IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/677.html

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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
    3. 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.
    4. 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.
    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.
    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.

    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. Jos� Fernando Moreno Guti�rrez & Luis Fernando Melo Velandia, 2011. "Pron�stico de incumplimientos de pago mediante m�quinas de vectores de soporte: una aproximaci�n inicial a la gesti�n del riesgo de cr�dito," Borradores de Economia 9079, Banco de la Republica.
    2. 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.
    3. Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
    4. Matuszyk, Anna & So, Mee Chi & Mues, Christophe & Moore, Angela, 2016. "Modelling repayment patterns in the collections process for unsecured consumer debt: A case studyAuthor-Name: Thomas, Lyn C," European Journal of Operational Research, Elsevier, vol. 249(2), pages 476-486.
    5. 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.
    6. Bravo, Cristián & Maldonado, Sebastián & Weber, Richard, 2013. "Granting and managing loans for micro-entrepreneurs: New developments and practical experiences," European Journal of Operational Research, Elsevier, vol. 227(2), pages 358-366.
    7. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    8. Liu, Zhengchi & Shang, Jennifer & Wu, Shin-yi & Chen, Pei-yu, 2020. "Social collateral, soft information and online peer-to-peer lending: A theoretical model," European Journal of Operational Research, Elsevier, vol. 281(2), pages 428-438.
    9. 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).
    10. Arno Botha & Conrad Beyers & Pieter de Villiers, 2020. "The loss optimisation of loan recovery decision times using forecast cash flows," Papers 2010.05601, arXiv.org.
    11. 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.
    12. Ana Preda & Mirela Monea & Lorand Bogdanffy, 2016. "Simulation Insured Results by Purchasing a Life Insurance," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 16(2), pages 109-116.
    13. Janette Larney & Arno Botha & Gerrit Lodewicus Grobler & Helgard Raubenheimer, 2025. "A cost of capital approach to determining the LGD discount rate," Papers 2503.23992, arXiv.org.
    14. Valdez, Emiliano A. & Vadiveloo, Jeyaraj & Dias, Ushani, 2014. "Life insurance policy termination and survivorship," Insurance: Mathematics and Economics, Elsevier, vol. 58(C), pages 138-149.
    15. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    16. Arno Botha & Tanja Verster & Bernard Scheepers, 2025. "Exploring different subtypes of recurrent event Cox-regression models in modelling lifetime default risk: A tutorial," Papers 2505.01044, arXiv.org, revised Jan 2026.
    17. K Bijak, 2011. "Kalman filtering as a performance monitoring technique for a propensity scorecard," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 29-37, January.
    18. Tomasz Pisula, 2020. "An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship," JRFM, MDPI, vol. 13(2), pages 1-35, February.
    19. Huaming Du & Xingyan Chen & Yu Zhao & Qing Li & Fuzhen Zhuang & Fuji Ren & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised Mar 2025.
    20. Huang, Shirley Hsueh-Li & Hu, Guo-Hsin & Hsu, Ming-Fu, 2025. "Identifying contextual content-based risk drivers for advanced risk management strategies," Research in International Business and Finance, Elsevier, vol. 73(PB).

    More about this item

    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.