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Pronóstico de incumplimientos de pago mediante máquinas de vectores de soporte: una aproximación inicial a la gestión del riesgo de crédito

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  • José Fernando Moreno Gutiérrez
  • Luis Fernando Melo Velandia

Abstract

Este documento describe la metodología desarrollada por Vapnik (1995), denominada máquinas de vectores de soporte (SVM, por sus siglas en inglés) y realiza dos aplicaciones al caso de clasificación de agentes para el otorgamiento de créditos a partir de sus características. El primer caso de estudio clasifica individuos de un banco alemán. En el segundo caso se pronostica el incumplimiento del pago de créditos comerciales otorgados a empresas colombianas utilizando las características iniciales del crédito. SVM se compara con dos metodologías utilizadas en el análisis de este tipo de problemas, regresión logística y análisis lineal discriminante. Los resultados arrojan un mejor desempeño en la predicción por parte de SVM respecto a las otras dos metodologías.

Suggested Citation

  • José Fernando Moreno Gutiérrez & Luis Fernando Melo Velandia, 2011. "Pronóstico de incumplimientos de pago mediante máquinas de vectores de soporte: una aproximación inicial a la gestión del riesgo de crédito," Borradores de Economia 9079, Banco de la Republica.
  • Handle: RePEc:col:000094:009079
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    References listed on IDEAS

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    1. Matthew Brosnahan & Tan Chong Lee, 1989. "International convergence of capital measurement and capital standards for banks," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 52, march.
    2. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    3. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
    4. Christian Gourieroux & Joann Jasiak, 2007. "Introduction to The Econometrics of Individual Risk: Credit, Insurance, and Marketing," Introductory Chapters, in: The Econometrics of Individual Risk: Credit, Insurance, and Marketing, Princeton University Press.
    5. Lean Yu & Shouyang Wang & Kin Keung Lai & Ligang Zhou, 2008. "Bio-Inspired Credit Risk Analysis," Springer Books, Springer, number 978-3-540-77803-5, January.
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    Cited by:

    1. Fabián Enrique Salazar Villano, 2013. "Cuantificación del riesgo de incumplimiento en créditos de libre inversión: un ejercicio econométrico para una entidad bancaria del municipio de Popayán, Colombia," Estudios Gerenciales, Universidad Icesi, December.

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    More about this item

    Keywords

    Clasificación; máquinas de aprendizaje; riesgo de crédito; support vector machines.;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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