Behavioral biometrics and machine learning are fundamentally changing the world of authentication — for the better. Whether used for preventing account takeover fraud, identity proofing, or risk-based authentication in payment apps, together they are overcoming the shortcomings of the traditional authentication process.

BioCatch co-founder Avi Turgeman recently contributed to Forbes on how behavioral biometrics and machine learning go hand-in-hand. Behavioral biometrics collect large amounts of data on user behavior, from the way a person holds a phone, scrolls or applies pressure, to identify people based on how they interact with devices and online applications. But people are unpredictable, which is why behavioral biometrics rely heavily on machine learning to parse through the data, establish user profiles and validate digital identities.

With new types of fraud on the rise, fueled by the continued drumbeat of data breaches, the type of authentication provided by AI and behavioral biometrics is needed more than ever.

For a more in-depth analysis on the relationship between machine learning and behavioral biometrics, read the full article: Machine Learning and Behavioral Biometrics: A Match Made in Heaven.

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