A CWGAN-GP-based multi-task learning model for consumer credit scoring

Abstract

In consumer credit scoring practice, there is often an imbalanced distribution in accepted borrowers, which means there are far fewer defaulters than borrowers who pay on time. This makes it difficult for traditional models to function. Aside from traditional sampling methods for imbalanced data, the idea of using rejected information to one’s benefit is new. Without historical repayment performance, rejected data are often discarded or simply disposed of during credit scoring modeling. However, these data play an important role because they capture the distribution of the borrower population as well as the accepted data. Besides, due to the increasing complexity in loan businesses, the current methods have difficulties in addressing high-dimensional multi-source data. Thus, a more effective credit scoring approach towards imbalanced data should be studied. Inspired by the state-of-the-art neural network methods, in this paper, we propose a conditional Wasserstein generative adversarial network with a gradient penalty (CWGAN-GP)-based multi-task learning (MTL) model (CWGAN-GP-MTL) for consumer credit scoring. First, the CWGAN-GP model is employed to learn about the distribution of the borrower population given both accepted and rejected data. Then, the data distribution between good and bad borrowers is adjusted through augmenting synthetic bad data generated by CWGAN-GP. Next, we design an MTL framework for both accepted and rejected and good and bad data, which improves risk prediction ability through parameter sharing. The proposed model was evaluated on real-world consumer loan datasets from a Chinese financial technology company. The empirical results indicate that the proposed model performed better than baseline models across different evaluation metrics, demonstrating its promising application potential.