Scores transactions in real time with supervised models and rules, auto-escalating risky events to investigators with evidence trails.
Catch suspicious activity early without slowing down good users. We fuse supervised ML with rules and graph checks to score transactions, applications, and behaviors in real time. The system prioritizes what matters, routes cases to analysts, and learns from outcomes.
How it works: signals stream in (transactions, device, location, history). A rules layer handles obvious patterns; ML models evaluate complex risk. Scores, reasons, and recommended actions (allow, step-up verification, block, review) are returned in milliseconds. Case management captures analyst feedback, attachments, and dispositions. Models retrain on confirmed fraud and false positives to improve precision.
Why Tagbin AI: we design for explainability and control. Analysts see the “why” behind scores and can override with policy. We support compliance with audit trails, retention policies, and in-region hosting. The result: lower loss rates, fewer manual reviews, and better user experience—because good users shouldn’t feel your defenses.
