A Support Vector Machine Based Method for Credit Risk Assessment

The credit card industry has been growing rapidly in recent years, and credit risk assessment becomes critically important for financial companies. In this paper, a novel support vector machine (SVM) based ensemble model is proposed for credit risk assessment. In the proposed method, principles component analysis (PCA) is firstly employed for credit feature selection. Secondly, SVMs with different kernels are trained by using genetic algorithm (GA) to optimize the parameters, and the corresponding assessment results are obtained. Thirdly, all results produced by different SVMs are combined by several ensemble strategies. Finally, an optimal ensemble strategy is selected for credit scoring. For validation, two real world credit datasets are used to test the effectiveness and efficiency of our proposed method. The experiment results find that our proposed ensemble model outperforms commonly used credit scoring tools. The findings of the study reveal the support vector machine based ensemble method to be a promising alternative for credit scoring.