Methodology that is designed to prevent enrollment of fraudsters, leverages risk analysis for authentication of genuine callers, meaning risky callers aren’t enrolled for authentication.
Machine learning derived risk intelligence for every call. Using the risk analysis to guide enrollment, fraud controls, and authentication protocols.
The ability to recognize different speaker profiles allows for developers to build customization and personalization features into contact centers, devices, and applications.
Helping predict what accounts are being actively targeted by criminal rings as early as 60 days before an account takeover attempt.
Recognizing devices that have been encountered before, whether an enrolled user or a potential bad actor that can be quickly spotted.
Utilizes Deep Voice® Engine technology to estimate age range, biological gender, and language.
We take our advanced audio analysis engine and compress the model to fit on embedded chips without compromising accuracy.
Measures risk using machine learning combined with insights from suspicious calling patterns and confirmed fraud calls.
Call metadata analysis with machine learning to help identify when a number is spoofed.