Real-Time Fraud Detection System

Project Summary

Built an end-to-end real-time fraud detection pipeline processing 10M+ transactions daily with sub-100ms latency, reducing fraud losses by 60% while maintaining a low false positive rate.

Problem Statement

• Legacy rule-based system was missing 40% of fraud cases
• False positive rate of 15% created customer friction and support burden
• Batch processing meant fraud was detected hours after occurrence
• No ability to adapt to emerging fraud patterns without manual rule updates

System Architecture

The system uses a streaming architecture with Kafka for real-time event ingestion, a feature store backed by Redis for low-latency feature serving, and a model serving layer on Kubernetes with automatic scaling. The ML models are trained offline using PyTorch and deployed through a CI/CD pipeline with shadow mode testing.

Model & Approach

• Developed a two-stage model: fast heuristic filter followed by deep learning classifier
• Engineered 200+ features including real-time aggregations, graph-based features, and device fingerprinting
• Implemented online learning components to adapt to emerging fraud patterns
• Built an automated labeling pipeline using fraud analyst feedback loops

MLOps & Deployment

• Automated retraining pipeline triggered by data drift detection
• A/B testing framework for safe model rollouts with automatic rollback
• Feature store ensuring consistency between training and serving
• Real-time monitoring dashboard with alerting on key metrics
• Shadow mode deployment allowing comparison before production switch

Results & Impact

Lessons Learned

• Feature engineering contributed more to model performance than architecture changes
• Investing in observability early paid dividends during incident response
• Close collaboration with fraud analysts was crucial for labeling quality
• Shadow mode deployment caught several edge cases before they reached production