Fraud detection remains a challenging task due to the complex and deceptive nature of fraudulent activities. Current approaches primarily concentrate on learning only one perspective of the graph: either the topological structure of the graph or the attributes of individual nodes. However, we conduct empirical studies to reveal that these two types of features, while nearly orthogonal, are each independently effective. As a result, previous methods can not fully capture the comprehensive characteristics of the fraud graph. To address this dilemma, we present a novel framework called Relation-Aware GNN with transFormer~(RAGFormer) which simultaneously embeds both semantic and topological features into a target node. The simple yet effective network consists of a semantic encoder, a topology encoder, and an attention fusion module. The semantic encoder utilizes Transformer to learn semantic features and node interactions across different relations. We introduce Relation-Aware GNN as the topology encoder to learn topological features and node interactions within each relation. These two complementary features are interleaved through an attention fusion module to support prediction by both orthogonal features. Extensive experiments on two popular public datasets demonstrate that RAGFormer achieves state-of-the-art performance. The significant improvement of RAGFormer in an industrial credit card fraud detection dataset further validates the applicability of our method in real-world business scenarios.

翻译：欺诈检测因欺诈活动的复杂性和欺骗性而始终是一项具有挑战性的任务。现有方法主要侧重于学习图的单一方面视角：即图的拓扑结构或单个节点的属性特征。然而，我们的实证研究表明，这两类特征虽近乎正交，却各自独立有效。因此，先前的方法无法充分捕捉欺诈图的综合特征。为解决这一困境，我们提出了一种名为关系感知图神经网络与Transformer（RAGFormer）的新型框架，该框架能够将语义特征与拓扑特征同时嵌入目标节点。这一简洁而有效的网络由语义编码器、拓扑编码器和注意力融合模块组成。其中，语义编码器利用Transformer学习跨不同关系的语义特征与节点交互；拓扑编码器采用关系感知图神经网络学习各关系内部的拓扑特征与节点交互。两类互补特征通过注意力融合模块交错融合，借助正交特征支持预测任务。在两个主流公开数据集上的大量实验表明，RAGFormer达到了最先进性能。该模型在工业信用卡欺诈检测数据集上的显著提升，进一步验证了其在真实业务场景中的适用性。