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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/57326
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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 Técnicas2025-09-03T09:45:34Zoai: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:34982025-09-03 09:45:34.663CONICET 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 |
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CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
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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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13.13397 |