FraudShield

Real-time fraud detection pipeline for financial transactions using stream processing and ML

Overview

FraudShield is an end-to-end fraud detection pipeline combining ensemble machine learning models with real-time transaction scoring. The system processes financial transaction streams, identifies anomalous patterns, and provides explainable predictions to support human decision-making.

Architecture

  • Feature engineering: Temporal aggregations, velocity checks, device fingerprinting features
  • Model ensemble: Gradient boosted trees (XGBoost) + isolation forest for anomaly detection
  • Explainability: SHAP-based feature attribution for every prediction
  • Real-time scoring: Sub-100ms inference latency per transaction

Performance

Metric Value
AUC-ROC 0.97
Precision @ 1% FPR 0.82
Inference latency < 50ms