Noise Filtering and Feature Enhancement Based Graph Neural Network Method for Fraud Detection

Existing graph neural network (GNN)-based fraud detection methods have at least three shortcomings: (1) They do not adequately consider the problem of imbalanced distribution of sample labels. (2) They do not take into account the problem that fraudsters deliberately create noise to interfere with fraud detection in order to avoid detection by detectors. (3) They fail to consider the limitations of sparse connections for fraud data. To address these three shortcomings, this paper proposes a fraud detection method, called NFE-GNN (Noise Filtering and feature Enhancement based Graph Neural Network method for fraud detection), to improve the fraud detection performance. The proposed NFE-GNN method first employs a dataset-based fraud rate sampling technology to achieve a balance of benign and fraudulent samples. Based on this, a parameterized distance function is introduced to calculate the similarities between nodes, and the optimal noise filtering threshold is obtained through adaptive reinforcement learning. Finally, an effective algorithm is presented to increase the connections between fraudulent samples, and enrich the topology information in the graph to enhance the feature representation capability of fraudulent samples. The experimental results on two publicly available datasets demonstrate that the detection performance of the proposed NFE-GNN method is better than that of state-of-the-art graph neural network methods. © 2023 Chinese Institute of Electronics. All rights reserved.