Autor según el artículo: Patricia Jimbo Santana; Augusto Villa Monte; Enzo Rucci; Laura Lanzarini; Aurelio Fernández Bariviera

Departamento: Gestió d'Empreses

Autor/es de la URV: Fernández Bariviera, Aurelio

Palabras clave: Particle swarm optimization Credit scoring Competitive neural networks Classification rules

Resumen: 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.

Áreas temáticas: Interdisciplinar Engenharias iv Engenharias iii Ciência da computação

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 16666038

Direcció de correo del autor: aurelio.fernandez@urv.cat

Identificador del autor: 0000-0003-1014-1010

Fecha de alta del registro: 2023-09-02

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://journal.info.unlp.edu.ar/JCST/article/view/521

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Journal Of Computer Science And Technology (La Plata. En Línea). 17 (1): 20-28

Referencia de l'ítem segons les normes APA: Patricia Jimbo Santana; Augusto Villa Monte; Enzo Rucci; Laura Lanzarini; Aurelio Fernández Bariviera (2017). Analysis of Methods for Generating Classification Rules Applicable to Credit Risk. Journal Of Computer Science And Technology (La Plata. En Línea), 17(1), 20-28

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2017

Tipo de publicación: Journal Publications