Think of this like a hyper-vigilant bank teller who has watched millions of checks go by and learned the subtle patterns of what ‘fraud’ looks like. Instead of relying on a few rigid rules, it uses AI to spot odd behavior in real time and flag suspicious checks before the money leaves the bank.
Traditional, rules-based systems are failing to keep up with a rapid surge in check fraud and increasingly sophisticated fraud rings. Manual review is slow, expensive, and misses complex patterns that span accounts, channels, and time. This AI system aims to automatically detect and prevent fraudulent checks with higher accuracy and lower false positives, reducing losses and investigation costs.
Proprietary fraud behavior data across institutions, mature anomaly-detection models, and deeply embedded integrations with core banking and payment-processing workflows that are costly to rip and replace.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Real-time scoring latency and stateful feature computation at very high transaction volumes, plus data-integration complexity across multiple banking systems and geographies.
Early Majority
Positioned as an AI-first, behavioral analytics-driven fraud platform focused on complex, evolving check fraud patterns rather than static rules, with an emphasis on enterprise-scale, real-time detection for the world’s largest banks.