Financial fraud refers to the act of obtaining financial benefits through dishonest means. Such acts not only disrupt the order of the financial market, but also harm social and economic development, and breed other illegal and criminal acts. With the proliferation of the internet and online payment methods, many fraudulent activities and money laundering have shifted from offline to online, posing a significant challenge for regulators. In this work, we proposed a novel detection model by utilizing the graph neural network. Specifically, the general model structure includes the combination of Graph Convolutional Network (GCN) and Graph Attention Network (GAT), which can effectively capture the complex relationships and features in the financial transaction network. By building a multilayer graph neural network, the model can perform deep learning on the implicit patterns between nodes, thereby improving the accuracy of fraud detection. Experimental analysis results show that the performance of the proposed method on multiple public datasets is better than that of traditional methods, showing its potential in practical application.