How to Leverage Digital Identity Data to Outfox Fraudsters and Cut False Positives 30%

Financial institutions using digital identity resolution have seen fraud detection accuracy improve by over 20% and false positives drop by up to 30% in year one — all while responding to risk in roughly 100 milliseconds.

Most fraud solutions are reactive, not preventative. They score single, completed transactions using historical patterns and third-party data — ignoring the behavior happening around every transaction. That leaves fraud teams buried in false positives, chasing losses instead of stopping them, while closed-box environments and PII transfer restrictions limit what third-party data can even tell you.

The question is clear: how do you use digital identity resolution and behavioral biometrics to stop fraud before it happens, not after the loss is already booked?

Celebrus builds evidence-based digital identity profiles from the very first anonymous visit — capturing keystroke dynamics, mouse movement, and navigation habits across sessions, devices, and channels. This first-party behavioral data feeds machine learning models that assess risk in milliseconds, enabling real-time intervention pre- and post-authentication instead of reactive, after-the-fact fraud management.

Digital Identity Resolution • Behavioral Biometrics • Real-Time Fraud Prevention • First-Party Data • Identity Graphs