Real-time Fraud Detection System

Developed a state-of-the-art fraud detection system using deep learning and anomaly detection techniques. The system analyzes transaction patterns, user behavior, and network effects to identify fraudulent activities in real-time.

The solution combines multiple neural network architectures including autoencoders for anomaly detection, graph neural networks for relationship analysis, and recurrent networks for sequence modeling. The system processes over 100,000 transactions per minute with millisecond-level response times.

The fraud detection system has been deployed across multiple financial institutions, preventing over $50M in fraudulent transactions while significantly reducing false positive rates that impact legitimate customers.