Real-Time Fraud Detection System for Digital Banking

A fast-growing digital bank was experiencing a 3.2% fraud rate on transactions, costing millions annually. Their rule-based system generated too many false positives, frustrating legitimate customers and overwhelming the fraud investigation team.

We deployed a real-time machine learning fraud detection system using ensemble methods and graph neural networks to identify suspicious patterns. The system analyzes transaction behavior, device fingerprints, and network relationships to flag fraud with high precision while minimizing false positives.