This is like having a 24/7 digital security guard watching every bank transaction in real time, learning what ‘normal’ looks like for each customer and instantly flagging or blocking anything that looks suspicious or out of character.
Reduces financial loss and chargebacks from fraud in banking and payments by detecting suspicious transactions earlier and more accurately than rule-based systems, while lowering false positives that frustrate customers and overload compliance teams.
Moat typically comes from proprietary labeled transaction data (fraud/not-fraud), integration into core banking workflows, and continuous model improvement based on feedback loops across many customers’ fraud patterns.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Real-time scoring latency and maintaining feature pipelines at high transaction throughput while meeting strict SLAs and regulatory auditability requirements.
Early Majority
Likely positions itself as a more modern, AI-native fraud detection stack versus legacy rule-based or on-prem solutions, with faster deployment, better real-time performance, and potentially lower integration cost for digital-first banks and fintechs.