Credit risk assessment of corporate customers using support vector machine and genetic algorithm hybrid model - a Case Study of Tejarat Bank

Subject Areas : Financial engineering

Received: 2016-02-24 Accepted : 2016-04-26 Published : 2016-06-04

Keywords: genetic algorithms, Credit Risk, Credit rating, Support vector machine,

Abstract :

Design and implementation of credit rating model in the banking system plays an important role in enhancing the efficiency of resource allocation is to target customers. In this research aims to develop a model for evaluating the credit risk of the bank's corporate clients have been used Support Vector Machine (SVM) and Genetic Algorithms (GA). Therefore, a study has been on the financial variables of 282 companies during the years 2007 to 2010, have received loans from TEJARAT bank. In this research, to optimize the input of support vector machine is used of genetic algorithms. The power of the genetic algorithm to select the optimum points, always provides confidence that the optimal-made for the proposed going to be higher optimum points. In the hybrid model GA-SVM, genetic algorithm optimizes SVM model inputs the data. Research findings show GA-SVM hybrid model performed better than the SVM model in the identifying good customer accounts and bad customer accounts and credit risk prediction.