  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.
