A Graph Attentive Network Model for P2P Lending Fraud Detection

Fraud detection for peer-to-peer (P2P) lending is an important and challenging problem in both real application and research area. Different from existing methods which are mainly based on user demographic information, in this paper we study if other information such as user relationship represented by graph and transaction description information are helpful for the fraud detection problem. Meanwhile, attention mechanism is widely employed to explain how deep learning model works. However, existing studies don’t discriminate the importance of neighbors and different edge features in graph. In this paper, we propose a new graph attentive network model called ‘FDNE’ for P2P lending fraud detection based on text information and/or user relationship information. We design a novel attention method called edge-feature attention and use a global normalization operation to identify influential edge feature. Experiments conducted on a real dataset demonstrate that our model significantly outperforms other baselines and can make reasonable explanations simultaneously.