Project Overview
Developed for Global Financial Services, this real-time fraud detection system analyzes transaction patterns, user behavior, and historical data to identify fraudulent activities within milliseconds.
Challenge
The client was experiencing significant financial losses due to sophisticated fraud schemes while struggling with high false positive rates that impacted customer experience.
Solution
We built an ensemble machine learning model combining anomaly detection, graph neural networks, and behavioral analytics to identify complex fraud patterns in real-time.
Technology Stack
- Apache Kafka for real-time data streaming
- Python with XGBoost and PyTorch
- Graph databases for network analysis
- AWS Lambda for serverless processing
- React Dashboard for monitoring
Results & Impact
The system processes over 5 million transactions daily and has prevented more than $5 million in fraudulent activities while improving legitimate customer approval rates.