OmniTrust

Fraud has nowhere to hide. OmniTrust sees it all.

Defend is powered by OmniTrust, the first ML fraud model purpose-built to flag impersonation for any type of authorization.

Talk to sales

Data from our network provides the fraud intelligence to protect every authorization

OmniTrust builds a comprehensive risk profile by integrating traditional identity signals with data from our extensive network. This approach provides a complete understanding of an individual and the things they agree to.

Deepfake detection

Proof has secured millions of authorizations over face-to-face video meetings with over 600K hours of video. OmniTrust has been trained to spot deepfakes on video.

Credential and biometric matching

OmniTrust identifies and flags inconsistencies such as a single individual registering as multiple users or a biometric scan not matching the registered user.

Liveliness detection

The machine learning model detects physical presence to protect against replay attacks or AI impersonation.

Payment networks signals

OmniTrust incorporates signals from payment networks to assess the risk associated with prior fraud indicators tied to a credit card.

Synthetic identity detection

The fraud model flags fake identities by detecting discrepancies in personal records or behavioral patterns.

Human-in-the-loop feedback

Thousands of agents help identify fraud and train the model when they report a suspicious user. Our network of agents provide a human in the loop.

IP and location data signals

By comparing a user's current IP, geolocation, and device context with historical patterns, the fraud model flags suspicious activity like impossible travel and the use of VPNs.

Device fingerprinting

Device intelligence helps catch coordinated fraud patterns like if one mobile phone is associated with multiple identities.

Transactional data

OmniTrust understands the nature of the transaction and incorporates that into the model. A suspicious phone number can carry different risks on a $100K retirement withdrawal vs. a mortgage application.