Autor según el artículo: Lanzarini L; Villa Monte A; Bariviera A; Jimbo Santana P

Departamento: Gestió d'Empreses

Autor/es de la URV: Fernández Bariviera, Aurelio

Palabras clave: Risk Particle swarm optimization Learning vector quantization Credit risk Classification rules Classification Algorithms

Resumen: One of the key elements in the banking industry relies on the appropriate selection of customers. To manage credit risk, banks dedicate special efforts to classify customers according to their risk. The usual decision-making process consists of gathering personal and financial information about the borrower. Processing this information can be time-consuming, and presents some difficulties because of the heterogeneous structure of data. This paper presents an alternative method that is able to generate rules that work not only on numerical attributes but also on nominal ones. The key feature of this method, called learning vector quantization and particle swarm optimization (LVQ+PSO), is the finding of a reduced set of classifying rules. This is possible because of the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method useful for credit officers aiming to make decisions about granting a credit. It also could act as an orientation for borrower's self evaluation about her/his creditworthiness. This study develops a new method that combines a competitive neural network and an optimization technique. It was applied to a real database of a financial institution in a developing country.

Áreas temáticas: Theoretical computer science Software Social sciences (miscellaneous) Medicina ii Interdisciplinar Information systems Engineering (miscellaneous) Engenharias iv Engenharias iii Electrical and electronic engineering Control and systems engineering Computer science, cybernetics Computer science (miscellaneous) Ciências biológicas i Ciências ambientais Ciência da computação Artificial intelligence Arquitetura, urbanismo e design Arquitetura e urbanismo

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

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

Identificador del autor: 0000-0003-1014-1010

Fecha de alta del registro: 2023-02-18

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

Enlace a la fuente original: https://www.emerald.com/insight/content/doi/10.1108/K-06-2016-0158/full/html

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

Referencia al articulo segun fuente origial: Kybernetes. 46 (1): 8-16

Referencia de l'ítem segons les normes APA: Lanzarini L; Villa Monte A; Bariviera A; Jimbo Santana P (2017). Simplifying credit scoring rules using LVQ + PSO. Kybernetes, 46(1), 8-16. DOI: 10.1108/K-06-2016-0158

DOI del artículo: 10.1108/K-06-2016-0158

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2017

Tipo de publicación: Journal Publications