Study on digital financial fraud risk identification based on heterogeneous graph convolutional attention network

To enhance the accuracy of digital financial fraud risk identification and reduce the identification time, this paper introduces a digital financial fraud risk identification method utilising a heterogeneous graph convolutional attention network. Initially, financial business data is gathered and processed for data imbalance using generative adversarial networks. Subsequently, extreme learning machines are employed to extract spatially correlated features among various transactions. Following this, a robust graph convolutional attention network is constructed, a risk identification function is designed, and ultimately, the fraud data is fed into the graph convolutional neural network for training. The output data is categorised by transaction type to ascertain the presence of digital financial fraud risk. The results indicate that our method achieves a recognition accuracy exceeding 96%, with time consumption not surpassing 8.5 s, demonstrating that our method exhibits excellent recognition performance.