Author, as appears in the article.: Santana P; Lanzarini L; Bariviera A

Department: Gestió d'Empreses

URV's Author/s: Fernández Bariviera, Aurelio

Keywords: Support vector machines Pso Particle swarm optimization Neural-networks Fuzzy classification rules Credit risk Algorithm

Abstract: © 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.

Thematic Areas: Strategy and management Sociología Economics, econometrics and finance (miscellaneous) Ciências ambientais Business, finance Accounting

licence for use: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 22279091

Author's mail: aurelio.fernandez@urv.cat

Author identifier: 0000-0003-1014-1010

Record's date: 2023-02-18

Papper version: info:eu-repo/semantics/publishedVersion

Link to the original source: https://www.mdpi.com/2227-9091/8/1/2

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: Risks. 8 (1):

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

Article's DOI: 10.3390/risks8010002

Entity: Universitat Rovira i Virgili

Journal publication year: 2020

Publication Type: Journal Publications