Effective High-order Graph Representation Learning for Credit Card Fraud Detection

• URL: http://arxiv.org/abs/2503.01556v1
• Date: Mon, 03 Mar 2025 13:59:46 GMT
• Title: Effective High-order Graph Representation Learning for Credit Card Fraud Detection
• Authors: Yao Zou, Dawei Cheng,
• Abstract summary: Fraudsters often disguise their crimes by using legitimate transactions through several benign users to bypass anti-fraud detection.<n>Existing graph neural network (GNN) models struggle with learning features of camouflaged, indirect multi-hop transactions.<n>We propose a novel High-order Graph Representation Learning model (HOGRL) to avoid incorporating excessive noise during the multi-layer aggregation process.
• Score: 11.174026504498931
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Credit card fraud imposes significant costs on both cardholders and issuing banks. Fraudsters often disguise their crimes, such as using legitimate transactions through several benign users to bypass anti-fraud detection. Existing graph neural network (GNN) models struggle with learning features of camouflaged, indirect multi-hop transactions due to their inherent over-smoothing issues in deep multi-layer aggregation, presenting a major challenge in detecting disguised relationships. Therefore, in this paper, we propose a novel High-order Graph Representation Learning model (HOGRL) to avoid incorporating excessive noise during the multi-layer aggregation process. In particular, HOGRL learns different orders of \emph{pure} representations directly from high-order transaction graphs. We realize this goal by effectively constructing high-order transaction graphs first and then learning the \emph{pure} representations of each order so that the model could identify fraudsters' multi-hop indirect transactions via multi-layer \emph{pure} feature learning. In addition, we introduce a mixture-of-expert attention mechanism to automatically determine the importance of different orders for jointly optimizing fraud detection performance. We conduct extensive experiments in both the open source and real-world datasets, the result demonstrates the significant improvements of our proposed HOGRL compared with state-of-the-art fraud detection baselines. HOGRL's superior performance also proves its effectiveness in addressing high-order fraud camouflage criminals.

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