FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection

Abstract

As financial systems become increasingly complex and interconnected, traditional fraud detection methods struggle to keep pace with sophisticated fraudulent activities. This article introduces FraudGNN-RL, an innovative framework that combines Graph Neural Networks (GNNs) with Reinforcement Learning (RL) for adaptive and context-aware financial fraud detection. Our approach models financial transactions as a dynamic graph, where entities (e.g., users, merchants) are nodes and transactions form edges. We propose a novel GNN architecture, Temporal-Spatial-Semantic Graph Convolution (TSSGC), which simultaneously captures temporal patterns, spatial relationships, and semantic information in transaction data. The RL component, implemented as a Deep Q-Network (DQN), dynamically adjusts the fraud detection threshold and feature importance, allowing the model to adapt to evolving fraud patterns and minimize detection costs. We further introduce a Federated Learning scheme to enable collaborative model training across multiple financial institutions while preserving data privacy. Extensive experiments on a large-scale, real-world financial dataset demonstrate that FraudGNN-RL outperforms state-of-the-art baselines, achieving a 97.3% F1-score and reducing false positives by 31% compared to the best-performing baseline. Our framework also shows remarkable resilience to concept drift and adversarial attacks, maintaining high performance over extended periods. These results suggest that FraudGNN-RL offers a robust, adaptive, and privacy-preserving solution for financial fraud detection in the era of Big Data and interconnected financial ecosystems.