Analysis of Methods for Generating Classification Rules Applicable to Credit Risk

cic.isFulltexttruees
cic.isPeerReviewedtruees
cic.lugarDesarrolloInstituto de Investigación en Informáticaes
cic.versioninfo:eu-repo/semantics/publishedVersiones
dc.date.accessioned2017-05-04T14:31:46Z
dc.date.available2017-05-04T14:31:46Z
dc.identifier.urihttps://digital.cic.gba.gob.ar/handle/11746/5667
dc.titleAnalysis of Methods for Generating Classification Rules Applicable to Credit Risken
dc.typeArtículoes
dcterms.abstractCredit 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.en
dcterms.creator.authorJimbo Santana, Patriciaes
dcterms.creator.authorVilla Monte, Augustoes
dcterms.creator.authorRucci, Enzoes
dcterms.creator.authorLanzarini, Laura Cristinaes
dcterms.creator.authorFernández Bariviera, Aurelioes
dcterms.extentp. 20-28es
dcterms.identifier.otherISSN 1666-6038es
dcterms.identifier.urlRecurso completoes
dcterms.isPartOf.issuevol. 17, no. 1es
dcterms.isPartOf.seriesJournal of Computer Science and Technologyes
dcterms.issued2017-04
dcterms.languageIngléses
dcterms.licenseAttribution 4.0 International (BY 4.0)es
dcterms.subjectclassification rulesen
dcterms.subjectcredit scoringen
dcterms.subjectcompetitive neural networksen
dcterms.subjectparticle swarmen
dcterms.subjectOptimizaciónes
dcterms.subject.materiaCiencias de la Computación e Informaciónes
Archivos
Paquete original
Mostrando1 - 1 de 1
Imagen en miniatura
Nombre:
JCST-44-Paper-3.pdf-PDFA.pdf
Tamaño:
1008.67 KB
Formato:
Adobe Portable Document Format
Descripción:
Documento completo