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Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador

Author

Listed:
  • Patricia Jimbo Santana

    (Facultad de Ciencias Administrativas, Carrera de Contabilidad y Auditoría, Universidad Central del Ecuador, Quito 170129, Ecuador)

  • Laura Lanzarini

    (Instituto de Investigación en Informática LIDI, Facultad de Informática, Universidad Nacional de la Plata, La Plata C1900, Buenos Aires, Argentina)

  • Aurelio F. Bariviera

    (Departament of Business, Universitat Rovira i Virgili, Avenida de la Universitat, 43204 Reus, Spain)

Abstract

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.

Suggested Citation

  • Patricia Jimbo Santana & Laura Lanzarini & Aurelio F. Bariviera, 2019. "Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador," Risks, MDPI, vol. 8(1), pages 1-14, December.
    • Handle: RePEc:gam:jrisks:v:8:y:2019:i:1:p:2-:d:303464
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    References listed on IDEAS

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