OmniTrust Capabilities
OmniTrust is a comprehensive machine learning fraud detection model that integrates diverse data sources—including deepfake detection from extensive video analysis, biometric and credential matching, liveliness checks, payment network signals, synthetic identity detection, human feedback, IP and location data, device fingerprinting, and transactional data—to build detailed risk profiles and effectively identify and prevent impersonation and fraud across various authorization types.
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.
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.
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Related
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A June 2026 report reveals that financial institutions' traditional identity verification methods—relying on secret information, trusted documents, and biometric authenticity—are under massive commercial-scale attack, with infostealer malware in 2025 exfiltrating over 1.8 billion credentials from 5.8 million devices (an 800% increase), leading to widespread circulation of valid stolen banking and payment card data on dark web marketplaces, fueling account takeovers, synthetic identity fraud, and ransomware attacks, thereby exposing the critical vulnerabilities in current security assumptions.
How To Protect Your Business From Digital Identity Fraud
Digital identity fraud poses a significant and growing threat to businesses, involving complex tactics like phishing and synthetic identity theft that exploit multiple digital channels to steal sensitive information, resulting in substantial financial losses, and necessitating a layered defense strategy including multi-factor authentication, identity verification, proactive monitoring, and regular audits to effectively protect against and respond to such attacks.
Multi-Signal Fraud Detection Benchmarks
Proof has developed a layered fraud detection model that combines passive signals, active checks, and collective telemetry from its Identity Authorization Network to outperform traditional passive-only methods by 600-1,300% in detecting sophisticated fraud without increasing user friction, addressing the shortcomings of standard approaches like MFA and KBA that are increasingly ineffective against targeted attacks.
Top 10 Identity Verification Solutions to Consider in 2026
The 2026 guide on top identity verification solutions highlights the need for advanced IDV platforms like Proof that combat AI-driven fraud such as deepfakes and synthetic identities by offering high-assurance, legally defensible verification with biometric and document checks, human review, and transaction-level evidence tailored for regulated, high-value, and legally binding digital interactions.
Creating Phantoms: How Fraud Actors Build Synthetic Identities
The article explains how fraud actors create synthetic identities—fraudulent personas built from fictitious or partially fabricated personal information—by exploiting document and data verification processes, using a single authoritative identity document as a backbone and various easily forged residency proofs to bypass KYC checks and commit financial crimes, exemplified by the AI-generated persona Arthur Vance.
Deepfakes in the Financial Services Industry | Proof
The article discusses how AI-generated deepfakes pose significant fraud risks to financial services by exploiting the fraud triangle—motivation, opportunity, and rationalization—enabling sophisticated attacks like account takeover and payment fraud, while highlighting Proof's AI-driven platform that detects deepfake anomalies in real time to protect institutions from these emerging threats.