Temporal-Aware Graph Neural Network for Credit Risk Prediction

Abstract

Credit risk prediction is a fundamental problem for most financial institutions. Previous methods mainly adopt users' individual features on a single snapshot. However, users' individual features on financial platforms are usually too sparse to be informative. And previous methods ignore that the features, the behaviours and the credit risk of the users are all dynamic. To resolve the problems, we aim to model the credit risk prediction on dynamic graphs and propose a Temporal-Aware Graph Neural Network (TemGNN) to predict user credit risk. In detail, the model consists of three parts: i) a static model to extract the user's static factors regarding the credit risk. ii) a short-term graph encoder with special graph convolution modules for each snapshot to enrich the user's information through aggregating short-term temporal and structural information. iii) a long-term temporal model based on LSTM with interval-decayed attention to adaptively aggregate the long-term information from the static factors and interval-irregular dynamic snapshots. By combining the three parts together, our model is able to mine both the short-and long-term temporal-structural information. Experimentally, we use the users' authorized lending behaviours as the temporal graphs to do default prediction on Alipay. The results show that our model achieves the best performance among the state-of-the-art methods.

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