Abstract
A loan is vital for individuals and organizations to meet their goals. However, financial institutions face challenges like managing losses and missed opportunities in loan decisions. A key issue is the imbalanced datasets in credit risk assessment, hindering accurate predictions of defaulters. Previous research has utilized machine learning techniques, including single or multiple classifier systems, ensemble methods, and class-balancing approaches. This review summarizes various factors and machine learning methods for assessing credit risk, presented in a tabular format to provide valuable insights for researchers. It covers data complexity, minority class distribution, sampling techniques, feature selection, and meta-learning parameters. The goal is to help develop novel algorithms that outperform existing methods. Even a slight improvement in defaulter prediction rates could significantly influence society by saving millions for lenders.