Feature enhanced ensemble modeling with voting optimization for credit risk assessment

The ChinaZJB dataset consists of 1,329 valid samples of SMEs after merging the non-financial behavioral information and soft information on credit rating with the financial information, loan information, and non-financial basic information found in the annual loan ledger data. Among them, 108 SMEs have default records, while 1,221 SMEs have no default records, resulting in an imbalanced ratio of approximately 1:11.Five datasets from the UC Irvine (UCI) machine-learning repository, that is, the Polish 1, Polish 2, Polish 3 , Australian, and Taiwan credit datasets, were used for robustness checks.