Deep Reinforcement Learning with Graph Neural Networks for Financial Fraud Risk Mitigation

Financial fraud risk mitigation is a growing challenge as fraudsters continuously develop new tactics to evade detection. Traditional fraud prevention methods, including rule-based systems and supervised machine learning models, struggle to adapt to evolving fraud patterns, leading to high false positives and an increased risk of undetected fraudulent transactions. Recent advancements in graph neural networks (GNNs) have enabled fraud detection models to capture complex transactional relationships, allowing for the identification of hidden fraud networks. However, static GNN models remain limited in their ability to adapt to new fraud strategies in real-time.This study proposes a deep reinforcement learning (DRL)-based fraud risk mitigation framework, integrating GNNs with adaptive decision-making policies. The GNN component models financial transactions as a heterogeneous graph, capturing multi-hop fraud pathways and high-risk account interactions. The DRL agent continuously optimizes fraud classification thresholds, ensuring that fraud detection strategies remain adaptive to emerging fraud tactics. The model is evaluated on large-scale financial transaction datasets, demonstrating higher fraud detection accuracy, lower false positive rates, and improved real-time adaptability compared to traditional fraud detection models. The results confirm that graph-based learning combined with DRL provides a scalable, intelligent solution for financial fraud risk mitigation.