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。

SEFraud：基于图解释掩码学习的自解释欺诈检测

SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative  Mask Learning

Graph-based fraud detection (GFD) can be regarded as a challenging
semi-supervised node binary classification task. In recent years, Graph Neural
Networks(GNN) have been widely applied to GFD, characterizing the anomalous
possibility of a node by aggregating neighbor information. However, fraud
graphs are inherently heterophilic, thus most of GNNs perform poorly due to
their assumption of homophily. In addition, due to the existence of heterophily
and class imbalance problem, the existing models do not fully utilize the
precious node label information. To address the above issues, this paper
proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector
includes a hybrid filtering module and a local environmental constraint module,
the two modules are utilized to solve heterophily and label utilization problem
respectively. The first module starts from the perspective of the spectral
domain, and solves the heterophily problem to a certain extent. Specifically,
it divides the spectrum into multiple mixed frequency bands according to the
correlation between spectrum energy distribution and heterophily. Then in order
to make full use of the node label information, a local environmental
constraint module is adaptively designed. The comprehensive experimental
results on four real-world fraud detection datasets show that SEC-GFD
outperforms other competitive graph-based fraud detectors.

本研究提出了一种半监督的基于图神经网络的欺诈检测器 SEC-GFD，通过混合过滤模块和局部环境约束模块解决了异态性和标签利用问题，实验证明 SEC-GFD 在四个真实世界的欺诈检测数据集上优于其他竞争对手的基于图的欺诈检测器。