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
- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/5667
Ver los metadatos del registro completo
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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 |
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 |
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 http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ |
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application/pdf |
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Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
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CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
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marisa.degiusti@sedici.unlp.edu.ar |
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