Introducing GovFaceMatch: A New Standard in Identity Verification
For years, identity verification has forced organizations into an impossible choice: convert customers or stop fraud. For the first time, they no longer have to choose. GovFaceMatch eliminates that tradeoff entirely.
This is not an incremental improvement. It is a new standard for identity verification.
The Problem with “Better” Identity Verification
Traditional identity verification systems were built for a different era of fraud, before the scale and complexity of sophisticated, AI-enabled attacks. They rely on probabilistic signals such as database lookups, document authenticity checks, and risk scores to estimate whether someone is who they claim to be. That model worked when fraud was simpler, but it breaks down when fraudsters use real stolen data, high-quality fake documents, and their own biometrics to pass every check.
Consider a common scenario: a fraudster gains access to a real driver’s license through a data breach. AI tools now make it trivial to manipulate the photo on the document to match the fraudster’s own likeness. The underlying identity is real. The fraudster’s face matches the altered document during liveness detection. As a result, the verification attempt succeeds, and the fraud goes undetected.
The industry’s response has been to layer on more checks: additional document scans, database cross-references, and manual review queues. But adding more steps doesn’t fix a broken foundation. Verifying the wrong signals more frequently isn’t a solution.
Why the Existing Model Is Reaching Its Limit
Identity data is already widely compromised, with U.S. breaches at record highs. The Federal Reserve estimates that synthetic identity fraud, where real and fabricated data are combined to create entirely fictional identities, accounts for billions in annual losses and continues to grow as AI lowers the barrier to creating convincing credentials.
Fake identities built on real stolen data can cost just a few dollars to generate, and fully functional synthetic identities that pass 95% of onboardings can now be created in under 7 minutes. At this scale, traditional identity verification systems that rely on document integrity or database checks are structurally outmatched. Fraud is easier to execute, faster to scale, and harder to detect.
In response to more sophisticated fraud, the industry has layered on additional checks. But fraudsters aren’t the only ones experiencing friction. Up to 40% of legitimate users now abandon verification flows before completing them, and user expectations for fast and frictionless experiences are only increasing over time. Worse, fraudsters still get through.
We built GovFaceMatch because the traditional approach has reached its breaking point.
How GovFaceMatch Delivers a New Standard in Identity Verification
GovFaceMatch changes the model. Instead of asking “Does this document look legitimate?”, GovFaceMatch simplifies identity verification to its most essential function: confirming that the person undergoing verification matches a real government-issued identity.
By performing a real-time, one-to-one biometric comparison between a user’s live selfie and their official DMV record, GovFaceMatch verifies identity against the only source that cannot be fabricated: the issuing authority.
This is not just better document verification. It verifies identity directly against the government record, eliminating the need to trust the document at all.
The user experience is intentionally simple:
- The user scans their driver’s license barcode
- The user takes a live selfie
GovFaceMatch then performs a secure, real-time comparison against the corresponding DMV record and portrait, returning a simple match result. No document image analysis. No probabilistic scoring against third-party databases. A direct comparison against the government source of truth.
The Results
- 6x faster user experience than traditional document-based verification
- 20% increase in overall conversion rates
- 1000x more accurate at catching fraud than data-only identity checks
- 150x more accurate at catching fraud than document-based verification alone
Simultaneously reducing friction and improving fraud prevention was always the goal. GovFaceMatch doesn’t rebalance the tradeoff. It removes it entirely.
Built on Government Infrastructure, Designed for Scale
GovFaceMatch works through secure, consent-based integrations with state DMV systems, enabling direct verification against official issuing records. This approach extends trusted public infrastructure into private sector use cases without compromising control or oversight.
By grounding verification in government records, GovFaceMatch introduces a level of certainty that legacy systems cannot replicate.
It is also part of Incode’s broader GovMatch solution, alongside GovDataMatch, which verifies key identity attributes such as name and date of birth. Together, these solutions provide coverage across more than 95% of the U.S. population.
Privacy by Design
GovFaceMatch is designed to meet both consumer expectations and public sector requirements:
- For users: all verifications require explicit user consent, and no facial biometrics are retained after verification is complete.
- For government partners: all biometric matching occurs within the DMV environment, behind its security perimeter. No government-held data is shared back.
This ensures that identity can be verified with high certainty, without introducing privacy risks.
Moving Beyond the Tradeoff
For years, identity verification has been defined by compromise. Security came at the cost of user frustration, and a good user experience invited more exposure to fraud.
GovFaceMatch doesn’t make identity verification incrementally better. It sets a new standard: biometric matching directly against the government record, for organizations who never want to choose between their conversion and fraud prevention goals again.
Ready to see it in action? Request a demo.