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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

Listed author(s):
  • José Fernando Moreno Gutiérrez


  • Luis Fernando Melo Velandia


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.

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Paper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 677.

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Length: 33
Date of creation: Oct 2011
Handle: RePEc:bdr:borrec:677
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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.
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