Oversampling for class-imbalanced learning in credit risk assessment based on CVAE-WGAN-gp model

Credit risk assessment is a crucial task in bank risk management. By making lending decisions based on credit risk assessment results, banks can reduce the probability of non-performing loans. However, class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models. To address this issue, this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty (CVAE-WGAN-gp) model for oversampling, generating samples similar to the original default customer data to enhance model prediction performance. To evaluate the quality of the data generated by the CVAE-WGAN-gp model, we selected several bank loan datasets for experimentation. The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.