Attempt of Graph Neural Network Algorithm in the Field of Financial Anomaly Detection

In recent years, the development of mobile internet and big data has propelled the digital transformation of the finance industry, enabling inclusive finance across society. Meanwhile, financial fraud detection technologies need continuous updates and improvements to combat new fraud tactics. Currently, the development of anomaly detection techniques in the traditional finance industry lags behind the pace of financial digitalization. In addition, emerging technologies lack practical application in financial scenarios. Therefore, we test the anomaly detection performance of two graph neural networks, GCN and GAT, on the DGraph dataset and compare with the MLP model. Experiments demonstrate that graph neural networks outperform the fully connected network and achieve good performance on financial anomaly detection.