Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning

TL;DR

This paper introduces MimbFD, a dual-view graph learning method that addresses message imbalance in fraud detection by enhancing topology-aware representations and debiasing node features, leading to improved detection accuracy.

Contribution

It proposes a novel dual-view approach with topology and debiasing modules to mitigate message imbalance in GNN-based fraud detection, a problem caused by fraudsters' obfuscation tactics.

Findings

MimbFD outperforms existing methods on three public datasets.

The topology module improves node representation quality.

The debiasing module balances class influence effectively.

Abstract

Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improved neighborhood knowledge capture. However, the focus on local interactions leads to imbalanced transmission of global topological information and increased risk of node-specific information being overwhelmed during aggregation due to the imbalance between fraud and benign nodes. In this paper, we first summarize the impact of topology and class imbalance on downstream tasks in GNN-based fraud detection, as the problem of imbalanced supervisory messages is caused by fraudsters' topological behavior obfuscation and identity feature concealment. Based on statistical validation, we propose a novel dual-view graph representation learning method to mitigate Message imbalance in Fraud Detection (MimbFD). Specifically, we design a topological message reachability module for high-quality node representation learning to penetrate fraudsters' camouflage and alleviate insufficient propagation. Then, we introduce a local confounding debiasing module to adjust node representations, enhancing the stable association between node representations and labels to balance the influence of different classes. Finally, we conducted experiments on three public fraud datasets, and the results demonstrate that MimbFD exhibits outstanding performance in fraud detection.