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

  • Identification data

    Identifier: imarina:6063482
    Authors:
    Santana PLanzarini LBariviera A
    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.
  • Others:

    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
    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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.3390/risks8010002
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications
  • Keywords:

    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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