Autor segons l'article: Santana P; Lanzarini L; Bariviera A
Departament: Gestió d'Empreses
Autor/s de la URV: Fernández Bariviera, Aurelio
Paraules clau: Support vector machines Pso Particle swarm optimization Neural-networks Fuzzy classification rules Credit risk Algorithm
Resum: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval criteria. In this direction, classification rules are an appropriate tool, provided that the rule set has low cardinality and that the antecedent of the rules has few conditions. This paper analyzes different solutions based on Particle Swarm Optimization (PSO) techniques, which are able to construct a set of classification rules with the aforementioned characteristics using information from the borrower and the macroeconomic environment at the time of granting the loan. In addition, to facilitate the understanding of the model, fuzzy logic is incorporated into the construction of the antecedent. To reduce the search time, the particle swarm is initialized by a competitive neural network. Different variants of PSO are applied to three databases of financial institutions in Ecuador. The first institution specializes in massive credit placement. The second institution specializes in consumer credit and business credit lines. Finally, the third institution is a savings and credit cooperative. According to our results, the incorporation of fuzzy logic generates rule sets with greater precision.
Àrees temàtiques: Strategy and management Sociología Economics, econometrics and finance (miscellaneous) Ciências ambientais Business, finance Accounting
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 22279091
Adreça de correu electrònic de l'autor: aurelio.fernandez@urv.cat
Identificador de l'autor: 0000-0003-1014-1010
Data d'alta del registre: 2023-02-18
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.mdpi.com/2227-9091/8/1/2
Referència a l'article segons font original: Risks. 8 (1):
Referència de l'ítem segons les normes APA: Santana P; Lanzarini L; Bariviera A (2020). Variations of particle swarm optimization for obtaining classification rules applied to credit risk in financial institutions of Ecuador. Risks, 8(1), -. DOI: 10.3390/risks8010002
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI de l'article: 10.3390/risks8010002
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Tipus de publicació: Journal Publications