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Variations of particle swarm optimization for obtaining classification rules applied to credit risk in financial institutions of Ecuador

  • Datos identificativos

    Identificador: imarina:6063482
    Autores:
    Santana PLanzarini LBariviera A
    Resumen:
    © 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.
  • Otros:

    Autor según el artículo: Santana P; Lanzarini L; Bariviera A
    Departamento: Gestió d'Empreses
    Autor/es de la URV: Fernández Bariviera, Aurelio
    Palabras clave: Support vector machines Pso Particle swarm optimization Neural-networks Fuzzy classification rules Credit risk Algorithm
    Resumen: © 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.
    Áreas temáticas: Strategy and management Sociología Economics, econometrics and finance (miscellaneous) Ciências ambientais Business, finance Accounting
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 22279091
    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/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/2227-9091/8/1/2
    Referencia al articulo segun fuente origial: Risks. 8 (1):
    Referencia 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 Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3390/risks8010002
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Accounting,Business, Finance,Economics, Econometrics and Finance (Miscellaneous),Strategy and Management
    Support vector machines
    Pso
    Particle swarm optimization
    Neural-networks
    Fuzzy classification rules
    Credit risk
    Algorithm
    Strategy and management
    Sociología
    Economics, econometrics and finance (miscellaneous)
    Ciências ambientais
    Business, finance
    Accounting
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