Credit Facilities Applicants Classification by SVM

Subject Areas : Futurology

A. Toloei ashlaghi ¹ , H. Nikoomaram ² , F. i Maghdoori sharabian ³

1 - نویسنده مسئول یا طرف مکاتبه
2 - ندارد
3 - ندارد

Received: 2008-11-17 Accepted : 2009-01-15 Published : 2010-03-21

Keywords: data mining, SVM, character selection,

Abstract :

In the banking industry, one issue that must always be considered by the credit policy makers is riskmanagement. Among various risks which banks are dealing with, credit risk is most important. It iscaused by the losses of disability or lack of tendency of borrowers to pay their credit obligations. Tomanage and control the mentioned risk, classification systems are undeniable requirement. Suchsystems, according to existent documents and information, determine the class of customers. It isevident that use of these systems helps bank to choose customers in a good way and through thecontrol and reduction the credit risk, improves efficiency level of providing bank facilities. In Thisresearch artificial intelligent based classification model consist of support vector machine is used topredict bank legal customers financial performance. Indeed, in this paper SVM is used with othermechanisms like F-score and Grid search to increase the accuracy of the model and classify the legalcustomers. The results justify the improvements in the classification accuracy and demonstrate thatSVM can provide the better accuracy than other models