FIW-GNN: A Heterogeneous Graph-Based Learning Model for Credit Card Fraud Detection

Kuan Yan, Junbin Gao, Dmytro Matsypura

Abstract: The global economic losses caused by credit card fraud are enormous and continuously increasing. Effective and accurate fraud detection has become a crucial task in recent years. Prior approaches can achieve good detection performance under certain conditions. However, these existing methods lack the robustness and scalability to deal with real-world credit card transactional datasets containing a large number of missing values. In this paper, we propose a Feature Importance-based Weighted Graph Neural Network (FIW-GNN) as an effective, stable, and practical solution for credit card fraud detection. First, we propose a method to construct a heterogeneous graph designed for credit card transactional datasets. Next, based on the architecture of the relational graph convolutional network, a feature importance-based method is employed to assign edge weights. Finally, we evaluate the effectiveness of FIW-GNN on two benchmark datasets. The experimental results demonstrate that FIW-GNN outperforms the state-of-the-art baselines in all selected evaluation metrics.