Informative patterns for credit scoring: Support vector machines preselect data subsets for linear discriminant analysis

Pertinent statistical methods for credit scoring can be very simple like e.g. linear discriminant analysis (LDA) or more sophisticated like e.g. support vector machines (SVM). There is mounting evidence of the consistent superiority of SVM over LDA or related methods on real world credit scoring problems. Methods like LDA are preferred by practitioners owing to the simplicity of the resulting decision function and owing to the ease of interpreting single input variables. Can one productively combine SVM and simpler methods? To this end, we use SVM as the preselection method. This subset preselection results in a final classification performance consistently above that of the simple methods used on the entire data.