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.
Materia
Ciencias de la Computación e Información
classification rules
credit scoring
competitive neural networks
particle swarm
Optimización
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/5667

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repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling Analysis of Methods for Generating Classification Rules Applicable to Credit RiskJimbo Santana, PatriciaVilla Monte, AugustoRucci, EnzoLanzarini, Laura CristinaFernández Bariviera, AurelioCiencias de la Computación e Informaciónclassification rulescredit scoringcompetitive neural networksparticle swarmOptimizaciónCredit 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.2017-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/5667enginfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-04T09:43:27Zoai:digital.cic.gba.gob.ar:11746/5667Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-04 09:43:28.243CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
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
Ciencias de la Computación e Información
classification rules
credit scoring
competitive neural networks
particle swarm
Optimización
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 Ciencias de la Computación e Información
classification rules
credit scoring
competitive neural networks
particle swarm
Optimización
topic Ciencias de la Computación e Información
classification rules
credit scoring
competitive neural networks
particle swarm
Optimización
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.
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
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info:eu-repo/semantics/publishedVersion
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info:ar-repo/semantics/articulo
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dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/5667
url https://digital.cic.gba.gob.ar/handle/11746/5667
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
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dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
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institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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