Credit Decision-making Modeling of Banks with Support Vector Machine on Empirical Samples from Chinese Listed Companies between 2001 - 2010

Lu-Ya      Lv; Fan      Shi; Xiao-Qin      Ni; Jie      Sun; Qing-Hua      Huang; Hui      Li

doi:10.2174/2213275911104010053

Abstract

Credit decision is one of the major businesses for banks, and it plays a vital role for a bank ’ s development. It is of vital importance to improve the identification accuracy of debtors financial situations, though there are many tools for financial data analysis. Support vector machine (SVM), a technique of artificial intelligence, has a relatively high accuracy for solving binary classification problems with small samples. This paper used ST (Special Treatment) together with an improvement of long-term loans for two consecutive years and non-ST (never under special treatment) of Chinese listed companies as two classes of financial situations and employed financial indicators as variables to construct SVM models for banks credit decision-making, along with the discussion of related recent patents. Our conclusion is that SVM has relatively higher classification accuracy than some other classifiers, including neural network, decision tree, and discriminant analysis. Therefore, application of SVM to bank credit decision-making is feasible and efficient. The review also discussed relevant patents.

Keywords: Chinese listed companies, classification accuracy, credit risk in banks, decision making, polynomial kernel, support vector machine (SVM), multivariate discriminant analysis (MDA), DATA PROCESSING, MODELING IMPLEMENTATION, SPSS software, Software Preparation, Data Grouping