  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 leverage original
features only or require manual feature engineering. They lack the 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 paper, 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 leverage cosine
similarity and edge weights to adaptively select neighbors with similar
behavior patterns for target nodes and then find multi-hop neighbors for
fraudulent nodes. A neighbor diversity metric is designed by calculating the
entropy among neighbors to tackle the camouflage issue of fraudsters and
explicitly alleviate the over-smoothing phenomena. Extensive experiments on
three real financial datasets demonstrate that the proposed method ASA-GNN
outperforms state-of-the-art ones.
