Multi-Hop Relational Modeling for Credit Fraud Detection via Graph Neural Networks

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Abstract
Credit fraud detection is a central problem in financial risk management. It is challenging due to complex relationships among transaction entities, strong coordination of fraudulent activities, and high levels of behavioral camouflage at the individual level. Traditional approaches mainly rely on features from independent samples. They often fail to represent multi-entity interaction structures effectively. From a relational modeling perspective, this study investigates a graph neural network-based method for credit fraud detection. The method represents transaction entities and their interactions as a unified graph. It performs information propagation and neighborhood aggregation in graph space. It jointly models node attributes and structural context. It therefore learns more discriminative risk representations. During modeling, the network captures both local interaction patterns and multi-hop relational information. This makes anomalous patterns embedded in complex transaction networks more explicit. Based on the proposed method, this study establishes a standardized comparative evaluation on a public credit fraud dataset. It systematically compares the method with multiple existing models. The overall results confirm the effectiveness of graph-structured modeling for credit fraud detection. The findings show that, compared with detection methods that ignore or weaken relational information, graph neural network models achieve clear advantages in stability and consistency of risk identification. They better reflect the propagation characteristics of fraudulent behavior within network structures. These results support modeling credit fraud detection as a relation-aware graph learning problem. This formulation enhances risk characterization in complex financial scenarios. It also provides a structured modeling perspective for building intelligent risk control systems.

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