Graph-based fraud detection has widespread application in modern industry
scenarios, such as spam review and malicious account detection. While
considerable efforts have been devoted to designing adequate fraud detectors,
the interpretability of their results has often been overlooked. Previous works
have attempted to generate explanations for specific instances using post-hoc
explaining methods such as a GNNExplainer. However, post-hoc explanations can
not facilitate the model predictions and the computational cost of these
methods cannot meet practical requirements, thus limiting their application in
real-world scenarios. To address these issues, we propose SEFraud, a novel
graph-based self-explainable fraud detection framework that simultaneously
tackles fraud detection and result in interpretability. Concretely, SEFraud
first leverages customized heterogeneous graph transformer networks with
learnable feature masks and edge masks to learn expressive representations from
the informative heterogeneously typed transactions. A new triplet loss is
further designed to enhance the performance of mask learning. Empirical results
on various datasets demonstrate the effectiveness of SEFraud as it shows
considerable advantages in both the fraud detection performance and
interpretability of prediction results. Moreover, SEFraud has been deployed and
offers explainable fraud detection service for the largest bank in China,
Industrial and Commercial Bank of China Limited (ICBC). Results collected from
the production environment of ICBC show that SEFraud can provide accurate
detection results and comprehensive explanations that align with the expert
business understanding, confirming its efficiency and applicability in
large-scale online services.

提出 SEFraud，一种新颖的基于图的自解释欺诈检测框架，通过利用可学习的特征掩码和边缘掩码，从信息丰富的异构类型交易中学习表达能力强的表示，采用新的三元组损失增强掩码学习的性能，演示了的效果。在中国工商银行的生产环境中部署并提供可解释的欺诈检测服务，结果与专业人员的业务理解相一致，确认了它在大规模在线服务中的高效性和适用性。