FIW-GNN: A Heterogeneous Graph-Based Learning Model for Credit Card Fraud Detection - Yan, Kuan; Gao, Junbin; Matsypura, Dmytro (2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023)
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.