Credit rating of bank customers using a new ensemble method based on support vector machine (Case study of Pasargad bank)

In recent years, the issue of credit rating and identification of good and bad customers have received a lot of attention from banks. Granting facilities to well-accounted customers and avoiding granting facilities to badly accounted customers, which leads to reducing bank arrears, are always the major concerns of bank managers, which are not out of reach with the help of an efficient and good credit rating system. This paper presents a new ensemble model based on support vector machine algorithm for the credit rating of bank customers. First, the data set is divided into several subsets by a bootstrap approach. The support vector machine algorithm is then applied to each subset and several models are formed. At the end, voting is done between the models, and the final model is obtained. In order to show the accuracy of the ensemble model, the credit data of Pasargad Bank’s costumers including 2218 instances of credit applicants, each instance contains 14 explanatory attribute, are evaluated using the proposed method. Based on different criteria, the results obtained on the data of the Pasargad Bank’s costumers confirm the superiority of the ensemble support vector machine method over the usual support vector machine method and the random forest method. The type II error (indicates the proportion of bad applicants who are wrongly predicted to be good applicants) of the proposed ensemble method with linear core is 17% less than the usual support vector machine method and 15% less than the random forest method.