How We Built a Real-Time Fraud Detection Engine That Saved $4.2M

The Challenge

Our client — a mid-sized Indian private bank — was losing ₹3.5Cr monthly to fraud. Their rule-based system generated 40% false positives, frustrating legitimate customers.

Our Approach

Stage 1: Feature Engineering

We identified 200+ features per transaction: velocity patterns, device fingerprints, geo-velocity, merchant category risk scores, and behavioral biometrics.

Stage 2: Model Architecture

Ensemble of:

• ▸Gradient Boosting (XGBoost) for structured features
• ▸GNN for transaction graph analysis
• ▸Isolation Forest for anomaly detection

Stage 3: Explainability

Every blocked transaction generates a plain-English explanation: "Transaction blocked: card used in Mumbai 3 minutes after being used in Delhi."

Results After 6 Months

The system paid for itself in 11 days.