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Simplifying credit scoring rules using LVQ+PSO

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  • Laura Cristina Lanzarini
  • Augusto Villa Monte
  • Aurelio F. Bariviera
  • Patricia Jimbo Santana

Abstract

One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borrower. Processing this information can be time consuming, and presents some difficulties due to the heterogeneous structure of data. We offer in this paper an alternative method that is able to classify customers' profiles from numerical and nominal attributes. The key feature of our method, called LVQ+PSO, is the finding of a reduced set of classifying rules. This is possible, due to 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 not only useful for credit officers aiming to make quick decisions about granting a credit, but also could act as borrower's self selection. Our method was applied to an actual database of a credit consumer financial institution in Ecuador. We obtain very satisfactory results. Future research lines are exposed.

Suggested Citation

  • Laura Cristina Lanzarini & Augusto Villa Monte & Aurelio F. Bariviera & Patricia Jimbo Santana, 2017. "Simplifying credit scoring rules using LVQ+PSO," Papers 1704.04450, arXiv.org.
    • Handle: RePEc:arx:papers:1704.04450
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    3. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
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    1. Luis J. Mena & Vicente García & Vanessa G. Félix & Rodolfo Ostos & Rafael Martínez-Peláez & Alberto Ochoa-Brust & Pablo Velarde-Alvarado, 2024. "Enhancing financial risk prediction with symbolic classifiers: addressing class imbalance and the accuracy–interpretability trade–off," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.

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