The increasing complexity and dynamic nature of financial data present significant challenges in accurately predicting credit risk, a critical task in the banking and finance sector. The application of machine learning (ML) in credit risk prediction has been hindered by the imbalanced nature of credit datasets. This study proposes an improved approach for predicting credit risk using a stacked ensemble method combined with a hybrid data resampling technique. The ensemble comprises random forests, logistic regressi…