Temporal Heterogeneous Graph Contrastive Learning for Fraud Detection in Credit Card Transactions

Credit card fraud detection remains a critical challenge in financial security, characterized by evolving fraud patterns, sparse labeled data, and severe class imbalance. Traditional methods often fail to capture the complex temporal dynamics and heterogeneous relationships inherent in financial transaction networks. To address these limitations, we propose TH-GCL (Temporal Heterogeneous Graph Contrastive Learning), a novel framework that integrates heterogeneous graph modeling, temporal pattern recognition, and contrastive learning for enhanced fraud detection. Our approach constructs a temporal heterogeneous graph incorporating multiple entity types including users, transactions, merchants, and devices, with time-aware edge weights to capture evolving behavioral patterns. We design a temporal-aware graph neural network architecture that learns hierarchical representations by jointly modeling structural dependencies and temporal evolution patterns. Furthermore, we introduce a dual-view contrastive learning mechanism that creates augmented graph views through both structural perturbation and temporal masking, enabling the model to learn robust representations under different perspectives. The contrastive objective encourages the model to distinguish between normal and fraudulent transaction patterns while maintaining consistency across augmented views. Extensive experiments on the IEEE-CIS Fraud Detection dataset demonstrate that TH-GCL achieves superior performance compared to state-of-the-art baselines, with improvements of 5.2% in AUC-ROC and 8.7% in AUC-PR. Ablation studies confirm the effectiveness of each component, while analysis of learned representations reveals meaningful fraud pattern discovery. Our framework exhibits strong generalization capability across different time periods and transaction volumes, making it practical for real-world deployment in dynamic fraud detection scenarios.