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Modelos de credit scoring: qué, cómo, cuándo y para qué
[Credit scoring models: what, how, when and for what purposes]

Author

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  • Gutierrez Girault, Matias Alfredo

Abstract

Introduced in the 70’s, credit scoring techniques became widespread in the 90’s thanks to the development of better statistical and computational resources. Nowadays almost all the financial intermediaries use these techniques, at least to originate credits. Credit scoring models are algorithms that in a mechanical way assess the credit risk of a loan applicant or an existing bank client, by means of statistical, mathematic, econometric or artificial intelligence developments. They are focused on the borrower’s creditworthiness or credit risk, regardless of his interaction with the rest of the portfolio. Although all of them yield fairly similar results, those most commonly used are probit and logistic regressions, and decision trees. In general they are used to evaluate the retail portfolio; corporate obligors are typically assessed with rating systems. Besides using different explanatory variables, the assessment of corporate borrowers implies revising qualitative aspects of their business that are difficult to standardize. Therefore the result of their assessment is better expressed with a rating. To clarify how credit scores are constructed and used, with the information contained in the BCRA’s public credit registry (Central de Deudores del Sistema Financiero (CENDEU)) we estimate a sample credit score and show how it operates with a probit model. The only purpose of this model is to show some stylized facts of credit scores, and by no means seeks to establish or indicate what are the best practices in their use, construction or interpretation.

Suggested Citation

  • Gutierrez Girault, Matias Alfredo, 2007. "Modelos de credit scoring: qué, cómo, cuándo y para qué
    [Credit scoring models: what, how, when and for what purposes]
    ," MPRA Paper 16377, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:16377
    as

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    File URL: https://mpra.ub.uni-muenchen.de/16377/1/MPRA_paper_16377.pdf
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    References listed on IDEAS

    as
    1. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
    2. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541.
    3. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    4. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 119-149, January.
    5. Srinivasan, Venkat & Kim, Yong H, 1987. " Credit Granting: A Comparative Analysis of Classification Procedures," Journal of Finance, American Finance Association, vol. 42(3), pages 665-681, July.
    6. Powell, Andrew & Mylenko, Nataliya & Miller, Margaret & Majnoni, Giovanni, 2004. "Improving credit information, bank regulation, and supervision : on the role and design of public credit registries," Policy Research Working Paper Series 3443, The World Bank.
    7. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, vol. 40(1), pages 3-14, January.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    credit risk; credit scoring; binary probit;

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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