Deep Reinforcement Learning Enhanced Semi-supervised Graph Neural Network for Credit Card Fraud Detection

Abstract

Credit card fraud threatens global payment ecosystems, causing billions in losses and undermining public trust. Efficient fraud detection remains challenging due to surging transaction volumes and evolving tactics. While Graph Neural Networks (GNNs) excel at modeling structural relationships, they struggle in real-world scenarios characterized by label scarcity and often overlook discriminative feature-level signals, leaving rich risk signals underutilized without costly manual engineering. To address this, we propose DRESS, a Deep Reinforcement Learning (DRL) Enhanced Semi-supervised GNN framework. It employs a DRL agent to automatically capture and enhance feature-level risks, fusing them with graph-based structural risks and propagating via a gated temporal attention network for final prediction. To mitigate inefficient exploration of the DRL module, we incorporate a feature self-attention layer to weigh feature contributions to fraud detection and employ self-supervised intrinsic rewards to help optimize the DRL module efficiently. Extensive experiments on real-world datasets demonstrate that DRESS outperforms state-of-the-art methods, especially in low-label scenarios with only 2%–10% labeled samples. By empowering resource-limited institutions to combat fraud and prevent financial loss, DRESS secures the digital trust essential for inclusive growth, contributing to AI for poverty alleviation and economic development.