Abstract
Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study used three strategies to construct the hybrid FSVM-based credit scoring models to evaluate the applicant's credit score from the applicant's input features. (1) using CART to select input features, (2) using MARS to select input features, (3) using GA to optimize model parameters. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the hybrid FSVM-based model not only has the best classification, but also has the lower type II error.
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