Predicting non-performing loans in Vietnam’s financial sector: a deep Q-learning approach

Abstract

Non-performing loans (NPLs) prediction is a very important task in risk management of financial institutions. NPLs often lead to substantial losses when loans are not paid back on time. While traditional machine learning (ML) models have been conventionally exploited for credit risk assessment, they frequently face challenges with handling imbalanced data. To deal with this problem, this paper introduces a novel approach using deep reinforcement learning (DRL), specifically deep Q-learning, to enhance the prediction of NPLs. To verify the effectiveness of the method, we introduce a new dataset comprising 83,732 customer records (each described with 22 key features) from one of Vietnam's largest financial entities. Our method is compared with standard ML techniques such as random forest, decision tree, logistic regression, support vector machine, LightGBM, and XGBoost. Experimental results on this dataset demonstrate that deep Q-learning outperforms these traditional models in handling imbalanced data and boosting prediction accuracy. This research highlights the potential of DRL as a robust risk management tool, helping financial institutions make credit assessments more efficiently and reducing decision-making costs.