Nghiên cứu mô hình học máy dự đoán xác suất vỡ nợ của khách hàng doanh nghiệp tại ngân hàng thương mại cổ phần ở Việt Nam - Nguyễn Quốc, Hùng; Quan Toại, Mẫn; Trương Thị Minh, Lý (JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES, 2026)
Predicting the probability of customer default in the future using modern technologies is a growing trend in the overall financial industry, specifically in the banking sector. It is necessary for financial institutions to have timely solutions such as credit risk reduction, credit process analysis, and credit portfolio optimization. The paper utilizes data related to credit information, financial indicators, and corporate customer characteristics from the bank, employing quantitative research methods to collect and process data to build a machine-learning model. The result shows that the XGBoost model achieved the highest accuracy with an F1-score of 0.84, and the ROC curve had an AUC of 0.97. By utilizing these findings, the bank can implement the model into practice to facilitate business decision-making, enhance credit risk forecasting capabilities, improve operational efficiency, and mitigate undesirable losses.