Predictive Fraud Detection Engine

One of the world's largest banks was losing $400M annually to sophisticated fraud schemes that evaded their rule-based detection systems. We deployed a gradient-boosted decision tree ensemble trained on 8 years of transaction data, combined with real-time behavioral biometrics. The system processes 50,000 transactions per second with sub-100ms latency, flagging suspicious activity before funds leave the account. Fraud losses dropped by 94% in the first year.