Articles producció científica> Gestió d'Empreses

Analysis of Methods for Generating Classification Rules Applicable to Credit Risk

  • Identification data

    Identifier: imarina:6409717
    Authors:
    Patricia Jimbo SantanaAugusto Villa MonteEnzo RucciLaura LanzariniAurelio Fernández Bariviera
    Abstract:
    Credit risk is defined as the probability of loss due to non-compliance by the borrower with the required payments in relation to any type of debt. When financial institutions select their customers correctly, they can reduce their credit risk. To achieve this, they use various classification methodologies to sort customers based on their risk, analyzing a set of variables such as reputation, leverage, income and so forth. The extensive analysis and processing of these variables is quite time-consuming, partly because the data to be analyzed are not homogeneous. In this paper, we present an alternative method that operates on nominal and numeric attributes, which allows obtaining a predictive model that uses a reduced set of classification rules aimed at reducing credit risk. When the number of rules used decreases, credit analysts need less time to make their decisions, which will also result in better customer service. The methodology proposed here was applied to two databases of the UCI repository and two real databases of Ecuadorian banks that grant various types of credit. The results obtained have been satisfactory. Finally, our conclusions are discussed and future research lines are suggested.
  • Others:

    Author, as appears in the article.: Patricia Jimbo Santana; Augusto Villa Monte; Enzo Rucci; Laura Lanzarini; Aurelio Fernández Bariviera
    Department: Gestió d'Empreses
    URV's Author/s: Fernández Bariviera, Aurelio
    Keywords: Particle swarm optimization Credit scoring Competitive neural networks Classification rules
    Abstract: Credit risk is defined as the probability of loss due to non-compliance by the borrower with the required payments in relation to any type of debt. When financial institutions select their customers correctly, they can reduce their credit risk. To achieve this, they use various classification methodologies to sort customers based on their risk, analyzing a set of variables such as reputation, leverage, income and so forth. The extensive analysis and processing of these variables is quite time-consuming, partly because the data to be analyzed are not homogeneous. In this paper, we present an alternative method that operates on nominal and numeric attributes, which allows obtaining a predictive model that uses a reduced set of classification rules aimed at reducing credit risk. When the number of rules used decreases, credit analysts need less time to make their decisions, which will also result in better customer service. The methodology proposed here was applied to two databases of the UCI repository and two real databases of Ecuadorian banks that grant various types of credit. The results obtained have been satisfactory. Finally, our conclusions are discussed and future research lines are suggested.
    Thematic Areas: Interdisciplinar Engenharias iv Engenharias iii Ciência da computação
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 16666038
    Author's mail: aurelio.fernandez@urv.cat
    Author identifier: 0000-0003-1014-1010
    Record's date: 2023-09-02
    Papper version: info:eu-repo/semantics/publishedVersion
    Papper original source: Journal Of Computer Science And Technology (La Plata. En Línea). 17 (1): 20-28
    APA: Patricia Jimbo Santana; Augusto Villa Monte; Enzo Rucci; Laura Lanzarini; Aurelio Fernández Bariviera (2017). Analysis of Methods for Generating Classification Rules Applicable to Credit Risk. Journal Of Computer Science And Technology (La Plata. En Línea), 17(1), 20-28
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2017
    Publication Type: Journal Publications
  • Keywords:

    Particle swarm optimization
    Credit scoring
    Competitive neural networks
    Classification rules
    Interdisciplinar
    Engenharias iv
    Engenharias iii
    Ciência da computação
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