Cross-Domain Credit Default Prediction via Interpretable Ensemble Transfer

Abstract

The evaluation and prediction of credit risk have always been a research hotspot to ensure the healthy and orderly development of the credit market. Most researchers use deep learning to predict credit risk. However, when training data are too small, deep learning models often lead to overfitting. Although we have a large amount of available training data, we often cannot ensure that the data are evenly distributed, which is still not conducive to model training. In addition, deep learning is often difficult to explain, and the unexplained model is often difficult to gain the trust of users, thus reducing the usefulness of the model. To solve these problems, we propose an integrated cross-domain credit default prediction network, called Transfer Light Gradient Boosting Machine (TrLightGBM), based on interpretable integration transfer. This network considers the weight of data from different domains in training and implements cross-domain credit default prediction by adjusting the weight. The experiment shows that our method TrLightGBM not only achieves the interpretability of the model to a certain extent but also has good performance.

Keywords

• credit default prediction
• interpretable model
• cross-domain
• ensemble learning
• decision tree