A granular computing-based approach to credit scoring modeling

Saberi, M; Mirtalaie, MS; Hussain, FK; Azadeh, A; Hussain, OK; Ashjari, B

A granular computing-based approach to credit scoring modeling

Saberi, M

Mirtalaie, MS Hussain, FK

Azadeh, A Hussain, OK Ashjari, B

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2013, 122 pp. 100 - 115
Issue Date:: 2013-12-25

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Field	Value	Language
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078	en_US
dc.contributor.author	Mirtalaie, MS	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.contributor.author	Azadeh, A	en_US
dc.contributor.author	Hussain, OK	en_US
dc.contributor.author	Ashjari, B	en_US
dc.date.issued	2013-12-25	en_US
dc.identifier.citation	Neurocomputing, 2013, 122 pp. 100 - 115	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26573
dc.description.abstract	The credit card industry has been growing rapidly and thus huge numbers of consumers' credit data are collected by the credit department of the banks. The credit scoring managers often evaluate the consumer's credit with intuitive experience. However, with the support of the credit classification models, the managers can accurately evaluate the applicants' credit score. In this study, a neurocomputing-based granular approach is proposed to model credit scoring. Granular computing is used to compute the size of training and testing groups. Artificial neural networks (ANN) and data envelopment analysis (DEA) are used to model credit lending decisions in the online and offline manner, respectively. Proposed method is composed of three distinct stages based on trust and credibility concept. Trust is introduced and modeled via ANN in online module. Also credibility is modeled via DEA in offline module in present study. This paper is a pioneer in examining the concept of granularity for selecting the optimum size of testing and training group in machine learning area. In addition, proposing flexible trust ranges comparing to the current constant ones will support the importance of customers with higher credit scores to financial markets. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card data set obtained from the UCI repository. © 2013 Elsevier B.V.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2013.05.020	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A granular computing-based approach to credit scoring modeling	en_US
dc.type	Journal Article
utslib.citation.volume	122	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	122	en_US

Abstract:

The credit card industry has been growing rapidly and thus huge numbers of consumers' credit data are collected by the credit department of the banks. The credit scoring managers often evaluate the consumer's credit with intuitive experience. However, with the support of the credit classification models, the managers can accurately evaluate the applicants' credit score. In this study, a neurocomputing-based granular approach is proposed to model credit scoring. Granular computing is used to compute the size of training and testing groups. Artificial neural networks (ANN) and data envelopment analysis (DEA) are used to model credit lending decisions in the online and offline manner, respectively. Proposed method is composed of three distinct stages based on trust and credibility concept. Trust is introduced and modeled via ANN in online module. Also credibility is modeled via DEA in offline module in present study. This paper is a pioneer in examining the concept of granularity for selecting the optimum size of testing and training group in machine learning area. In addition, proposing flexible trust ranges comparing to the current constant ones will support the importance of customers with higher credit scores to financial markets. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card data set obtained from the UCI repository. © 2013 Elsevier B.V.

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http://hdl.handle.net/10453/26573