Abstract
This study uses LGBM Regressor (Light Gradient Boosting Machine Regressor) algorithm in machine learning on python platform along with SHAP (Shapley Additive exPlanation) technique to extract information from machine learning model to evaluate the macro and micro factors affecting the provision for credit risks at commercial banks in Vietnam. Data was collected from 30 commercial banks in Vietnam from 2008 to 2020. Research results show that profitability, size, bad debt, credit balance, capital adequacy ratio, economic growth and unemployment rate have an impact on the provision for credit risk. From there, the article proposes some policy implications to limit credit risk at commercial banks in Vietnam.
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