Turn unique human behaviors into advanced security measures by using behavior-based fraud analytics
Identify subtle anomalies that traditional fraud prevention methods can miss
Streamline security measures with technology that adapts to evolving threats
Enhance continuous security and analytics without disrupting user experience.
Fortify your business with behavioral biometric fraud detection. Sumsub allows you to analyze multiple events and data from users' devices throughout their entire user journey to create dynamic profiles that fraudsters cannot replicate.
Detect abnormal behaviors, including atypical typing patterns, touch gestures, and application usage.
Enable real-time authentication to continuously monitor users instead of one-off biometric methods that are easy to hack.
Add an invisible fraud prevention layer to enhance your security measures without interrupting the user experience.
Adapt to emerging criminal threats with behavioral biometrics that adjust dynamically instead of wasting resources on costly updates.
You only require a one-time integration with Sumsub’s SDK, and most clients begin checks within one week. All further settings are code-free and available in the dashboard.
Works on all devices
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Behavioral biometrics authenticate users based on unique behavioral patterns like typing, touch gestures, mouse movements, and application usage, enhancing security invisibly. This data is analyzed and used to create a dynamic behavioral profile.
Behavioral biometrics for online fraud prevention employ unique behavioral attributes like fingerprints, facial features, and voice to verify identity. Biometrics adds an additional layer of protection to the verification process by mitigating the risk of unauthorized access and fraudulent activities.
Behavioral analytics for fraud leverages machine learning algorithms to analyze user behavior patterns. A dynamic user behavioral profile creates a baseline by assessing keystrokes, touch gestures, and other device behaviors. Deviations from this baseline trigger alerts, allowing real-time identification of potentially fraudulent activities and enabling proactive fraud prevention measures.
Fraudulent behavior red flags include anomalies in user interactions, sudden changes in transaction patterns, atypical device usage, and deviations from established behavioral baselines. Advanced algorithms in behavioral analytics systems identify these deviations, triggering alerts for immediate investigation and fraud prevention measures.