Predicting Bad Debt Risk in Banks using Machine Learning

Abstract

This study presents the construction of a credit risk prediction model to improve the effectiveness of risk management at credit institutions. The urgency of the study is underscored by the internal bad-debt ratio of the Vietnamese banking system increasing by nearly 3.4 times by the end of 2023, while the cost of credit risk provisioning rose by 40% compared to 2022. The key challenge is to address a severe data imbalance (bad-debt accounts for 1-5%). Advanced data preprocessing techniques are applied, including handling missing values with the miceforest library and feature selection using Mutual Information combined with Correlation. The key experimental solution is the Mixture of Experts (MoE) Model, using Stratified K-Fold to train experts on 1:1-balanced data. The results show that the MoE model achieves the highest performance with a Recall of 0.87 and an F1-score of 0.79, outperforming the classical Machine Learning models. Applying the model achieves 85-90% forecasting accuracy, optimises the credit process, reduces appraisal time by 25-30%, and supports the sustainable development of the financial system.