Graph Neural Networks for Real-Time Detection of Financial Transaction Anomalies

Abstract

Financial fraud continues to pose significant challenges for banking institutions worldwide, with annual losses exceeding billions of dollars. Traditional fraud detection methods struggle to identify sophisticated fraudulent patterns in real-time transaction networks. This research explores the application of Graph Neural Networks for detecting financial transaction anomalies in real-time banking environments. Using a mixed-methods approach, we developed and tested a GNN-based detection system on transaction data from a major financial institution. Our proposed architecture achieved 94.7% precision and 91.3% recall, outperforming traditional machine learning methods by approximately 23%. The system processes transactions with an average latency of 47 milliseconds, making it suitable for real-time deployment. This study demonstrates that GNN-based approaches effectively capture complex relational patterns in transaction networks, offering substantial improvements over conventional fraud detection systems. The findings have important implications for banking security infrastructure and fraud prevention strategies.