Support Vector Machine Applications In The Personal Credit Rating

Personal credit scoring is an important part of commercial banks' risk management. In the last 50 years, many credit scoring methods have been developed by foreign banks. Support Vector Machine(SVM) is a new machine learning method developed in recent years on the foundation of statistical learning theory. The focus of this thesis is to apply SVM on Crediting Scoring. In this paper, Genetic Algorithm was used to choose the optimal input feature subset and set the best kernel parameters simultaneously, establishing a credit scoring model named GA-SVM. In addition, SVM was applied as the basic learning machine of AdaBoost algorithm, establishing another credit scoring model named AdaBoost-SVM. Experimental results have shown that AdaBoost-SVM is better than GA-SVM, which is better than the usual SVM.The main job of this paper are following:1,The traditional methods of credit scoring prefer to do feature selection and parameters optimization independently. The correlation between them is not considered, prohibiting the global optimal results. This paper tries to combine feature selection with parameter optimation based on genetic algorithm during SVM modeling.2,Dynamic Boosting has been coupled with SVM to established a AdaBoost-SVM. Traditional AdaBoost prefers to use the identical learning machine during the boosting process. In this paper, we design a parameter adjusting strategy to get different and moderately accurate SVM component classifier for boosting. And good results have been obtained on benchmark data sets.