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dc.contributor.author
Jimbo Santana, Patricia
dc.contributor.author
Villa Monte, Augusto
dc.contributor.author
Rucci, Enzo
dc.contributor.author
Lanzarini, Laura Cristina
dc.contributor.author
Fernández Bariviera, Aurelio
dc.date.available
2018-08-28T14:24:52Z
dc.date.issued
2017-04
dc.identifier.citation
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
dc.identifier.issn
1666-6046
dc.identifier.uri
http://hdl.handle.net/11336/57326
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Universidad Nacional de La Plata. Facultad de Informática
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Classification Rules
dc.subject
Credit Scoring
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Competitive Neural Networks
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Particle Swarm Optimization
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Analysis of Methods for Generating Classification Rules Applicable to Credit Risk
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-08-21T18:37:06Z
dc.journal.volume
17
dc.journal.number
1
dc.journal.pagination
20-28
dc.journal.pais
Argentina
dc.journal.ciudad
La Plata
dc.description.fil
Fil: Jimbo Santana, Patricia. Universidad Central del Ecuador; Ecuador
dc.description.fil
Fil: Villa Monte, Augusto. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
dc.description.fil
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
dc.description.fil
Fil: Lanzarini, Laura Cristina. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
dc.description.fil
Fil: Fernández Bariviera, Aurelio. Universitat Rovira I Virgili; España
dc.journal.title
Journal of Computer Science & Techonology
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/JCST/article/view/521
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