Forecasting credit default risk with graph attention networks

Abstract

The importance of credit default risk management has risen that companies can utilize it to identify and forecast future credit default risk. Several approaches have been proposed, however, they paid little attention on the various underlying relationships between users, which can provide significant improvement. In this paper, we propose a Graph Attention Network (GAT)-based model for predicting credit default risk, leveraging various types of data, including credit default history, credit status and personal profile. These data provide a comprehensive representation of users’ overall status, including historical financial credit, recent financial credit and wealth status. Different graphs are constructed based on the similarities between users using these data, respectively. Then, for graphs, GAT modules are used to capture both the relationships with adjacent and high-order neighbors, as well as the linear and non-linear relationships. After fusing learned high-level features from GAT modules, final predictive results, whether users will default or not, are predicted. The effectiveness of our prediction model is validated using real-world datasets, and experimental results depict that our model can accurately predict credit default risks, outperforming several baseline methods. The codes and datasets are freely available at https://github.com/ZJUDataIntelligence/Foreknow.