This paper focuses on feature selection methods for support vector machine (SVM) classifiers, checking their optimality by comparing them with some statistical and baseline methods. To achieve the above objective, we exploit twelve feature selection methods from the family of filters and embedded approaches by splitting a Chinese database. Our findings suggest that the average result from sample division cases will achieve a more robust prediction ability than that from “no sample division” cases. Moreover, ridge regression (SVM9) in training and “average results from sample division” data sets, along with DTQUEST (SVM7) in “no sample division” example sets, give outstanding performance with respect to all performance criteria. With these contributions, therefore, our paper complements previous evidence and modernizes the methods of feature selection to render SVM classifiers favorable for credit approval data modeling. This study has practical implications for financial institutions, managers, employees, investors and government officials looking to sort out forthcoming lending transactions to attain a target risk/return trade-off.