A Study of Personal Credit Scoring Models on Support Vector Machine with Optimal Choice of Kernel Function Parameters
基于支持向量机的个人信用评估模型及最优参数选择研究

As credit industry has expanded rapidly over last several years,credit scoring models have drawn a lot of research interests in previous literature.Recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones.This paper applies support vector machines(SVMs) to the credit scoring prediction problem in an attempt to suggest a new model with better classification accuracy.To serve this purpose,we use a grid search technique using 5-fold cross-validation to find out the optimal parameter values of various kernel function of SVM.In addition,to evaluate the prediction accuracy of SVM,we compare its performance with those of linear discriminant analysis(LDA),logistic regression analysis(Logit),K-nearest neighbours(K-NN),classification and regression tree and neural networks(ANN).The experiment results show that SVM have a very good prediction accuracy.