ASA-GNN: Adaptive Sampling and Aggregation-Based Graph Neural Network for Transaction Fraud Detection

Many machine learning methods have been proposed to achieve accurate transaction fraud detection, which is essential to the financial security of individuals and banks. However, most existing methods either leverage original features only or require manual feature engineering so that they show a weak ability to learn discriminative representations from transaction data. Moreover, criminals often commit fraud by imitating cardholders' behaviors, which causes the poor performance of existing detection models. In this article, we propose an adaptive sampling and aggregation-based graph neural network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Specifically, we use cosine similarity and edge weights to adaptively select neighbors with similar behavior patterns for target nodes and then find multihop neighbors for fraudulent nodes. A neighbor diversity metric is designed by calculating the entropy of neighbors to tackle the camouflage issue of fraudsters and explicitly alleviate the oversmoothing phenomena. Extensive experiments on three real financial datasets demonstrate that ASA-GNN outperforms state-of-the-art ones.