Pronóstico de incumplimientos de pago mediante máquinas de vectores de soporte: una aproximación inicial a la gestión del riesgo de crédito
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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| Length: | 33 |
| Date of creation: | 24 Oct 2011 |
| Handle: | RePEc:col:000094:009079 |
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References listed on IDEAS
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- 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.
- 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.
- Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
- 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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