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.

信用违约风险管理的重要性已经上升，公司可以利用它来识别和预测未来的信用违约风险。已经提出了几种方法，但是，他们很少关注用户之间的各种潜在关系，这可以提供显着的改进。在本文中，我们提出了一种基于图注意力网络（GAT）的模型，利用各​​种类型的数据（包括信用违约历史、信用状况和个人资料）来预测信用违约风险。这些数据全面反映了用户的整体状况，包括历史金融信用、近期金融信用和财富状况。根据使用这些数据的用户之间的相似性，分别构建不同的图表。然后，对于图，GAT 模块用于捕获与相邻和高阶邻居的关系，以及线性和非线性关系。融合从 GAT 模块学习到的高级特征后，可以预测最终的预测结果，无论用户是否会违约。我们的预测模型的有效性使用真实世界的数据集进行了验证，实验结果表明我们的模型可以准确地预测信用违约风险，优于几种基线方法。代码和数据集可在https://github.com/ZJUDataIntelligence/Foreknow免费获取。