EconPapers    
Economics at your fingertips  
 

A comparison of data mining techniques for credit scoring in banking: A managerial perspective

Huseyin Ince and Bora Aktan ()

Journal of Business Economics and Management, 2009, vol. 10, issue 3, 233-240

Abstract: Credit scoring is a very important task for lenders to evaluate the loan applications they receive from consumers as well as for insurance companies, which use scoring systems today to evaluate new policyholders and the risks these prospective customers might present to the insurer. Credit scoring systems are used to model the potential risk of loan applications, which have the advantage of being able to handle a large volume of credit applications quickly with minimal labour, thus reducing operating costs, and they may be an effective substitute for the use of judgment among inexperienced loan officers, thus helping to control bad debt losses. This study explores the performance of credit scoring models using traditional and artificial intelligence approaches: discriminant analysis, logistic regression, neural networks and classification and regression trees. Experimental studies using real world data sets have demonstrated that the classification and regression trees and neural networks outperform the traditional credit scoring models in terms of predictive accuracy and type II errors.

Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.3846/1611-1699.2009.10.233-240 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jbemgt:v:10:y:2009:i:3:p:233-240

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TBEM20

Access Statistics for this article

Journal of Business Economics and Management is currently edited by Izolda Joksiene, Romualdas Ginevicius and Ieva Meidute

More articles in Journal of Business Economics and Management from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2019-06-04
Handle: RePEc:taf:jbemgt:v:10:y:2009:i:3:p:233-240