The Role of Deepfakes in Retail Fraud Schemes

Retail fraud schemes are an ever-evolving challenge in commerce. As scammers become more adept at technology-driven tactics, retailers must pay closer attention to deepfakes, a relatively new tool in the fraudster’s toolkit.

Deepfake audio and video can imitate individuals with astonishing accuracy, making it difficult to detect who is human and who is impersonating a legitimate customer or employee.

These risks extend beyond stealing goods in the retail industry; attacks can compromise payment details, loyalty accounts, and confidential customer data.

Deepfakes threaten retailers by causing fraud, data breaches, and financial losses. In this article, we will explore the core risks linked to deepfakes in retail fraud, their broader impacts, and key measures retailers can adopt to reduce these vulnerabilities.

The financial and business impact of deepfakes in retail fraud

Retail fraud takes many shapes, from return fraud to credit card scams. However, the involvement of deepfakes adds another level of complexity.

For instance, fraudsters might use AI-generated voices to manipulate contact center agents or digital self-service systems, bypass security questions, or conduct return fraud using fake identities.

The financial losses attributed to retail fraud schemes are staggering. Return fraud alone caused retailers to lose about $100 billion in 2023. The problem extends beyond large retail chains. Smaller businesses also shoulder the burden.

In 2023, retailers with 1–20 employees were the most likely to experience theft daily (17%), while businesses employing 21–30 staff were most likely to encounter theft multiple times a week (31%). These statistics highlight how retail fraud can drain profits and erode consumer confidence across all retail industry segments.

Types of deepfake-enabled retail fraud

Customer impersonation

Customer impersonation scams involve criminals mimicking the voice of a legitimate customer to conduct fraudulent activities. This can happen when a contact center receives a call from someone who sounds, in every respect, like an account holder.

Fraudsters who use deepfake voices can pose as legitimate customers, armed with enough personal or account information to request refunds, product returns, or account changes.

Example scenario

Employee impersonation

Another angle in retail fraud schemes is employee impersonation, where attackers clone the voice of a staff member—possibly someone in a managerial or security role—to instruct coworkers on suspicious tasks.

Example scenario

These scenarios highlight how flexible deepfakes enable different approaches to retail fraud. If you’re interested in learning more about the specific ways voice security solutions can bolster loss prevention strategies, consider the following articles:

Deepfake detection strategies

Advanced authentication methods

Conventional password-based or PIN-based protocols aren’t sufficient anymore. Instead, advanced authentication approaches are necessary. These could include:

By layering these methods, a contact center can reduce the success rate of deepfake-based fraud. In certain instances, organizations can also incorporate solutions that analyze metadata or other environmental cues, like a mismatch between the user’s known location and the call’s origin.

Deepfake detection software

As cybercriminals refine their manipulation tactics, technology providers are racing to develop software to flag artificially generated voices in real time. Pindrop^® Pulse uses advanced analysis techniques to spot anomalies in human speech patterns, known as liveness detection. This approach works by detecting subtle traits in a human’s voice that are difficult for generative AI models to replicate perfectly.

One illustrative example is our case study of a large eCommerce retailer on track to save nearly $10M in fraud losses. Here are the key highlights:

Improve deepfake detection with Pindrop technology

At Pindrop, we have closely followed the rise of deepfake-based retail fraud schemes. Human ears struggle to differentiate between a naturally occurring voice and an AI-generated one, and criminals capitalize on that gap.

Our liveness detection technology (available through the Pindrop^® Pulse add-on) fills that gap, evaluating a call for liveness and determining whether the voice is synthetic or human. Our solutions are proven in contact centers, and our latest technology helps address the rise of deepfakes. Our approach is multilayered:

Take the first step to help protect your business, build customer trust, and outsmart fraudsters by talking with one of our experts today.