Predicting Bad Debt Risk in Banks using Machine Learning
Chi Quynh Nguyen1, Ngoc Thi Bich Do2
1Chi Quynh Nguyen, Department of Computer Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
2Ngoc Thi Bich Do, Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
Manuscript received on 27 November 2025 | First Revised Manuscript received on 05 December 2025 | Second Revised Manuscript received on 07 December 2025 | Manuscript Accepted on 15 December 2025 | Manuscript published on 30 December 2025. | PP: 12-19 | Volume-15 Issue-1, December 2025 | Retrieval Number: 100.1/ijitee.A120515011225 | DOI: 10.35940/ijitee.A1205.15011225
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Bad Debt, Banking Finance, Credit Risk Prediction, Machine Learning.
Scope of the Article: Artificial Intelligence & Methods