AI-powered fraud decisions that see the whole picture

An AI risk agent is an autonomous system that evaluates identity signals, behavioral patterns, and transaction context in real time to generate a risk score and take automated action — approving, flagging, or blocking — without human intervention.

Rules-based systems apply static if/then logic that fraudsters can learn and evade. AI fraud detection uses adaptive machine learning models trained on billions of signals, detecting novel fraud patterns that no predefined rule could catch.

Risk scoring assigns a numerical probability of fraud to each identity interaction — from 0 (low risk) to 100 (high risk) — based on document quality, biometric confidence, device signals, behavioral anomalies, and historical fraud patterns.

Incode Risk AI uses entity-level context — combining document, biometric, device, and behavioral signals — to distinguish legitimate edge cases from genuine fraud, reducing false positives that cause unnecessary friction for good users.

Yes. Synthetic identity fraud — where fraudsters combine real and fabricated data — leaves specific signals across document metadata, biometric consistency, and behavioral patterns. Incode Risk AI is trained to surface these cross-signal anomalies automatically.