Graph–Temporal Fraud Detection with Triplet Loss under Class Imbalance

Abstract

Financial fraud detection in transaction networks is challenging due to evolving attack strategies, complex relational structures, and extreme class imbalance. We propose a hybrid deep learning model that fuses Graph Convolutional Neural Networks (GCNNs) with bidirectional LSTMs enhanced by temporal attention, enabling joint modeling of structural dependencies and sequential transaction patterns. To improve class discrimination, the framework incorporates Triplet Loss, enhancing prior contrastive approaches, which enhances embedding separability under highly imbalanced conditions. Furthermore, we introduce graph augmentation strategies, including edge perturbation, node feature masking, and adaptive subgraph sampling, to increase robustness against noise and incomplete networks. Experiments on the IEEE-CIS and synthetic datasets with varied patterns demonstrate that the proposed model achieves up to 6.2% improvement in F1-score with a modest precision-recall trade-off favoring high-recall scenarios and 12% higher recall compared to strong baselines. Ablation studies confirm the complementary roles of the graph, temporal, and metric learning components. These consistent improvements, demonstrate that incorporating Triplet Loss within graph–temporal modeling provides a principled and effective approach to imbalanced fraud detection, establishing our model as a scalable and robust solution for fraud detection in financial transaction networks.