Articles producció científica> Gestió d'Empreses

Fuzzy Credit Risk Scoring Rules using FRvarPSO

  • Identification data

    Identifier: imarina:5132939
    Authors:
    Jimbo Santana, PatriciaLanzarini, LauraBariviera, Aurelio F.
    Abstract:
    There is consensus that the best way for reducing insolvency situations in financial institutions is through good risk management, which involves a good client selection process. In the market, there are methodologies for credit scoring, each analyzing a large number of microeconomic and/or macroeconomic variables selected mostly depending on the type of credit to be granted. Since these variables are heterogeneous, the review process carried out by credit analysts takes time. The objective of this article is to propose a solution for this problem by applying fuzzy logic to the creation of classification rules for credit granting. To achieve this, linguistic variables were used to help the analyst interpret the information available from the credit officer. The method proposed here combines the use of fuzzy logic with a neural network and a variable population optimization technique to obtain fuzzy classification rules. It was tested with three databases from financial entities in Ecuador ¿ one credit and savings cooperative and two banks that grant various types of credits. To measure its performance, three benchmarks were used: accuracy, number of classification rules generated, and antecedent length. The results obtained indicate that the hybrid model that is proposed performs better than its previous versions due to the addition of fuzzy logic. At the end of the article, our conclusions are discussed and future research lines are suggested.
  • Others:

    Author, as appears in the article.: Jimbo Santana, Patricia; Lanzarini, Laura; Bariviera, Aurelio F.;
    Department: Gestió d'Empreses
    URV's Author/s: Fernández Bariviera, Aurelio
    Keywords: Varpso (variable particle swarm optimization) Particle swarm optimization Fuzzy rules Credit risk
    Abstract: There is consensus that the best way for reducing insolvency situations in financial institutions is through good risk management, which involves a good client selection process. In the market, there are methodologies for credit scoring, each analyzing a large number of microeconomic and/or macroeconomic variables selected mostly depending on the type of credit to be granted. Since these variables are heterogeneous, the review process carried out by credit analysts takes time. The objective of this article is to propose a solution for this problem by applying fuzzy logic to the creation of classification rules for credit granting. To achieve this, linguistic variables were used to help the analyst interpret the information available from the credit officer. The method proposed here combines the use of fuzzy logic with a neural network and a variable population optimization technique to obtain fuzzy classification rules. It was tested with three databases from financial entities in Ecuador ¿ one credit and savings cooperative and two banks that grant various types of credits. To measure its performance, three benchmarks were used: accuracy, number of classification rules generated, and antecedent length. The results obtained indicate that the hybrid model that is proposed performs better than its previous versions due to the addition of fuzzy logic. At the end of the article, our conclusions are discussed and future research lines are suggested.
    Thematic Areas: Software Matemática / probabilidade e estatística Interdisciplinar Information systems Engenharias iv Economia Control and systems engineering Computer science, artificial intelligence Ciência da computação Artificial intelligence
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: aurelio.fernandez@urv.cat
    Author identifier: 0000-0003-1014-1010
    Record's date: 2024-09-07
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://www.worldscientific.com/doi/epdf/10.1142/S0218488518400032
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: International Journal Of Uncertainty Fuzziness And Knowledge-Based Systems. 26 39-57
    APA: Jimbo Santana, Patricia; Lanzarini, Laura; Bariviera, Aurelio F.; (2018). Fuzzy Credit Risk Scoring Rules using FRvarPSO. International Journal Of Uncertainty Fuzziness And Knowledge-Based Systems, 26(), 39-57. DOI: 10.1142/S0218488518400032
    Article's DOI: 10.1142/S0218488518400032
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2018
    Publication Type: Journal Publications
  • Keywords:

    Artificial Intelligence,Computer Science, Artificial Intelligence,Control and Systems Engineering,Information Systems,Software
    Varpso (variable particle swarm optimization)
    Particle swarm optimization
    Fuzzy rules
    Credit risk
    Software
    Matemática / probabilidade e estatística
    Interdisciplinar
    Information systems
    Engenharias iv
    Economia
    Control and systems engineering
    Computer science, artificial intelligence
    Ciência da computação
    Artificial intelligence
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