Analysis of Methods for Generating Classification Rules Applicable to Credit Risk

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 metho...

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Detalles Bibliográficos
Autores principales:	Jimbo Santana, Patricia, Villa Monte, Augusto, Rucci, Enzo, Lanzarini, Laura Cristina, Fernández Bariviera, Aurelio
Formato:	Articulo
Lenguaje:	Español
Publicado:	2017
Materias:	Ciencias Informáticas classification rules credit scoring competitive neural networks particle swarm Optimization
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/59978 http://journal.info.unlp.edu.ar/wp-content/uploads/2017/05/JCST-44-Paper-3.pdf
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-59978
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Español
topic	Ciencias Informáticas classification rules credit scoring competitive neural networks particle swarm Optimization
spellingShingle	Ciencias Informáticas classification rules credit scoring competitive neural networks particle swarm Optimization 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
topic_facet	Ciencias Informáticas classification rules credit scoring competitive neural networks particle swarm Optimization
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.
format	Articulo Articulo
author	Jimbo Santana, Patricia Villa Monte, Augusto Rucci, Enzo Lanzarini, Laura Cristina Fernández Bariviera, Aurelio
author_facet	Jimbo Santana, Patricia Villa Monte, Augusto Rucci, Enzo Lanzarini, Laura Cristina Fernández Bariviera, Aurelio
author_sort	Jimbo Santana, Patricia
title	Analysis of Methods for Generating Classification Rules Applicable to Credit Risk
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
publishDate	2017
url	http://sedici.unlp.edu.ar/handle/10915/59978 http://journal.info.unlp.edu.ar/wp-content/uploads/2017/05/JCST-44-Paper-3.pdf
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bdutipo_str	Repositorios
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Analysis of Methods for Generating Classification Rules Applicable to Credit Risk

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