Comparison Between Neural Network, Genetic Algorithm and Logit Models in Evaluating Consumer Credit Risk

Fethullah Tari, seyed Ahmad Ebrahimi, Seyed Jafar Mousavi and Mahmoud Kalantari
Additional contact information
Fethullah Tari: Allame Tabatabaei University
seyed Ahmad Ebrahimi: Researcher at National Research Institute For Science Policy
Seyed Jafar Mousavi: Entrepreneurship Education
Mahmoud Kalantari: Iran University of Economic Science

Journal of Monetary and Banking Research (فصلنامه پژوهش‌های پولی-بانکی), 2018, vol. 10, issue 34, 680-657

Abstract: The purpose of this study is to assess the credit rating methods of real customers (micro-credit recipients) of banks, by reviewing the financial records and characteristics of the applicant's characteristics. In this research, the effectiveness of some methods (logit model, neural network, and genetic algorithm) is evaluated for accurate measurement

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://jmbr.mbri.ac.ir/article-1-578-en.pdf (application/pdf)
http://jmbr.mbri.ac.ir/article-1-578-en.html (text/html)
http://jmbr.mbri.ac.ir/article-1-578-fa.html (text/html)

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:mbr:jmbres:v:10:y:2018:i:34:p:680-657

Access Statistics for this article

More articles in Journal of Monetary and Banking Research (فصلنامه پژوهش‌های پولی-بانکی) from Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran
Bibliographic data for series maintained by P. R. ().