Articles producció científica> Gestió d'Empreses

Simplifying credit scoring rules using LVQ + PSO

  • Identification data

    Identifier: imarina:5130746
    Authors:
    Lanzarini LVilla Monte ABariviera AJimbo Santana P
    Abstract:
    One of the key elements in the banking industry relies on the appropriate selection of customers. To manage credit risk, banks dedicate special efforts to classify customers according to their risk. The usual decision-making process consists of gathering personal and financial information about the borrower. Processing this information can be time-consuming, and presents some difficulties because of the heterogeneous structure of data. This paper presents an alternative method that is able to generate rules that work not only on numerical attributes but also on nominal ones. The key feature of this method, called learning vector quantization and particle swarm optimization (LVQ+PSO), is the finding of a reduced set of classifying rules. This is possible because of the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method useful for credit officers aiming to make decisions about granting a credit. It also could act as an orientation for borrower's self evaluation about her/his creditworthiness. This study develops a new method that combines a competitive neural network and an optimization technique. It was applied to a real database of a financial institution in a developing country.
  • Others:

    Author, as appears in the article.: Lanzarini L; Villa Monte A; Bariviera A; Jimbo Santana P
    Department: Gestió d'Empreses
    URV's Author/s: Fernández Bariviera, Aurelio
    Keywords: Risk Particle swarm optimization Learning vector quantization Credit risk Classification rules Classification Algorithms
    Abstract: One of the key elements in the banking industry relies on the appropriate selection of customers. To manage credit risk, banks dedicate special efforts to classify customers according to their risk. The usual decision-making process consists of gathering personal and financial information about the borrower. Processing this information can be time-consuming, and presents some difficulties because of the heterogeneous structure of data. This paper presents an alternative method that is able to generate rules that work not only on numerical attributes but also on nominal ones. The key feature of this method, called learning vector quantization and particle swarm optimization (LVQ+PSO), is the finding of a reduced set of classifying rules. This is possible because of the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method useful for credit officers aiming to make decisions about granting a credit. It also could act as an orientation for borrower's self evaluation about her/his creditworthiness. This study develops a new method that combines a competitive neural network and an optimization technique. It was applied to a real database of a financial institution in a developing country.
    Thematic Areas: Theoretical computer science Software Social sciences (miscellaneous) Medicina ii Interdisciplinar Information systems Engineering (miscellaneous) Engenharias iv Engenharias iii Electrical and electronic engineering Control and systems engineering Computer science, cybernetics Computer science (miscellaneous) Ciências biológicas i Ciências ambientais Ciência da computação Artificial intelligence Arquitetura, urbanismo e design Arquitetura e urbanismo
    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.emerald.com/insight/content/doi/10.1108/K-06-2016-0158/full/html
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Kybernetes. 46 (1): 8-16
    APA: Lanzarini L; Villa Monte A; Bariviera A; Jimbo Santana P (2017). Simplifying credit scoring rules using LVQ + PSO. Kybernetes, 46(1), 8-16. DOI: 10.1108/K-06-2016-0158
    Article's DOI: 10.1108/K-06-2016-0158
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2017
    Publication Type: Journal Publications
  • Keywords:

    Artificial Intelligence,Computer Science (Miscellaneous),Computer Science, Cybernetics,Control and Systems Engineering,Electrical and Electronic Engineering,Engineering (Miscellaneous),Information Systems,Social Sciences (Miscellaneous),Software,Theoretical Computer,Theoretical Computer Science
    Risk
    Particle swarm optimization
    Learning vector quantization
    Credit risk
    Classification rules
    Classification
    Algorithms
    Theoretical computer science
    Software
    Social sciences (miscellaneous)
    Medicina ii
    Interdisciplinar
    Information systems
    Engineering (miscellaneous)
    Engenharias iv
    Engenharias iii
    Electrical and electronic engineering
    Control and systems engineering
    Computer science, cybernetics
    Computer science (miscellaneous)
    Ciências biológicas i
    Ciências ambientais
    Ciência da computação
    Artificial intelligence
    Arquitetura, urbanismo e design
    Arquitetura e urbanismo
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