Autor segons l'article: Patricia Jimbo Santana; Augusto Villa Monte; Enzo Rucci; Laura Lanzarini; Aurelio Fernández Bariviera
Departament: Gestió d'Empreses
Autor/s de la URV: Fernández Bariviera, Aurelio
Paraules clau: Particle swarm optimization Credit scoring Competitive neural networks Classification rules
Resum: 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.
Àrees temàtiques: Interdisciplinar Engenharias iv Engenharias iii Ciência da computação
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 16666038
Adreça de correu electrònic de l'autor: aurelio.fernandez@urv.cat
Identificador de l'autor: 0000-0003-1014-1010
Data d'alta del registre: 2023-09-02
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Referència a l'article segons font original: Journal Of Computer Science And Technology (La Plata. En Línea). 17 (1): 20-28
Referència 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
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2017
Tipus de publicació: Journal Publications