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Credit risk assessment model for Jordanian commercial banks: Neural scoring approach

Despite the increase in the number of non-performing loans and competition in the banking market, most of the Jordanian commercial banks are reluctant to use data mining tools to support credit decisions. Artificial neural networks represent a new family of statistical techniques and promising data...

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Main Authors: Bekhet H.A., Eletter S.F.K.
Other Authors: 37100908800
Format: Article
Published: Elsevier 2023
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spelling my.uniten.dspace-221362023-05-16T10:47:43Z Credit risk assessment model for Jordanian commercial banks: Neural scoring approach Bekhet H.A. Eletter S.F.K. 37100908800 55539589600 Despite the increase in the number of non-performing loans and competition in the banking market, most of the Jordanian commercial banks are reluctant to use data mining tools to support credit decisions. Artificial neural networks represent a new family of statistical techniques and promising data mining tools that have been used successfully in classification problems in many domains. This paper proposes two credit scoring models using data mining techniques to support loan decisions for the Jordanian commercial banks. Loan application evaluation would improve credit decision effectiveness and control loan office tasks, as well as save analysis time and cost. Both accepted and rejected loan applications, from different Jordanian commercial banks, were used to build the credit scoring models. The results indicate that the logistic regression model performed slightly better than the radial basis function model in terms of the overall accuracy rate. However, the radial basis function was superior in identifying those customers who may default. © 2014 Africagrowth Institute. Final 2023-05-16T02:47:43Z 2023-05-16T02:47:43Z 2014 Article 10.1016/j.rdf.2014.03.002 2-s2.0-84901622845 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901622845&doi=10.1016%2fj.rdf.2014.03.002&partnerID=40&md5=c5982ef60dc374c48822b72d0ff8d3d3 https://irepository.uniten.edu.my/handle/123456789/22136 4 1 20 28 All Open Access, Hybrid Gold Elsevier Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Despite the increase in the number of non-performing loans and competition in the banking market, most of the Jordanian commercial banks are reluctant to use data mining tools to support credit decisions. Artificial neural networks represent a new family of statistical techniques and promising data mining tools that have been used successfully in classification problems in many domains. This paper proposes two credit scoring models using data mining techniques to support loan decisions for the Jordanian commercial banks. Loan application evaluation would improve credit decision effectiveness and control loan office tasks, as well as save analysis time and cost. Both accepted and rejected loan applications, from different Jordanian commercial banks, were used to build the credit scoring models. The results indicate that the logistic regression model performed slightly better than the radial basis function model in terms of the overall accuracy rate. However, the radial basis function was superior in identifying those customers who may default. © 2014 Africagrowth Institute.
author2 37100908800
author_facet 37100908800
Bekhet H.A.
Eletter S.F.K.
format Article
author Bekhet H.A.
Eletter S.F.K.
spellingShingle Bekhet H.A.
Eletter S.F.K.
Credit risk assessment model for Jordanian commercial banks: Neural scoring approach
author_sort Bekhet H.A.
title Credit risk assessment model for Jordanian commercial banks: Neural scoring approach
title_short Credit risk assessment model for Jordanian commercial banks: Neural scoring approach
title_full Credit risk assessment model for Jordanian commercial banks: Neural scoring approach
title_fullStr Credit risk assessment model for Jordanian commercial banks: Neural scoring approach
title_full_unstemmed Credit risk assessment model for Jordanian commercial banks: Neural scoring approach
title_sort credit risk assessment model for jordanian commercial banks: neural scoring approach
publisher Elsevier
publishDate 2023
_version_ 1806425543431684096
score 13.211869

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