Fraud Lab

Fraud Lab is Microblink’s in-house red and blue team of experts and adversarial AI researchers.  They simulate and test emerging attacks, build defenses against them, and ship those defenses to production before new fraud techniques reach the market.

• Adversarial detection at the source. Catches AI-generated IDs, deepfakes, face swaps, and synthetic identities before they enter downstream systems.
• Continuous model training. Hundreds of thousands of training and testing synthetic documents are generated every month. Models stay ahead of fraud evolution through constant adversarial pressure testing.
• Global threat mapping. Fraud hotspots and attack patterns are tracked across regions and vectors. Defenses are calibrated to match real-world conditions.

A Persistent, Unified View of the Actor

Identity Graph links documents, biometrics, session signals, payment card data, and registry data (PEP, sanctions, adverse media) into one profile per actor. That connected view makes it possible to recognize the same person across web, mobile, in-person, and agent-driven flows. 

Synthetic identities and reused credentials surface across accounts. Behavioral shifts become visible over time rather than getting lost between sessions. Every decision carries the full context of what came before, so risk scoring reflects the actor’s actual history. When new signals arrive, they enrich the existing profile automatically, making each subsequent evaluation more precise than the last.

Continuous User Journey Monitoring

Verification at onboarding sets the baseline. Continuous monitoring keeps that decision current. When a location shifts, a behavioral pattern breaks, or a high-value transaction hits an elevated risk threshold, the system re-evaluates trust in real time. The same applies to account changes like email, phone, or beneficiary updates, and to AI agents initiating actions on behalf of a user. Each trigger revalidates both identity and authority before the interaction proceeds.

The result is earlier fraud interception and higher approval rates because risk is graded in context. Legitimate actors move through with less friction. Sessions that don’t pass get escalated at exactly the right moment, with the right level of verification, based on the full picture of the actor’s history.