Obtaining Classification Rules Using LVQ+PSO: An Application to Credit Risk

  • Laura Lanzarini
  • Augusto Villa-Monte
  • Aurelio Fernández-Bariviera
  • Patricia Jimbo-Santana
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 377)


Credit risk management is a key element of financial corporations. One of the main problems that face credit risk officials is to approve or deny a credit petition. The usual decision making process consists in gathering personal and financial information about the borrower. This paper present a new method that is able to generate classifying rules that work no only on numerical attributes, but also on nominal attributes. This method, called LVQ+PSO, combines a competitive neural network with an optimization technique in order to find a reduced set of classifying rules. These rules constitute a predictive model for credit risk approval. Given the reduced quantity of rules, our method is very useful for credit officers aiming to make decisions about granting a credit. Our method was applied to two credit databases that were extensively analyzed by other competing classification methods. We obtain very satisfactory results. Future research lines are exposed.


Credit risk Classification rules Learning vector quantization (LVQ) Particle swarm optimization (PSO) 



We thank UCI Machine Learning Repository for the generous provision of data for this paper.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Laura Lanzarini
    • 1
  • Augusto Villa-Monte
    • 2
  • Aurelio Fernández-Bariviera
    • 2
  • Patricia Jimbo-Santana
    • 3
  1. 1.Instituto de Investigación en Informática LIDIUniversidad Nacional de la PlataBuenos AiresArgentina
  2. 2.Departament of BusinessUniversitat Rovira i VirgiliReusSpain
  3. 3.Dpto. Ciencias de la ComputaciónESPE Universidad de las Fuerzas Armadas, Campus PolitécnicoSangolquíEcuador

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