UCLA

Graph neural network (GNN) is one of the most widely used methods that leverage relational information in the data to learn and make predictions. Fraud detection is a challenging task considering the nature of fraudulent transactions which changes drastically from one case to another as fraudsters often collude to hide their abnormal behavior/features. To this end, GNN has a fitting application because it leverages graph structure to learn relational information to distinguish malicious transactions from legitimate ones. This study implements various GNNs such as graph convolution network (GCN), graph attention network (GAT), and modified GAT to predict fraudulent Bitcoin transactions. It focuses on benchmarking the results of two versions of GAT against GCN to demonstrate the superior predictive power of the attention mechanism. The two versions include conventional GAT and modified GAT, the latter consists of a dynamic attention mechanism. The two versions of the GAT model are also compared in detail. GNN has been used to detect fraud or anomalies for various practical implementations such as financial transactions, credit cards, and customer reviews. However, a detailed study focusing on the two versions of GAT and benchmarking it against GCN has not yet been conducted. We show that GAT has an enhanced ability to predict fraudulent transactions. The excellent predictive performance of GAT gives a clear indication that it could play a vital role in detecting broader cryptocurrency fraud. Finally, this study discusses the challenges of building an explainable GNN models.