IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/677.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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 677, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:677
    DOI: 10.32468/be.677
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/be.677
    Download Restriction: no

    File URL: https://libkey.io/10.32468/be.677?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pisula Tomasz & Mentel Grzegorz & Brożyna Jacek, 2015. "Non-Statistical Methods of Analysing of Bankruptcy Risk," Folia Oeconomica Stetinensia, Sciendo, vol. 15(1), pages 7-21, June.
    2. Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
    3. Davidescu Adriana AnaMaria & Agafiței Marina-Diana & Strat Vasile Alecsandru & Dima Alina Mihaela, 2024. "Mapping the Landscape: A Bibliometric Analysis of Rating Agencies in the Era of Artificial Intelligence and Machine Learning," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 67-85.
    4. Christa N. Gibbs & Benedict Guttman-Kenney & Donghoon Lee & Scott Nelson & Wilbert Van der Klaauw & Jialan Wang, 2024. "Consumer Credit Reporting Data," Staff Reports 1114, Federal Reserve Bank of New York.
    5. Sun, Weixin & Zhang, Xuantao & Li, Minghao & Wang, Yong, 2023. "Interpretable high-stakes decision support system for credit default forecasting," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    6. R T Stewart, 2011. "A profit-based scoring system in consumer credit: making acquisition decisions for credit cards," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1719-1725, September.
    7. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    8. Tingqiang Chen & Suyang Wang, 2023. "Incomplete information model of credit default of micro and small enterprises," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2956-2974, July.
    9. Ka-Kit Leung & Horas T H Wong & Claire M Naftalin & Shui Shan Lee, 2014. "A New Perspective on Sexual Mixing among Men Who Have Sex with Men by Body Image," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-5, November.
    10. Shi, Peng & Valdez, Emiliano A., 2011. "A copula approach to test asymmetric information with applications to predictive modeling," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 226-239, September.
    11. So, Mee Chi & Thomas, Lyn C. & Huang, Bo, 2016. "Lending decisions with limits on capital available: The polygamous marriage problem," European Journal of Operational Research, Elsevier, vol. 249(2), pages 407-416.
    12. M Malik & L C Thomas, 2010. "Modelling credit risk of portfolio of consumer loans," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 411-420, March.
    13. Douw Gerbrand Breed & Tanja Verster & Willem D. Schutte & Naeem Siddiqi, 2019. "Developing an Impairment Loss Given Default Model Using Weighted Logistic Regression Illustrated on a Secured Retail Bank Portfolio," Risks, MDPI, vol. 7(4), pages 1-16, December.
    14. Ju, Yong Han & Sohn, So Young, 2014. "Updating a credit-scoring model based on new attributes without realization of actual data," European Journal of Operational Research, Elsevier, vol. 234(1), pages 119-126.
    15. Juan Laborda & Seyong Ryoo, 2021. "Feature Selection in a Credit Scoring Model," Mathematics, MDPI, vol. 9(7), pages 1-22, March.
    16. Raffaella Calabrese & Galina Andreeva & Jake Ansell, 2019. "“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 71-84, January.
    17. Lee, Jooh & Kwon, He-Boong, 2017. "Progressive performance modeling for the strategic determinants of market value in the high-tech oriented SMEs," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 91-102.
    18. Dendramis, Y. & Tzavalis, E. & Varthalitis, P. & Athanasiou, E., 2020. "Predicting default risk under asymmetric binary link functions," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1039-1056.
    19. Fahner, Gerald, 2012. "Estimating causal effects of credit decisions," International Journal of Forecasting, Elsevier, vol. 28(1), pages 248-260.
    20. Bąk Sylwia, 2020. "The problem of uncertainty and risk as a subject of research of the Nobel Prize Laureates in Economic Sciences," Journal of Economics and Management, Sciendo, vol. 39(1), pages 21-40, March.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdr:borrec:677. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Clorith Angélica Bahos Olivera (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.