allow them to transact.

OTP is sometimes offered as an option for callers to receive an SMS-based code and then provide it to the IVR/IVA application to service their call.

Advantages

Enhanced security as long as the OTP is only sent to registered mobile phone numbers or email addresses.
OTP provides a form of fraud mitigation as it is only valid for a single session, which can make it more challenging for fraudsters to gain unauthorized access using the same OTP.
Response to a numeric passcode is more effortless than providing a complex IVR/IVA application password.

Disadvantages

User experience is cumbersome as the method requires users to switch between their IVR call and their mobile phone or email application to retrieve the passcode, which could ultimately lead to low success rates and decreased caller engagement.
Security risks posed as hackers may access a caller’s email or mobile device and intercept the OTP.
The total cost of ownership can be expensive, primarily if the IVR/IVA handles significant call volumes, which could outweigh the tool’s overall cost savings.

Multifactor authentication (MFA)

MFA in IVR/IVA platforms requires users to provide multiple forms of identification before they are granted access to information or services. Typical MFA strategies involve:

Something the caller knows: this is often an account number, member ID, social security number, or PIN.
Something the caller has: this is typically a mobile device that must be present for the caller to confirm their identity.
Something the caller is: this refers to biometrics, which are typically voice-based in an IVR/IVA environment.

One way this may be implemented in an IVR application is to ask the caller to provide information (something they know), such as an account number. The next step in the process could be a mobile push or OTP to the mobile device on file for that account (something the caller has), and the final step might be to evaluate features of the caller’s voice as they provide their account number or OTP passcode (something the caller is). MFA can involve two or all three factors when authenticating a caller.

Advantages

By combining multiple forms of identification, MFA can provide a higher level of security than a single authentication method.
MFA can provide a more convenient and expedient authentication process when appropriately designed.
MFA can reduce the cost per call by decreasing its average handle time.

Disadvantages

If not appropriately designed, MFA could add friction to callers, negatively impacting the customer experience.
Implementing MFA often requires integrating additional hardware and software, which can increase the cost of servicing calls.

Voice Biometrics Authentication

Biometric authentication offers a secure way to authenticate individuals based on different characteristics. Commonly used biometric technologies include:

Facial recognition
Voice analysis
Fingerprint analysis
Iris scans
Behavioral biometrics

The use of biometric technology in IVR/IVA platforms is gradually evolving as organizations seek ways to improve security without compromising caller experience. Voice analysis is the most commonly implemented technology in self-service telephony applications that employ biometrics.

Advantages

Voice biometric authentication is easy to use. The caller doesn’t have to remember a complex password, carry a specific device, or speak a particular language.
Decreased vulnerability by providing a layer of security that is very difficult for unauthorized users to access and steal.
It can be more cost-effective because it reduces the costs associated with other authentication methods, such as agent and OTP-based authentication.

Disadvantages

Not all callers can use voice biometric authentication due to physical disabilities or medical conditions.
Not all contact centers have speech-based IVR/IVA applications
Some callers may be uncomfortable with voice analysis

Selecting the right IVA and IVR solution

Security and compliance considerations

Whether choosing IVA vs IVR, organizations must ensure the selected solution meets their industry’s regulations and compliance standards. The correct authentication strategy can reduce the risk of fraud, help protect sensitive information, and enhance overall contact center security.

User experience and customer satisfaction metrics

Enhancing customer satisfaction involves minimizing friction. The chosen method should not overly complicate the process, whether using KBA, MFA, OTP, or voice authentication. Easy authentication leads to higher containment rates, greater trust, and improved loyalty. Consider how user-focused solutions can strike the right balance.

Cost-effectiveness and ROI

Solutions that streamline customer identity verification and reduce fraud can eventually lead to substantial cost savings. Improved IVR authentication can reduce call transfers, agent involvement, and security breaches.

Moreover, advanced technologies that enable effective self-service reduce average handle times and associated expenses.

Make the right choice for your business

When designing a modern IVR/IVA authentication module, organizations must carefully assess each authentication method’s potential risks and benefits.

Balancing security, compliance, cost, and user experience is essential to help protect customer data, secure calls, and maintain high satisfaction levels.

Investing in a well-designed solution, whether it relies on IVR or IVA, can strengthen fraud detection, shorten resolution times, and improve overall outcomes.

To learn more about potential vulnerabilities, check out Pindrop’s IVR fraud detection and IVR containment solutions to enhance fraud mitigation strategies.

Pindrop® Protect provides instant risk assessments for calls to the IVR analyzing voice devices and behavior. Request a demo to learn more.

Guide

Strengthen Security + Trust in Your Healthcare Contact Center

Don’t let outdated authentication methods put your healthcare organization at risk. Explore how voice security protocols can help you detect fraud in your contact center before it escalates—without sacrificing the caller experience.

What’s in the guide?

How fraudsters exploit vulnerabilities of contact centers and the financial impact on healthcare organizations
How technologies like voice analysis, ANI validation, and behavioral analysis enhance fraud detection
How modern authentication methods can reduce fraud while making interactions faster and more seamless for patients

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Download the guide below

Healthcare contact centers face rising fraud risks, while outdated authentication methods are time-consuming for patients and not as secure. Download the guide to learn how AI-driven voice security solutions—such as voice-based authentication, ANI validation, and behavioral analysis—enhance fraud detection, streamline authentication, and can improve patient trust.

Click here to download the guide.

Can you distinguish between what’s real and what’s not in audio and video? With the rapid rise of deepfake attacks, this challenge is no longer theoretical—it’s a growing threat to businesses worldwide.

Fueled by advancements in artificial intelligence (AI), machine learning, and other emerging technologies, deepfakes have become increasingly accessible and cost-effective, amplifying their potential for misuse.

Voice-enabled AI agents, powered by widely accessible AI tools, can now perform common scams at scale. Meanwhile, worldwide adoption of voice-enabled AI agents is projected to reach USD 31.9 billion from 2024 to 2033.

The cost of deepfake attacks goes beyond immediate financial losses. These attacks can lead to brand erosion, compliance penalties, and recovery expenses.

Unsurprisingly, 90% of consumers have raised concerns about deepfake attacks, as revealed in our 2023 Deepfake and Voice Clone Consumer Report. Without proactive defenses, organizations are left vulnerable to these sophisticated fraud tactics.

In this article, we’ll explore:

The various types of deepfake attacks and why they’re rising
The direct and indirect financial consequences for organizations
Corporate vulnerabilities and practical strategies to mitigate risks
How Pindrop^® Pulse Tech offers advanced solutions to combat these threats

Types of deepfake attacks

Deepfake technology generates hyper-realistic synthetic media that mimics individuals and fabricates misleading situations. These attacks manifest in multiple forms, targeting specific organizational vulnerabilities. The main types include:

Audio deepfakes

Scammers use AI-generated audio to replicate a person’s voice, often impersonating executives, public figures, and others convincingly.

These deepfakes can manipulate phone-based authentication systems, posing significant risks to contact centers in industries like insurance or financial institutions.

Video deepfakes

AI-generated videos manipulate or alter facial expressions, actions, and more to mimic individuals and create fabricated scenarios.

In insurance and financial institutions, these videos can deceive stakeholders or employees by portraying false statements or actions, which can lead to reputational damage and a breakdown of organizational trust.

Synthetic identity creation

Combining AI-generated visuals and voices, attackers create entirely fabricated identities to bypass traditional security checks. This method is increasingly used in financial scams and other fraudulent activities, making detection more challenging.

Rising concerns about deepfake attacks

As mentioned, the proliferation of AI systems and open-source tools has made deepfakes easier and cheaper. Fraudsters can now generate convincing synthetic media quickly.

This growing accessibility raises significant concerns about AI safety and the preparedness of organizations to handle these evolving threats.

Notable deepfake incidents involving organizations in 2024:

The $25 million deepfake scam at Arup. Cybercriminals used AI-generated deepfakes to impersonate Arup’s CFO and other employees during a video conference, convincing a staff member to transfer $25 million to Hong Kong bank accounts.
The WPP CEO impersonation attempt. Fraudsters targeted WPP, the world’s largest advertising group, by creating a deepfake voice clone and fake WhatsApp account to impersonate CEO Mark Read. The attack involved using YouTube footage to deceive employees during a virtual meeting.
The YouTube cryptocurrency scams. Scammers orchestrated “Double Your Crypto” frauds using AI-generated deepfakes of public figures like Elon Musk, Ripple’s CEO Brad Garlinghouse, and Michael J. Saylo. They hijacked YouTube accounts to promote fake Bitcoin giveaways, stealing over $600,000 from victims.

Recent findings highlight the rise in deepfake scams:

Research revealed that deepfake-powered scams targeted 53% of businesses, and 43% of those fell victim to the attacks.
In this survey of 1,533 U.S. and U.K. finance professionals, 85% viewed deepfake scams as an existential threat to their organization’s financial security. However, only 40% of professionals surveyed said protecting the business from deepfakes is a top priority.
Additionally, an early study showed that we are only 53.7% accurate in identifying deepfake audio, and this accuracy is expected to decline as AI technology advances.

Direct financial losses from deepfake attacks

The financial repercussions of deepfake attacks can be devastating. In a single orchestrated attack, businesses can lose millions of dollars.

Fraudulent transactions

Deepfake scams often manipulate financial processes, enabling attackers to complete unauthorized payments or withdrawals. For example, synthetic audio may impersonate a company executive, instructing finance teams to transfer funds to fraudulent accounts.

The scale of potential losses is alarming. A recent Deloitte Center for Financial Services report estimates that fraud losses enabled by generative AI could reach $40 billion in the U.S. by 2027, highlighting the critical need for enhanced fraud detection measures.

Identity theft

Deepfakes also facilitate identity theft by enabling fraudsters to mimic trusted voices, granting them access to confidential accounts and sensitive information. These attacks often target financial institutions, where stolen identities can lead to:

Access to restricted accounts: Fraudsters use deepfake technology to bypass security checks, posing as customers or employees to exploit banking systems.
Siphoning of financial assets: Once access is granted, attackers can withdraw funds, compromise assets, or initiate fraudulent transactions.

Indirect financial losses from deepfake attacks

Brand and reputation damage

The aftermath of a deepfake attack can erode public trust in an organization. Companies that fail to prevent or address these attacks risk losing consumer confidence and long-term profitability.

Legal and compliance costs

If deepfake fraud leads to data breaches or financial losses, organizations may face regulatory scrutiny or lawsuits. Non-compliance with evolving security standards can result in significant penalties.

Mitigation and recovery expenses

Post-attack recovery often involves forensic analysis, rebuilding security protocols, and investing in employee training—costly endeavors that strain resources.

Corporate and organizational impacts of a deepfake attack

Internal security breaches

Deepfake attacks often exploit vulnerabilities in an organization’s internal systems, bypassing traditional security measures like password-based authentication. For example:

A deepfake-generated audio clip of a CEO might direct an employee to transfer funds, bypassing internal verification protocols.
Impersonation of employees through synthetic voices can facilitate unauthorized access to sensitive systems, disrupting operations and compromising data integrity.

Once trust within internal communications is undermined, organizations may face a cascading effect of security breaches, requiring significant time and resources to restore confidence and operational normalcy.

Employee training and preparedness

Employees are often the first line of defense against deepfake fraud, but most are unprepared to recognize the sophisticated tactics used in these attacks. Without adequate training, employees may:

Fall victim to deepfake-generated impersonations during phone or video interactions.
Share sensitive information or grant unauthorized access based on fabricated directives.

Organizations must invest in ongoing training programs to:

Educate staff on identifying red flags in voice and video communications.
Enhance awareness of the potential risks posed by deepfakes and other AI-enabled threats.

This proactive approach minimizes vulnerabilities and fosters a culture of vigilance.

Loss of intellectual property

Deepfake technology is also being used to target intellectual property (IP). Fraudsters may exploit synthetic media to:

Coerce employees into revealing proprietary data or trade secrets.
Fabricate internal communications to gain access to sensitive R&D projects.

The theft or misuse of IP can have long-term consequences, including the loss of competitive advantages, decreased market share, and reputational harm.

How to protect against deepfake attacks

Technological solutions

Adopting advanced tools and technologies is critical to effectively detecting and mitigating deepfake threats. Key solutions include:

Liveness detection: This technology analyzes subtle human characteristics in voice or video interactions to identify synthetic content. Pindrop^®deepfake detection technology, for instance, verifies that a voice is human, not machine, helping to ensure reliable customer interactions.
Voice biometrics analysis: By analyzing vocal features, such as pitch, tone, and rhythm, voice biometrics analysis can identify anomalies indicative of a deepfake. With tools like Deep Voice^Ⓡ tech, you can enhance caller authentication with a neural network-based biometric analysis engine.
Deepfake fraud detection software: AI-powered tools analyze audio and video for signs of manipulation, flagging potential deepfake threats in real-time. Pindrop® Pulse Tech enhances fraud detection and authentication with cutting-edge audio deepfake detection, offering industry-leading accuracy and seamless integration for real-time detection in contact center environments.
Multifactor Authentication (MFA): Combining voice authentication with additional factors like PINs, one-time passwords, or facial recognition adds an extra layer of security. MFA helps ensure that even if one security layer is compromised, others remain intact. Pindrop® multifactor authentication solution helps contact centers authenticate legitimate callers quickly and accurately.

Deepfake compliance measures

Organizations must establish internal regulatory processes to manage the risks associated with deepfakes while adhering to emerging external regulations. Internally, companies should develop clear guidelines for creating, disseminating, and detecting AI-generated content. This includes:

Internal auditing processes: Regular reviews of AI tools and systems to ensure compliance with ethical standards and industry best practices.
Awareness training: Involves educating employees on identifying and managing deepfake risks, particularly for teams handling sensitive communications or transactions.

Externally, organizations should stay informed about evolving laws and proposed regulations.

Defend against deepfake attacks with Pindrop^® Pulse Tech

With Pindrop® Pulse Tech and Pindrop® Pulse Inspect technology, your organization can leverage innovative solutions to tackle the increasing issue of deepfakes in real time.

With features like liveness detection, real-time monitoring, and many more innovative solutions, you can help safeguard your organization against synthetic fraud.

These tools can assist you in preserving trust and operational integrity while reducing the financial impact of deepfake attacks. Request a complimentary deepfake demo today.

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The webinar details have been sent to your email address. We look forward to seeing you there! Ready to see how Pindrop® Pulse is at the forefront of the battle against deepfake technology?

Watch our recent coverage on CNN’s Anderson Cooper 360°.

In the meantime, please check out our resources around authentication and deepfakes:

Pindrop Deepfake Site – Find resources around deepfake detection and prevention, plus request a demo of our liveness detection solution.
2023 Deepfake and Voice Clone Consumer Report – Dive into Pindrop and Voicebot.ai’s research around consumer sentiment of deepfakes and voice clones.
Deepfake Attacks in 2024: What You Need to Know – Read up on how deepfakes are expected to evolve in 2024.

If you have any questions, please reach out to [email protected].

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Advances in artificial intelligence (AI) have changed how we interact with technology, but they have also opened new avenues for fraud.

For instance, phone scams, which are already problematic in the U.S. and globally, have advanced into a more sophisticated threat aided by AI, targeting individuals and businesses with greater precision.

In this article, we’ll explore how and why voice-enabled AI agents are used to perform common scams, why this presents a growing concern, and what steps you can take to protect yourself and your organization.

Why are voice-enabled AI scams a growing concern today?

The most significant risk posed by voice-enabled AI is how effectively these attacks can scale. Previously, fraudsters would collaborate to form fraud rings targeting several banks or other institutions simultaneously. However, the availability of advanced AI tools alongside realistic voice cloning technology has shifted their strategies.

An individual fraudster can now orchestrate attacks of equal or greater magnitude using a generative AI toolkit. This can involve:

Creating multiple synthetic (artificially generated) voices to interact with targets.
Training an AI model to have automatic and simultaneous conversations with a target, such as a contact center agent.
Calling and socially engineering multiple organizations simultaneously.
Avoiding detection by voice recognition systems, as synthetic voices mimic natural human inflection.

The fraudster does not have to use their authentic voice. The low cost and ease of access to these tools make such attacks highly accessible. While these methods are imperfect, advancements in open-source AI tools suggest that large-scale, targeted attacks are highly likely.

Common phone scams explained

In the context of phone scammers targeting companies and contact centers, there are a few common examples of how they active this with AI:

Synthetic account reconnaissance: A fraudster utilizes a synthetic (artificially created) voice to collect account details and maneuver through a company’s interactive voice response (IVR) system. After obtaining the target’s account information, the fraudster contacts the contact center agent, pretending to be the victim, to take control of the victim’s account.
Using synthetic voice for authentication: The fraudster uses machine-generated voice to circumvent IVR authentication for selected accounts. They correctly respond to security questions and provide one-time passwords (OTP). The individuals orchestrating the attack follow up to carry out the fraud with the contact center agent.
OTP phishing: The fraudster initiates multiple calls with a synthetic voice to instruct a call contact agent to alter the victim’s information, such as their email or mailing address. Once this change is made, the fraudster can receive the OTP or request a new card sent to their address.
Voice spoofing or impersonation: The fraudster develops and trains a voice bot to replicate the intended target’s voice, including that of an organization’s IVA agent. The voice bot collects internal information from organizations, including employee details, allowing it to evade fraud detection methods.

How voice-enabled agents perform common scams

Agent design and architecture

AI agents utilize advanced Text-to-Speech (TTS) tools to replicate realistic human voices. With access to public speech samples via social media or the dark web, fraudsters train these tools to mimic a person’s vocal nuances. The result? AI-generated voices that sound indistinguishable from their real counterparts.

Customer impersonation

Fraudsters impersonate individuals using stolen personal information such as names, addresses, phone numbers, and account details. Over 300 million records were compromised in 2023 alone, and this information is readily available on the dark web. Combined with TTS tools, fraudsters craft compelling synthetic voices and scenarios.

Ability to answer complex questions

AI models trained on stolen data can carry believable conversations, especially when combined with realistic-sounding synthetic voices. These agents can:

Navigate complex queries.
Provide convincing answers based on the victim’s leaked information.
Adapt to real-time conversational changes, making detection increasingly tricky.

The other side of the equation is humans’ limit to distinguishing between AI and human voices. Studies show we are only 54% accurate in identifying deepfake audio, and this number is expected to decline as AI technology advances.

The dangers of voice-enabled agents in authentication

Identity verification and authentication solutions have been considered effective and secure for a long time, but that is changing. According to Gartner, by 2026, 30% of enterprises will consider identity verification solutions unreliable due to the rise of AI-generated deepfakes. But why is this happening?

One reason is the rapid advancement in digital injection attacks, where AI-generated deepfakes bypass current standards for presentation attack detection (PAD). While PAD mechanisms in face biometrics assess a user’s liveness, these systems are not equipped to handle digitally injected synthetic media, making them vulnerable to AI-powered deception.

Voice biometrics authentication systems also face limitations in detecting synthetic voices. While they can identify some threats and have some protection, which is better than none, they are insufficient as a standalone solution. However, combining voice analysis with liveness detection and other authentication factors drastically improves reliability.

According to Pindrop’s response to the University of Waterloo study, this combined approach increases detection accuracy to an unmatched 99.2%, even against sophisticated signal-modified deepfakes. Pindrop’s Liveness Detection technology outperformed leading benchmarks, demonstrating superior performance against adversarially modified spoofed utterances.

This exceptional accuracy highlights the importance of leveraging multi-layered solutions to mitigate the growing risks posed by voice-enabled agents in authentication processes.

How to protect yourself from common phone scams

Protecting yourself and your organization requires a multi-layered approach. Combining advanced technologies with awareness and best practices can significantly reduce the risk of falling victim to these scams. Here’s how:

Caller authentication

Modern authentication systems can identify anomalies indicating synthetic voices by analyzing vocal features alongside other data points.

Deepfake fraud detection software

Advanced deepfake detection software for call centers is a vital component in combating AI voice scams. These tools analyze subtle features in audio recordings to identify signs of synthetic generation, such as:

Inconsistent tone or pitch.
Artifacts from audio processing.
Content-agnostic patterns that differ from natural human speech.
Real-time alerting for live risk scoring of every call.

Multifactor authentication (MFA)

MFA provides a layer of security by requiring users to verify their identity through multiple independent factors. These include:

Something you know: Security questions or PINs.
Something you have: A one-time password (OTP) sent to a mobile device or email.
Something you are: A fingerprint, face recognition, or voice analysis.

MFA is designed to ensure that additional barriers protect sensitive accounts and systems even if one layer is compromised. For example, with the Five9 + Pindrop® integration, businesses can streamline MFA and fraud detection processes. This integration enables quick and secure authentication of inbound calls, enhances automation in IVAs, and detects fraudulent activity in real-time, making it an invaluable tool for safeguarding customer interactions.

Protect your business from common scams with Pindrop^Ⓡ Solutions

Voice-enabled AI scams are an evolving threat, but with innovative Pindrop^Ⓡ solutions, you are better positioned to stay ahead. Pindrop® Pulse and Pindrop® Pulse Inspect leverage cutting-edge liveness detection software to analyze vocal features unique to humans, effectively identifying synthetic voices and mitigating the risks of AI-generated fraud.

Pindrop® Pulse Tech: Specifically intended for contact centers, this liveness detection solution enhances security and must be paired with Pindrop® Passport or Pindrop® Protect to deliver multifactor authentication and fraud detection capabilities.
Pindrop® Pulse Inspect: A standalone liveness detection solution tailored for media companies to determine if audio is synthetic or human, helping you restore integrity before distribution.
Pindrop® Passport provides comprehensive multifactor authentication by combining voice analysis with additional layers of security to support verification of user identities.
Pindrop® Passport provides comprehensive multifactor authentication by combining voice analysis with additional layers of security to support verification of user identities.

With these tools, Pindrop Solutions offer a robust defense against AI-driven scams, empowering businesses to better protect their operations and maintain customer trust. Ready to experience the difference? Request a demo today.

Voice analysis is the science of using an individual’s vocal characteristics to verify them. This feature is increasingly used to authenticate individuals in virtual and physical spaces. Maintaining a level of security is not always easy, and access control is often the first and most important step.

In healthcare, a sector that relies on vast amounts of highly sensitive data, traditional security methods like passwords or PINs are often insufficient, as they are easy targets for hackers. Voice biometrics authentication provides a possible answer to this issue, helping to enhance patient privacy by providing a more secure, convenient, and user-friendly way to verify a person.

The urgency of data security in healthcare

In the US, over 80% of hospitals and 90% of office-based practices use electronic health records (EHRs). This often results in better, centralized healthcare records and easy access for healthcare professionals and patients. However, it also means increased security risks, as EHRs can be vulnerable to cyberattacks without adequate protection.

Any breach of this data can lead to devastating consequences for patients, from identity theft to the misuse of medical information. With the increasing digitalization of healthcare services, including patient portals and telemedicine, the threat of data breaches continues to grow.

According to industry reports, healthcare organizations are frequently targeted by hackers because of the high value of medical data. For instance, in 2023, the number of reported ransomware attacks on healthcare nearly doubled, with 389 victims globally, up from 214 in 2022.

In the United States alone, these attacks increased by 128%, affecting 258 healthcare entities. In this climate, healthcare providers must implement strong authentication systems to safeguard patient data.

Voice biometrics authentication as a more secure solution for patient privacy

Voice authentication analyzes content-agnostic voice audio, and then uses this to create a numerical representation of a voice profile. Each time a user calls, the software compares the caller’s voice profile against the one previously enrolled to generate an authentication score.

Unlike passwords or security questions that are easy to guess or steal, a person’s voice is challenging to replicate. Voice biometric authentication systems use advanced voice analysis to verify users based on their vocal characteristics and features, such as pitch, tone, and speech patterns, making it extremely difficult for fraudsters to impersonate someone.

Benefits of voice biometrics authentication

Voice biometric authentication offers many benefits, making it an ideal choice for healthcare providers seeking to enhance data security while improving patient experience.

Reduce the risk of identity theft

Identity theft is one of the biggest worries with EHRs.

Replay attacks also pose a risk, but many voice biometrics authentication systems have mechanisms in place to protect against them. That’s why this form of authentication provides stronger protection against unauthorized access.

Take control of your medical data

Voice biometrics authentication can give patients greater control over their medical data. With healthcare providers who use it, patients can more easily and securely access their information. This reduces the need for passwords or knowledge-based authentication (KBA) that can easily become compromised.

Access your patient portal with ease

With voice authentication, accessing patient portals becomes seamless. Instead of remembering complex passwords or undergoing lengthy verification processes, patients can simply use their voice to log in. This improves the overall user experience while maintaining higher levels of security.

Experience frictionless telemedicine

Despite the ease of access and the benefits for those with physical disabilities or weak immune systems, telemedicine comes with unique security challenges.

Voice biometrics authentication provides an efficient authentication option for telemedicine sessions, helping ensure the right patient receives the care they need. This helps both patients and healthcare providers feel more confident that interactions are secure.

Strengthen access control

In environments that rely on sensitive patient information, such as hospitals or contact centers, voice biometrics authentication can improve access control by adding an extra layer of security. Each person, whether a patient or a healthcare provider, can use voice authentication, to better ensure that only authorized personnel can handle sensitive information.

Reduce administrative burden

By streamlining the authentication process, voice biometrics authentication can reduce the administrative burden on healthcare providers. Contact center agents or other staff members no longer need to use lengthy and often insecure means, such as asking security questions, to verify callers. This not only saves time but also creates a more accurate verification process.

How voice biometrics authentication can help you protect patient information

Voice biometrics authentication can provide increased security protection for patient information in several ways, from stronger verification to improved accuracy and security in healthcare delivery.

Multifactor authentication

Voice biometrics authentication is often part of a multifactor authentication system, where a voice is used alongside other verification forms, such as a password or a fingerprint.

This combination increases security by adding an extra layer of protection, making it more difficult for malicious actors to gain access.

Reduced risk of errors

Mitigates human error

Human error is one of the leading causes of data breaches in healthcare. By automating the authentication process with voice biometrics authentication, healthcare organizations can reduce the likelihood of mistakes, such as incorrect data entry or verification errors made by agents.

Improved accuracy in telemedicine

In telemedicine, accurate patient identification is critical for providing appropriate care. Voice biometrics authentication helps confirm that the authorized patient is receiving care, reducing the risk of mix-ups that could lead to misdiagnosis or inappropriate treatment.

Enhanced security measures

Real-time authentication

Some voice biometric systems authenticate users based on voice patterns and use liveness detection to help ensure that the voice belongs to a human and not a recording or synthetic imitation.

This added layer of protection helps prevent sophisticated fraud attempts, making it even harder for attackers to spoof or manipulate the system using pre-recorded audio or deepfake technologies.

Continuous monitoring

In addition to one-time verification, voice biometrics authentication systems can continuously analyze a user’s voice throughout a session, adding a layer of security by detecting any changes that might indicate potential fraud or unauthorized access.

Positive impact on overall patient safety

Reduced risk of identity theft

Voice biometrics authentication provides stronger verification, helping to reduce the risk of identity theft in healthcare and to protect patients from the potential financial and medical repercussions of stolen personal information.

Improved medication safety

When you integrate voice biometrics authentication into patient management systems, you help ensure that each patient receives the correct medication. This can prevent errors that could occur when manually verifying the person’s identity.

Enhanced telemedicine security

Voice biometrics authentication can improve security in telemedicine by providing real-time authentication for all communications, helping protect patients and healthcare providers from fraud and data breaches.

Addressing concerns about voice biometrics authentication

While voice biometrics authentication provides many security benefits, there are concerns about this technology’s potential risks and limitations. For instance, a 2023 survey revealed that 85% of consumers had concerns about using biometric technology.

This was largely because of the rise of generative AI and the possibility that companies will misuse their data. Some users also worry about background noise affecting the accuracy of voice verification.

These aren’t small concerns, but voice biometrics authentication technology has addressed many of them recently. For instance, modern systems incorporate liveness detection, which can differentiate between a human voice and a machine-generated or recorded one. Advanced algorithms can filter background noise so that the system works with high accuracy even in less-than-ideal conditions.

Another concern many organizations have is the high costs of biometric authentication. Plus, to work correctly, this technology will often require advanced technology and highly specialized personnel.

Using a solution, such as Pindrop® Passport, that offers a secure platform for voice authentication and helps assess risk on every call while offering a user-friendly interface can reduce the risk on healthcare organizations.

This approach minimizes the need for various specialized software, offering a simple yet comprehensive solution. The transition may feel like a significant investment at first, but it can help you save costs in the long run.

Better protect your patient’s privacy

With more of our healthcare data online and the rise of telemedicine, we need to prioritize securing patient data. Voice biometrics authentication provides a robust and user-friendly way to protect patient privacy, reduce identity theft risk, and improve the overall customer experience.

Solutions like Pindrop® Passport offer healthcare providers and contact centers a secure, convenient means of authenticating patients while addressing concerns associated with voice analytics use.

With voice biometrics authentication technology, healthcare organizations can enhance security, and improve patient safety, all while offering a more seamless, convenient experience for patients.

Pindrop is recognized for demonstrating exceptional innovation, collaboration, and impact through our software solutions, seamlessly integrating with Five9’s platform to drive value for customers in 2024.

ATLANTA, GA, February 12, 2025 – Pindrop Security Inc. received the 2024 ISV Partner of the Year award by Five9, the provider of the Intelligent CX Platform. The ISV Partner of the Year award celebrates a partner that has demonstrated exceptional innovation, collaboration, and impact through their software solutions, seamlessly integrating with our platform to drive value for customers.

For the past four years, Pindrop has partnered with Five9 to authenticate inbound callers to customer contact centers quickly and efficiently while helping to catch and stop fraud attacks. As AI and deepfake technologies advance, there has been a decline in consumer trust regarding protection from fraud. Businesses need to have robust security measures in place like multi-factor authentication, advanced identity verification and real-time liveness detection to safeguard their organization and customer data.

“Pindrop’s partnership and pre-built integration with Five9 not only allows us to intervene when a fraud attack occurs but also helps Five9 customers adapt to new challenges and maintain consumer trust.” ~ Sumant Mauskar, SVP of Sales and Global Partners, Pindrop

“We believe customer experience is the driving force behind business success and our partner ecosystem is central to this mission,” said Jake Butterbaugh, SVP International Sales and Global Partners, Five9. “Together, we empower enterprises to deliver outstanding CX by embracing innovation and a shared vision for success. Our collaborative approach accelerates cloud transformations, ensures seamless deployments, and provides deeper insights for our customers. It’s an honor to celebrate our Global Partner Award winners, who exemplify the excellence and values driving exceptional customer experiences.”

To learn more about how Pindrop and Five9 can provide you with a safer, more secure contact center experience, visit us here.

# # #

About Pindrop:

Pindrop’s solutions are leading the way to the future of voice by establishing the standard for identity, security, and trust for every voice interaction. Pindrop’s solutions protect some of the world’s biggest banks, insurers, and retailers using patented technology that extracts intelligence from every call and voice encountered. Pindrop solutions help detect fraudsters and authenticate genuine customers, reducing fraud and operational costs while improving customer experience and protecting brand reputation. Pindrop, a privately held company headquartered in Atlanta, GA, was founded in 2011 by Dr. Vijay Balasubramaniyan, Dr. Paul Judge, and Dr. Mustaque Ahamad and is venture-backed by Andreessen Horowitz, Citi Ventures, Felicis Ventures, CapitalG, GV, IVP, and Vitruvian Partners. For more information, please visit pindrop.com.

About Five9

Five9 empowers organizations to create hyper-personalized and effortless AI-driven customer experiences that deliver better business outcomes. Powered by Five9 Genius AI and our people, the Five9 Intelligent CX Platform is trusted by 3,000+ customers and 1,400+ partners globally. The New CX starts here and it’s at the heart of every winning experience. For more information, visit www.five9.com.

WATCH THE WEBINAR

2025 Trends: AI, Deepfakes + The Future of Authentication

As AI adoption accelerates, new fraud threats are reshaping the security landscape. From the rise of data breaches to the growing impact of deepfake fraud, organizations must adapt to stay ahead. In this webinar, Pindrop’s security experts will explore key trends around fraud prevention, authentication, and deepfakes, followed by strategies to protect your business and customers.

The top 2025 emerging security trends, including the rise of agentic AI, data breaches and deepfakes

The state of fraud prevention, authentication and deepfake detection in a new era of GenAI

Five steps you can take to ensure your contact center is secure and future-proofed

Your expert panel

Tara Garnett

Sr. Product Manager, Authentication Products

Dr. Payas Gupta

Director, Fraud Research

2025 Trends: AI, Deepfakes & The Future of Authentication

While deepfake technology may have legitimate applications in media and entertainment, its misuse poses significant risks for organizations.

AI-generated manipulations, known as deepfakes, can produce convincingly realistic audio and video, leading to significant threats such as financial fraud, identity theft, and the dissemination of false information.

Identifying and addressing these threats is essential for companies—but where can we even start?

Deepfake audits provide a structured and proactive approach to combating these risks. Businesses can protect themselves by identifying vulnerabilities, evaluating the impact of deep learning algorithms, and integrating robust detection tools.

This article explores the importance, components, and actionable steps for effectively implementing deepfake audits. Let’s dive in.

Understanding deepfake technology: How deepfake algorithms work

Deepfake employs machine learning (ML) and artificial intelligence (AI) to produce hyper-realistic synthetic media that can mimic human audio, video, or both.

This technology relies on advanced algorithms, such as Generative Adversarial Networks (GANs), which enable deepfake systems to learn and replicate intricate details of human behavior, such as speech patterns, facial expressions, and movements.

Key components of deepfake algorithms:

Training data: Large audio or video recording datasets train AI models. The more data available, the more realistic the deepfake becomes. The higher the quality and diversity of the data, the more accurate and convincing the resulting deepfake becomes.
Neural networks, including GANs, analyze and recreate speech patterns, facial movements, and other markers. They function through a generator that creates synthetic content and a discriminator that evaluates its authenticity.

This iterative process refines the output until the generated content is nearly indistinguishable from real media.
Synthetic output: Once trained, the algorithm produces manipulated media to deceive viewers or listeners. For audio deepfakes, the system recreates speech with seamless intonation and fluidity, often bypassing human detection. Video deepfakes involve synchronized lip movements, realistic facial expressions, and body language that align with the audio.

Benefits of conducting deepfake audits

Deepfake technology has progressed significantly, reducing telltale signs of manipulation, such as robotic inflections or visual artifacts. However, these advancements make detection increasingly challenging, even for trained professionals. This is why conducting audits is crucial.

Prevention of financial fraud

Deepfake audits help organizations detect and mitigate fraudulent activities before they escalate. By identifying synthetic audio or video used to impersonate executives, employees, or customers, audits can:

Prevent unauthorized financial transactions initiated through voice phishing or deepfake impersonations.
Safeguard sensitive financial information from being exploited by attackers.

Proactive approach for reviewing security

Conducting deepfake audits allows organizations to adopt a proactive security strategy. Regular audits help:

Identify gaps in current security frameworks, especially in systems reliant on video or voice authentication
Test the effectiveness of detection tools and protocols against emerging deepfake threats
Build resilience by ensuring that new AI-driven risks are addressed promptly

Protection of organizational reputation

Deepfake attacks can severely damage a company’s brand and stakeholder trust. For example, a deepfake video of an executive or product announcement could mislead stakeholders and harm the company’s credibility. Audits minimize reputational risks by:

Flagging manipulative synthetic media before it spreads widely
Ensuring that incidents are managed quickly and effectively to maintain customer confidence

Components of a deepfake audit

Identifying deepfake content

The foundation of any deepfake audit is the ability to detect synthetic media. Identifying deepfake content involves:

Content analysis: Use advanced detection tools to catch signs of manipulation. Look for inconsistencies in tone, pitch, background noise, or visual distortions, such as unnatural transitions or mismatched lip movements. For example, an AI-generated audio clip might have subtle variations in vocal intonation or background ambiance that don’t align with authentic recordings.
Tool-based detection: Technologies like liveness detection and voice biometrics are essential. For instance, Pindrop® Pulse Tech excels at analyzing audio patterns to identify anomalies that indicate deepfake attacks. In one of many cases, we flagged suspicious patterns in contact center interactions, exposing fraudulent attempts early. Learn how we did it with our article about identifying patterns of deepfake attacks in call centers.
Manual review: While automated tools are essential, having trained experts to review flagged content ensures accuracy. These professionals can validate findings and provide nuanced insights that technology might miss.

Evaluating the impact of deepfakes

Once deepfake content is identified, assessing its potential impact on the organization is vital. This involves:

Risk assessment: Determine the level of harm the deepfake could cause. Consider the following:
- Could it lead to financial fraud or unauthorized transactions?
- Does it have the potential to damage the organization’s reputation?
- Could it erode trust among customers or stakeholders?
Operational impact: Evaluate how the deepfake could disrupt business operations, such as impersonating executives or compromising internal communications.
Compliance Risks: Assess whether the deepfake could lead to regulatory violations, especially involving financial data or personally identifiable information (PII). For example, a deepfake impersonating a CEO to authorize a fraudulent wire transfer could violate financial reporting and not comply with regulations. They can also steal sensitive information, which violates privacy regulations.

By using multifactor authentication, stores can drastically reduce fraudulent return attempts. This process also minimizes disruptions for genuine customers, maintaining a smooth and efficient return experience.

Assessing the reach and spread of deepfake content

Understanding the dissemination and reach of deepfake content is crucial for containment and mitigation. Key steps include:

Content tracking: Use digital tools to monitor the spread of deepfake content across platforms. Tools like media monitoring software can flag where the content has been shared or reposted.
Audience analysis: Identify the demographic or groups exposed to the deepfake. This helps prioritize mitigation efforts and communication strategies.
Impact quantification: Estimate the scale of the damage based on the spread. For instance:
- How many individuals or entities might have been misled?
- Are there public relations implications, such as media coverage or social media backlash?

Best practices for conducting deepfake audits

Developing a deepfake detection framework

A well-structured framework is essential for identifying and addressing deepfake threats. Key elements include:

Establish clear protocols: Define processes for analyzing and flagging potential deepfake content. This includes:
- Identifying high-risk areas such as financial transactions or executive communications.
- Creating escalation procedures or ticketing systems for suspected deepfakes.
Integrate detection at multiple levels: Ensure deepfake detection is embedded into every stage of the organization’s workflow, from initial customer interactions to high-level decision-making.
Set metrics for evaluation: Measure the effectiveness of detection methods by tracking metrics like false positive rates, detection speed, and the number of confirmed deepfake cases.
Simulate scenarios: Conduct regular simulations of deepfake attacks to evaluate the framework’s robustness and train employees on appropriate responses.

Collaborating with experts in AI and cybersecurity

Deepfake threats require specialized knowledge. Collaborating with experts ensures organizations have access to the latest technologies and insights.

You can begin by collaborating with or subscribing to academic institutions or private companies focusing on AI, deep learning, and machine learning. This will allow you to stay updated on the latest deepfake techniques.

Cybersecurity firms can also be a good option to strengthen your organization’s defenses. You can also join industry groups and forums to share knowledge about deepfake mitigation. These platforms provide valuable insights and foster innovation in combating deepfake fraud.

Leverage vendor expertise to gain the knowledge and resources for deepfake detection. They can help you evaluate your defense strategy against deepfakes and provide the tools needed for the job.

Implementing deepfake detection tools

Unsurprisingly, investing in advanced tools is critical to defending against deepfakes. It’s essentially technology vs. technology—and having the right tools makes all the difference.

When evaluating deepfake detection solutions, look for these key features to promote comprehensive protection:

Real-time detection: The ability to identify synthetic media as it’s being used, minimizing the window of opportunity for attackers.
Continuous assessment: Ongoing evaluation and improvement of detection algorithms to keep pace with advancing threats.
Resilience: Tools that adapt to new attack vectors, ensuring robust defense against evolving deepfake tactics.
Zero-day attack coverage: Early detection of novel threats, even those not previously encountered, to prevent breaches.
Explainability: Insights into how and why a piece of content is flagged as a deepfake, enabling clear communication of risks to stakeholders.

Pindrop offers cutting-edge solutions tailored for real-time deepfake detection, seamlessly integrating into existing security frameworks. With Pindrop® Pulse, organizations can:

Analyze audio for manipulation using advanced voice analysis and AI-driven algorithms.
Detect and block synthetic media in real time, preserving business continuity and protecting sensitive data.
Integrate with current security systems, enhancing the overall fraud prevention strategy without overhauling existing workflows.

Pindrop® Solutions help safeguard call centers in various industries such as financial institutions, retail, and more by proactively identifying deepfake content before it can cause harm.

Be proactive with your business’s security with Pindrop Solutions

As deepfake technology continues to evolve, so must your defenses. Proactive measures are key to protecting your organization from synthetic media’s financial, operational, and reputational risks.

Pindrop® Solutions empower businesses like yours to stay ahead of these threats by providing real-time detection, continuous improvement, and seamless integration into existing systems.

Take the next step in safeguarding your organization—schedule a free demo today.

Returns are a standard part of retail, but they’re not without risks. Fraudulent returns can cost businesses a significant amount of losses annually. While restricting returns might seem like the only way to fight against retail fraud, there are better ways to help reduce fraud losses that don’t sacrifice the customer experience.

Leveraging an advanced voice biometrics analysis solution can help protect customer accounts, spot fraudulent returns, and streamline the call experience. This article will explore the types of return fraud and how to combat it with advanced voice security.

Understanding return fraud

Return fraud involves customers exploiting return policies for personal gain. It comes in various forms, from returning stolen items to abusing liberal return policies.

According to the National Retail Federation, return fraud costs billions annually and contributes to operational inefficiencies. Retailers often face challenges balancing customer satisfaction with fraud detection.

The most common types of fraud in retail include:

Receipt fraud: Customers use fake receipts or receipts from other items to return merchandise
Wardrobing: Buying an item, using it briefly, and returning it as “new”
Stolen goods returns: Returning stolen goods for refunds or store credits
Refund fraud: Manipulating the system to receive more than the value of the returned item

What is voice biometrics in retail?

Voice biometrics is a technology that identifies individuals based on unique vocal characteristics. It analyzes various features of a person’s voice, such as pitch, tone, and rhythm.

This technology can help protect retail contact centers from refund fraud, offering a secure and efficient means of verifying customer voices during transactions, including returns.

Unlike traditional authentication methods, such as passwords, voice biometrics provide an additional layer of security by leveraging something inherently unique to each individual—their voice. When used in tandem with other authentication factors, this advanced technology can assist retailers in combating fraudulent returns while helping create a faster and simpler returns process.

How voice biometrics can detect return fraud

Voice biometric analysis brings multiple benefits to retailers, helping to reduce fraud and improve operational efficiency.

Real-time authentication

With voice biometrics, you can authenticate customers in real-time, helping to ensure that the person initiating a return is the purchaser. This technology can be particularly useful in contact centers, where authenticating customers through traditional methods is more challenging.

Fraud detection

Voice biometrics can identify suspicious behavior patterns by the individual attempting the return.

Multifactor authentication

You can use voice biometrics as part of a multifactor authentication (MFA) approach, combining content-agnostic voice verification with other verification methods like PINs or SMS codes.

With this approach, even if one method fails, or if some credentials are lost or stolen, you still have a method to detect fraudulent activity.

Secure transactions

Voice biometrics can help create a secure environment for customers during their transactions. Once the system receives authentication information on the customer, it can securely process the return, significantly reducing the chances of refund fraud. This helps protect the retailer from loss and can provide customers with peace of mind, knowing their information is securely handled.

Accelerating return transactions

When using traditional authentication methods, customers can often find the process tedious. Voice biometrics help speed up return transactions, as customers can skip more lengthy verification procedures.

This helps create a faster, hassle-free return process, contributing to a better overall customer experience.

Data protection

Retailers can use voice biometrics to enhance data protection protocols, maintaining their consumers’ trust.

Implementing voice biometrics in your retail system

Integrating voice biometrics into your retail system in a way that’s effective and user-friendly requires careful planning.

Evaluate current systems

Start by evaluating your existing return processes and fraud detection strategies. Understanding where current vulnerabilities lie will help identify how voice biometric analysis can fill those gaps.

Select a reliable voice biometrics solution provider

Partnering with a reliable voice biometrics provider is crucial. Look for vendors with experience in retail security, a track record of success, and robust data protection measures.

Integrate voice biometrics seamlessly into retail systems

Ensure that voice biometrics integrate smoothly with your existing retail systems. This will reduce disruption during the implementation phase and allow both customers and staff to adapt quickly to the new system.

Train staff on using voice biometrics system

Training your staff members on how to use the voice biometrics system effectively is critical. Otherwise, no matter how good the technology is, there’s an increased risk of human error that could eventually lead to return fraud.

Training should include knowing when and how to use the technology and troubleshooting potential issues to prevent delays in the returns process.

Monitor system performance and optimize processes

After implementation, regularly monitor the system’s performance to ensure it functions as expected. Make necessary adjustments to optimize the system’s capabilities and improve its accuracy and efficiency in supporting fraud prevention efforts.

Additional benefits of voice biometrics in retail

Beyond helping prevent return fraud, voice biometrics offer additional advantages that enhance the overall retail experience.

Reduced fraud costs: By minimizing fraudulent returns, retailers can significantly reduce the financial losses associated with them. This helps merchants optimize their operations, improve profitability, and focus resources on serving genuine customers.
Convenience: Voice biometrics streamline the return process by eliminating the need for physical IDs or receipts. Customers can complete their returns quickly and easily, leading to a better shopping experience.
Trust and loyalty: Implementing voice biometrics builds trust with customers, as they feel confident that their identities and transactions are secure. This increased level of trust enhances customer loyalty and encourages repeat business.
Transparency: Maintaining transparency with customers about the use of voice biometrics for fraud detection can foster confidence. Clear communication regarding how voice analysis is used will help consumers understand the purpose and benefits of this technology.

Adopt a voice biometrics solution to help prevent return fraud

Return fraud is a serious issue affecting retailers worldwide, leading to losses of billions of dollars each year. While strict return policies may be somewhat helpful, retailers need to find better, customer-friendly alternatives. One such approach is voice biometrics, which offers additional defenses against fraudulent returns while improving the customer experience.

Voice biometric solutions can help merchants secure their return processes, reduce fraud costs, and build stronger relationships with customers. Adopting such a technology may seem like a significant shift, but its long-term benefits, both in fraud detection and customer trust, make it the perfect choice for small and large retailers.

More and more incidents involving deepfakes have been making their way into the media, like the one mimicking Kamala Harris’ voice in July 2024. Although AI-generated audio can offer entertainment value, it carries significant risks for cybersecurity, fraud, misinformation, and disinformation.

Governments and organizations are taking action to regulate deepfake AI through legislation, detection technologies, and digital literacy initiatives. Studies reveal that humans aren’t great at differentiating between a real and a synthetic voice. Security methods like liveness detection, multifactor authentication, and fraud detection are needed to combat this and the undeniable rise of deepfake AI.

While deep learning algorithms can manipulate visual content with relative ease, accurately replicating the unique characteristics of a person’s voice poses a greater challenge. Advanced voice security can detect real or synthetic voices, providing a stronger defense against AI-generated fraud and impersonation.

What is deepfake AI?

Deepfake AI is synthetic media generated using artificial intelligence techniques, typically deep learning, to create highly realistic but fake audio, video, or images. It works by training neural networks on large datasets to mimic the behavior and features of real people, often employing methods such as GANs (generative adversarial networks) to improve authenticity.

The term “deepfake” combines “deep learning” and “fake content,” showing the use of deep learning algorithms to create authentic-looking synthetic content. These AI-generated deepfakes can range from video impersonations of celebrities to fabricated voice recordings that sound almost identical to the actual person.

What are the threats of deepfake AI for organizations?

Deepfake AI poses serious threats to organizations across industries because of its potential for misuse. From cybersecurity to fraud and misinformation, deepfakes can lead to data breaches, financial losses, and reputational damage and may even alter the public’s perception of a person or issue.

Cybersecurity

Attackers can use deepfake videos and voice recordings to impersonate executives or employees in phishing attacks.

For instance, a deepfake voice of a company’s IT administrator could convince employees to disclose their login credentials or install malicious software. Since humans have difficulty spotting the difference between a genuine and an AI-generated voice, the chances of a successful attack are high.

Voice security could help by detecting liveness and using multiple factors to authenticate calls.

Fraud

AI voice deepfakes can trick authentication systems in banking, healthcare, and other industries that rely on voice verification. This can lead to unauthorized transactions, identity theft, and financial losses.

A famous deepfake incident led to $25 million in losses for a multinational company. The fraudsters recreated the voice and image of the company’s CFO and several other employees.

They then proceeded to invite an employee to an online call. The victim was initially suspicious, but seeing and hearing his boss and colleagues “live” on the call reassured him. Consequently, he transferred $25 million into another bank account as instructed by the “CFO.”

Misinformation

Deepfake technology contributes to the spread of fake news, especially on social media platforms. For instance, in 2022, a few months after the Ukraine-Russia conflict began, a disturbing incident took place.

A video of Ukraine’s President Zelenskyy circulated online, where he appeared to be telling his soldiers to surrender. Despite the gross misinformation, the video stayed online and was shared by thousands of people and even some journals before finally being taken down and labeled as fake.

With AI-generated content that appears credible, it becomes harder for the public to distinguish between real and fake, leading to confusion and distrust.

Other industry-specific threats

The entertainment industry, for example, has already seen the rise of deepfake videos in which celebrities are impersonated for malicious purposes. But it doesn’t stop there—education and even everyday business operations are vulnerable to deepfake attacks. For instance, in South Korea, attackers distributed deepfakes targeting underaged victims in an attack that many labeled as a real “deepfake crisis.”

The ability of deepfake AI to create fake content with near-perfect quality is why robust security systems, particularly liveness detection, voice authentication, and fraud detection, are important.

Why voice security is essential for combating deepfake AI

Voice security can be a key defense mechanism against AI deepfake threats. While you can manipulate images and videos to a high degree, replicating a person’s voice with perfect accuracy remains more challenging.

Unique marker

Voice is a unique marker. The subtle but significant variations in pitch, tone, and cadence are extremely difficult for deepfake AI to replicate accurately. Even the most advanced AI deepfake technologies struggle to capture the complexity of a person’s vocal identity.

This inherent uniqueness makes voice authentication a highly reliable method for verifying a person’s identity, offering an extra layer of security that is hard to spoof.

Resistant to impersonation

Even though deepfake technology has advanced, there are still subtle nuances in real human voices that deepfakes can’t perfectly mimic. That’s why you can detect AI voice deepfake attempts by analyzing the micro-details specific to genuine vocal patterns.

Enhanced fraud detection

Integrating voice authentication and liveness detection with other security measures can improve fraud detection. By combining voice verification with existing fraud detection tools, businesses can significantly reduce the risks associated with AI deepfakes.

For instance, voice security systems analyze various vocal characteristics that are difficult for deepfake AI to replicate, such as intonation patterns and micro-pauses in speech. These systems can then catch these indications of synthetic manipulation.

How voice authentication mitigates deepfake AI risks

Voice authentication does more than just help verify identity—it actively helps reduce the risks posed by deepfake AI. Here’s how:

Distinct voice characteristics

A person’s voice has distinct characteristics that deepfake AI struggles to replicate with 100% accuracy. By focusing on these unique aspects, voice authentication systems can differentiate between real human voices and AI-generated fakes.

Real-time authentication

Voice authentication provides real-time authentication, meaning that security systems can detect a deepfake voice as soon as an impersonator tries to use it. This is crucial information for preventing real-time fraud attempts.

Multifactor authentication

Voice authentication can also serve as a layer in a multifactor authentication system. In addition to passwords, device analysis, and other factors, voice adds an extra layer of security, making it harder for AI deepfakes to succeed.

Enhanced security measures

When combined with other security technologies, such as AI models trained to detect deepfakes, voice authentication becomes part of a broader strategy to protect against synthetic media attacks and fake content.

Implementing voice authentication as a backup strategy

For many industries—ranging from finance to healthcare—the use of synthetic media, such as AI-generated voices, has increased the risk of fraud and cybersecurity attacks. To combat these threats, businesses need to implement robust voice authentication systems that can detect and help them mitigate deepfake attempts.

Pindrop, a recognized leader in voice security technology, can offer tremendous help. Our solutions come with advanced solutions for detecting deepfake AI, helping companies safeguard their operations from external and internal threats.

Pindrop® Passport is a robust multifactor authentication solution that allows seamless authentication with voice analysis. The system analyzes various vocal characteristics to verify a caller.

In real-time interactions, such as phone calls with customer service agents or in financial transactions, Pindrop® Passport continuously analyzes the caller’s voice, providing a secure and seamless user experience.

Pindrop® Pulse Tech goes beyond basic authentication, using AI and deep learning to detect suspicious voice patterns and potential deepfake attacks. It analyzes content-agnostic voice characteristics and behavioral cues to flag anomalies, helping organizations catch fraud before it happens.

Pindrop® Pulse Tech provides an enhanced layer of security and improves operational efficiency by spotting fraudsters early in the process. For companies that regularly interact with clients or partners over the phone, this is an essential tool for detecting threats in real time.

For those in the media, nonprofits, governments, and social media companies, deepfake AI can pose even more problems, as the risk of spreading false information can be high. Pindrop® Pulse Inspect offers a powerful solution to this problem by providing rapid analysis of audio files to detect synthetic speech.

The tool helps verify that content is genuine and reliable by analyzing audio for liveness and identifying segments likely affected by deepfake manipulation.

The future of voice security and deepfake AI

As deepfake AI technologies evolve, we need appropriate defense mechanisms.

Voice authentication is already proving to be a key factor in the fight against deepfakes, but the future may see even more advanced AI models capable of detecting subtle nuances in synthetic media. With them, organizations can create security systems that remain resilient against emerging deepfake threats.

Adopt a voice authentication solution today

Given the rise of deepfake AI and its growing threats, now is the time to consider implementing voice security in your organization’s security strategy.

Whether you’re concerned about fraud or the spread of misinformation, voice authentication provides a reliable, effective way to mitigate the risks posed by deepfakes.

If you received an audio message that sounded like someone you knew, could you tell if it was fake? Most people like to believe the answer is yes. Studies have a different opinion. Humans can’t reliably detect synthetic voices, so we make the perfect targets for deepfake impersonation.

On the other hand, liveness detection techniques have proven to spot synthetic audio reliably. How does this technology work, and why is it so critical? This article will explore those questions and more.

Understanding deepfake impersonation

Deepfake impersonation refers to the use of AI to generate highly realistic audio or video that mimics an individual’s voice or appearance. For voice specifically, deepfake impersonation can create fabricated speech patterns that sound almost indistinguishable from real voices.

This technology poses significant risks, especially concerning sensitive information or financial transactions. AI-driven voice synthesis can recreate a person’s voice with convincing clarity.

Common techniques for replicating voice samples include generative adversarial networks (GANs) and auto-encoders. These networks analyze and learn speech patterns until they can replicate them with good precision.

These primitive types of synthetic voices can be easily recognized as unnatural, but more advanced models based on neural networks are harder to spot. They can mirror aspects of genuine speech, like tone and emotional inflection, which make it harder for humans to detect.

Some of the most sophisticated algorithms even use the background noise of the original recording, enhancing the illusion of authenticity.

The rapid evolution of voice deepfakes

When synthetic voices first came to the public’s attention, their usage was mostly harmless. Creating these audios required technical knowledge and access to highly specialized tools. However, as generative AI becomes more accessible, it has become easier to create deepfakes–which raises many concerns about their potential usage for fraudulent purposes.

Anatomy of voice-based deepfake impersonation

AI-driven techniques are at the core of deepfake impersonation, and their applications range from scams and disinformation in the media to harmless entertainment.

AI-driven voice synthesis techniques

AI-driven voice synthesis relies on various techniques that use deep learning models to mimic a person’s speech. Key methods include:

WaveNet

Developed by DeepMind, this technique uses neural networks to produce high-quality speech by predicting waveforms.

Text-to-speech (TTS) synthesis

This transforms written text into speech while adjusting elements like speed, pitch, and tone to make the voice sound natural.

Generative adversarial networks (GANs)

GANs are a class of machine learning systems where two neural networks—one generating fake data and the other evaluating its authenticity—compete, leading to increasingly realistic outputs.

Voice cloning technologies

These systems require minimal voice data, sometimes just a few seconds, to replicate a speaker’s speech patterns and tonal characteristics.

Common applications of deepfake voice impersonation

Deepfake impersonation is now famous for its malicious uses, but legitimate use cases still exist. Two common applications include:

Entertainment and film

Producers can use AI technologies to recreate the voices of deceased actors or produce voiceovers when actors aren’t available for reshoots.

Customer service automation

Many call centers now use AI-generated voices as a first interaction with customers. While these can’t replace a real customer support agent, they’re a more pleasant way of triaging customers before connecting them to the right department.

Limitations of deepfake impersonation technology

While deepfake impersonation technology is progressing rapidly, it still has limitations. These flaws make it possible to spot and identify deepfakes using advanced detection tools, demonstrating that they are not as infallible as they may seem.

Audio artifacts

Slight distortions or glitches in the synthesized voice can give away the deepfake, especially in longer conversations.

Limited emotional range

While AI can mimic tone and cadence, it often struggles with complex emotional expression, leading to unnatural speech patterns.

High computational cost

Generating high-quality deepfakes requires significant computational resources, limiting its scalability for real-time applications.

Real-time challenges

Real-time voice impersonation is still difficult to achieve without lag or noticeable delays, which can signal you’re not listening to a real human.

How deepfake detection technology can outsmart synthetic speech

Deepfake detection software can better spot the difference between synthetic and human voice, even when the average person can’t. Here’s how these tools work:

1. Voice analysis

Voice analysis is important in detecting audio deepfake impersonation. It analyzes content-agnostic vocal features such as pitch, speech rhythm, and timbre. Evaluating these aspects of speech can help expose evidence of synthetic voice.

2. Real-time analysis

Real-time liveness detection helps catch deepfake impersonation during live conversations. Modern systems can analyze voice during speech, identifying signs of deepfake manipulation such as unnatural pauses, delays, or tonal inconsistencies.

These systems are crucial for high-stakes situations, such as customer service interactions or financial transactions, where prompt detection is required. Solutions like Pindrop® Pulse enable near real-time analysis, giving you the tools to react quickly to identified deepfakes.

3. Adaptability to new deepfake techniques

Something new is always emerging in the world of AI technology. Liveness detection systems must have the same rhythm and adapt continuously to new threats.

Researchers can train machine learning models to recognize new patterns associated with deepfake voices, improving detection rates over time. Updating algorithms regularly and leveraging large datasets of known deepfake attempts can strengthen these systems even when new deepfake techniques appear.

Examples of liveness detection vs. deepfake impersonation

In 2019, a voice fraud incident occurred when scammers targeted the UK subsidiary of a German firm. The attackers impersonated the company’s Germany-based CEO and convinced the CEO of the UK subsidiary to transfer $243,000. Since such attacks were uncommon then, the senior executive didn’t immediately react and transferred the money as requested.

The fraudster didn’t stop there, though. They called the company again, ensuring they had initiated a reimbursement. When that reimbursement didn’t come through, the victim became suspicious. When a new request for money came from the same source, the victim suspected something was wrong. Later analysis of the audio revealed the voice was indeed a deepfake.

Incidents like this have increased over the years. However, with tools like liveness detection, fraud attempts can be detected before they can cause harm.

Future-proofing against deepfake impersonation

Take steps to avoid falling prey to scams–like adopting advanced detection technologies and fostering an adaptive and layered security approach that grows alongside the threat landscape.

Use audio deepfake detection solutions

Use voice security technologies that can analyze audio and help protect against voice fraud.

Implement multifactor authentication (MFA) for voice-based systems

MFA is a great technique for improving security and combating deepfakes. Methods include behavioral analysis or device-based authentication that can be used alongside voice analysis.

Leverage cloud-based AI for scalable deepfake detection

Cloud-based AI systems offer a scalable and flexible option, helping organizations analyze vast amounts of voice data in near real-time. They are updated continuously, which can help organizations keep pace with new deepfake technologies.

Conduct regular training and awareness programs

While humans are less likely to recognize AI-generated voices, they can still avoid falling prey to scams if they understand what they’re up against. Conduct training that raises phishing awareness and helps people recognize red flags such as unusual requests or patterns of speech, audio glitches, and more.

Implement deepfake detection software in your organization

Deepfake impersonation poses a serious risk for organizations. A successful attack can impact your brand reputation and lead to severe financial losses.

Start using tools with liveness detection and real-time voice analysis to create a robust defense mechanism against AI-driven impersonation. These tools can help protect your company against costly breaches, fraud, and reputational damage in the future.

One great tool for liveness detection is Pindrop® Pulse which helps you verify whether or not a caller’s voice is human or synthetic–helping you prevent scams against your organization and stay one step ahead of fraudsters.

Platform

2025 Voice Intelligence and Security Report

Solutions

2025 Voice Intelligence and Security Report

Partners

2025 Voice Intelligence and Security Report

Company

2025 Voice Intelligence and Security Report

Contact center fraud is more rampant than ever

Modern fraud detection for modern threats

One agency’s journey to smarter fraud detection

“You always need the balance of keeping your legitimate customers happy with stopping your fraudulent customers. Pindrop allowed us to do both, and we could customize it to fit within our system. That was one big selling point for us when we implemented it”

AI voice + video makes way for fake job applicants

Real-time face swap in a live Zoom meeting

AI voice clone with live dialogue

Deepfake job applicants

Introducing Pindrop® Pulse for meetings

Introducing Pindrop® Pulse for meetings

Fraud looks + sounds different

Trust is the new attack surface

How Pindrop® Pulse can support your team

“Who’s on the call?” isn’t enough anymore

About the VEC

Challenges

Goals

Detect fraudulent call center activity in real time

Build a larger threat response and case investigation mechanism around the new solution

Reduce usage of legacy authentication methods

Before Pindrop® Solutions

Why Pindrop

Pindrop provides a proactive vs. a reactive solution

“At the end of the day, Pindrop fit best with what we needed to uncover and mitigate fraud, and it was an easier migration and integration with our system.”

“You always need the balance of keeping your legitimate customers happy with stopping potential fraudsters. Pindrop allowed us to do both, and we could customize it to fit within our system. That was a big selling point for us when we implemented the Protect solution.”

Why did VEC choose Pindrop®?

Multifactor fraud detection that alerts to risks in real time

Seamless integration with existing infrastructure

Balance of fraud detection with reduced friction for claimants

What ROI did Pindrop® Solutions deliver?

Better fraud tracking

“Pindrop not only identifies a lot [of fraud] for us, but also gives us robust tracking. It flags a call, gives us a score, and allows us to expand that out and do further investigations within it. This has helped us build the overall fraud detection system.”

Fraud detection in the IVR

Richer fraud insights with ‘Custom Attributes’

Fraud detection and loss avoidance

Fast implementation and results

Your contact center may already be under attack—from voices that aren’t real.

What’s inside the report?

Deepfake fraud in contact centers: 2025 trends and shocking stats

Agentic AI and voice deepfakes are redefining trust and identity

Authentication is under attack

The future of voice security starts now

Annual Report

2025 Voice Intelligence and Security Report

What’s in the report

Related research + insights

How One Agency Fought Back Against Rising Contact Center Fraud

The Virginia Employment Commission Reduced Fraud by+$500K Within Months

2025 Voice Intelligence and Security Report

ANNUAL REPORT

2024 Voice Intelligence and Security Report

Related research + insights

How One Agency Fought Back Against Rising Contact Center Fraud

The Virginia Employment Commission Reduced Fraud by+$500K Within Months

2025 Voice Intelligence and Security Report

What is return fraud in retail?

How fraudsters use return fraud

Receipt fraud

Wardrobing

Cross-retailer returns

Top example cases of return fraud in 2024

Example #1: The electronics resale racket

Example #2: Fraudulent gift card exchanges

Example #3: Social media–driven scam

Example #4: Fake return apps

How to combat return fraud in 2025 and beyond

Mitigate losses from return fraud with Pindrop® Solutions

Why Pindrop?

Understanding secure authentication

The unique challenges of healthcare data security

Authentication over passwords: what’s the difference?

Stronger access restrictions with authentication

Introducing Pindrop^® Pulse for meetings

Introducing Pindrop^® Pulse for meetings

How Pindrop^® Pulse can support your team

Mitigate losses from return fraud with Pindrop^® Solutions

Enhance your authentication process with Pindrop^® Solutions

Seamless integration with Pindrop^® Passport

Pindrop^® Agent Screen Pop for real-time authentication

Before Pindrop^® Solutions

What did Pindrop^® Solutions deliver?

Business impact of Pindrop^® Solutions

How Pindrop^® Solutions detect deepfake audio