Research on Fincial Fraud Detection Based on Deep Graph Neural Network

Financial fraud encompasses a range of dishonest practices that are designed to gain financial benefits, with the potential to adversely affect market stability, economic growth, and foster additional criminal activities. The shift of fraudulent practices to online platforms has made the detection of such activities increasingly challenging. This study introduces a graph neural network-based detection model combining Graph Convolutional Network (GCN) and Graph Attention Network (GAT), which enables the capture of intricate relationships within financial transactions. The model improves the accuracy of fraud detection by leveraging multilayer deep learning on complex patterns. The experimental results across various datasets demonstrate superior performance compared to traditional methods, suggesting strong potential for practical applications.