Analysis of Methods for Generating Classification Rules Applicable to Credit Risk

Autores
Jimbo Santana, Patricia; Villa Monte, Augusto; Rucci, Enzo; Lanzarini, Laura Cristina; Fernández Bariviera, Aurelio
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Credit risk is defined as the probability of loss due to non-compliance by the borrower with the required payments in relation to any type of debt. When financial institutions select their customers correctly, they can reduce their credit risk. To achieve this, they use various classification methodologies to sort customers based on their risk, analyzing a set of variables such as reputation, leverage, income and so forth. The extensive analysis and processing of these variables is quite time-consuming, partly because the data to be analyzed are not homogeneous. In this paper, we present an alternative method that operates on nominal and numeric attributes, which allows obtaining a predictive model that uses a reduced set of classification rules aimed at reducing credit risk. When the number of rules used decreases, credit analysts need less time to make their decisions, which will also result in better customer service. The methodology proposed here was applied to two databases of the UCI repository and two real databases of Ecuadorian banks that grant various types of credit. The results obtained have been satisfactory. Finally, our conclusions are discussed and future research lines are suggested.
Fil: Jimbo Santana, Patricia. Universidad Central del Ecuador; Ecuador
Fil: Villa Monte, Augusto. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
Fil: Rucci, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
Fil: Lanzarini, Laura Cristina. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
Fil: Fernández Bariviera, Aurelio. Universitat Rovira I Virgili; España
Materia
Classification rules
Credit scoring
Competitive Neural Networks
Particle Swarm Optimization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/57326
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Analysis of Methods for Generating Classification Rules Applicable to Credit RiskJimbo Santana, PatriciaVilla Monte, AugustoRucci, EnzoLanzarini, Laura CristinaFernández Bariviera, AurelioClassification rulesCredit scoringCompetitive Neural NetworksParticle Swarm Optimizationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Credit risk is defined as the probability of loss due to non-compliance by the borrower with the required payments in relation to any type of debt. When financial institutions select their customers correctly, they can reduce their credit risk. To achieve this, they use various classification methodologies to sort customers based on their risk, analyzing a set of variables such as reputation, leverage, income and so forth. The extensive analysis and processing of these variables is quite time-consuming, partly because the data to be analyzed are not homogeneous. In this paper, we present an alternative method that operates on nominal and numeric attributes, which allows obtaining a predictive model that uses a reduced set of classification rules aimed at reducing credit risk. When the number of rules used decreases, credit analysts need less time to make their decisions, which will also result in better customer service. The methodology proposed here was applied to two databases of the UCI repository and two real databases of Ecuadorian banks that grant various types of credit. The results obtained have been satisfactory. Finally, our conclusions are discussed and future research lines are suggested.Fil: Jimbo Santana, Patricia. Universidad Central del Ecuador; EcuadorFil: Villa Monte, Augusto. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; ArgentinaFil: Rucci, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; ArgentinaFil: Lanzarini, Laura Cristina. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; ArgentinaFil: Fernández Bariviera, Aurelio. Universitat Rovira I Virgili; EspañaUniversidad Nacional de La Plata. Facultad de Informática2017-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/57326Jimbo Santana, Patricia; Villa Monte, Augusto; Rucci, Enzo; Lanzarini, Laura Cristina; Fernández Bariviera, Aurelio; Analysis of Methods for Generating Classification Rules Applicable to Credit Risk; Universidad Nacional de La Plata. Facultad de Informática; Journal of Computer Science & Techonology; 17; 1; 4-2017; 20-281666-6046CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/JCST/article/view/521info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:36:13Zoai:ri.conicet.gov.ar:11336/57326instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:36:13.439CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Analysis of Methods for Generating Classification Rules Applicable to Credit Risk
title Analysis of Methods for Generating Classification Rules Applicable to Credit Risk
spellingShingle Analysis of Methods for Generating Classification Rules Applicable to Credit Risk
Jimbo Santana, Patricia
Classification rules
Credit scoring
Competitive Neural Networks
Particle Swarm Optimization
title_short Analysis of Methods for Generating Classification Rules Applicable to Credit Risk
title_full Analysis of Methods for Generating Classification Rules Applicable to Credit Risk
title_fullStr Analysis of Methods for Generating Classification Rules Applicable to Credit Risk
title_full_unstemmed Analysis of Methods for Generating Classification Rules Applicable to Credit Risk
title_sort Analysis of Methods for Generating Classification Rules Applicable to Credit Risk
dc.creator.none.fl_str_mv Jimbo Santana, Patricia
Villa Monte, Augusto
Rucci, Enzo
Lanzarini, Laura Cristina
Fernández Bariviera, Aurelio
author Jimbo Santana, Patricia
author_facet Jimbo Santana, Patricia
Villa Monte, Augusto
Rucci, Enzo
Lanzarini, Laura Cristina
Fernández Bariviera, Aurelio
author_role author
author2 Villa Monte, Augusto
Rucci, Enzo
Lanzarini, Laura Cristina
Fernández Bariviera, Aurelio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Classification rules
Credit scoring
Competitive Neural Networks
Particle Swarm Optimization
topic Classification rules
Credit scoring
Competitive Neural Networks
Particle Swarm Optimization
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Credit risk is defined as the probability of loss due to non-compliance by the borrower with the required payments in relation to any type of debt. When financial institutions select their customers correctly, they can reduce their credit risk. To achieve this, they use various classification methodologies to sort customers based on their risk, analyzing a set of variables such as reputation, leverage, income and so forth. The extensive analysis and processing of these variables is quite time-consuming, partly because the data to be analyzed are not homogeneous. In this paper, we present an alternative method that operates on nominal and numeric attributes, which allows obtaining a predictive model that uses a reduced set of classification rules aimed at reducing credit risk. When the number of rules used decreases, credit analysts need less time to make their decisions, which will also result in better customer service. The methodology proposed here was applied to two databases of the UCI repository and two real databases of Ecuadorian banks that grant various types of credit. The results obtained have been satisfactory. Finally, our conclusions are discussed and future research lines are suggested.
Fil: Jimbo Santana, Patricia. Universidad Central del Ecuador; Ecuador
Fil: Villa Monte, Augusto. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
Fil: Rucci, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
Fil: Lanzarini, Laura Cristina. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
Fil: Fernández Bariviera, Aurelio. Universitat Rovira I Virgili; España
description Credit risk is defined as the probability of loss due to non-compliance by the borrower with the required payments in relation to any type of debt. When financial institutions select their customers correctly, they can reduce their credit risk. To achieve this, they use various classification methodologies to sort customers based on their risk, analyzing a set of variables such as reputation, leverage, income and so forth. The extensive analysis and processing of these variables is quite time-consuming, partly because the data to be analyzed are not homogeneous. In this paper, we present an alternative method that operates on nominal and numeric attributes, which allows obtaining a predictive model that uses a reduced set of classification rules aimed at reducing credit risk. When the number of rules used decreases, credit analysts need less time to make their decisions, which will also result in better customer service. The methodology proposed here was applied to two databases of the UCI repository and two real databases of Ecuadorian banks that grant various types of credit. The results obtained have been satisfactory. Finally, our conclusions are discussed and future research lines are suggested.
publishDate 2017
dc.date.none.fl_str_mv 2017-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/57326
Jimbo Santana, Patricia; Villa Monte, Augusto; Rucci, Enzo; Lanzarini, Laura Cristina; Fernández Bariviera, Aurelio; Analysis of Methods for Generating Classification Rules Applicable to Credit Risk; Universidad Nacional de La Plata. Facultad de Informática; Journal of Computer Science & Techonology; 17; 1; 4-2017; 20-28
1666-6046
CONICET Digital
CONICET
url http://hdl.handle.net/11336/57326
identifier_str_mv Jimbo Santana, Patricia; Villa Monte, Augusto; Rucci, Enzo; Lanzarini, Laura Cristina; Fernández Bariviera, Aurelio; Analysis of Methods for Generating Classification Rules Applicable to Credit Risk; Universidad Nacional de La Plata. Facultad de Informática; Journal of Computer Science & Techonology; 17; 1; 4-2017; 20-28
1666-6046
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/JCST/article/view/521
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional de La Plata. Facultad de Informática
publisher.none.fl_str_mv Universidad Nacional de La Plata. Facultad de Informática
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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