IDEAS home Printed from
   My bibliography  Save this article

Credit Scoring Models: Missing Information and the Use of Data from a Credit Register


  • Verónica Balzarotti

    () (Central Bank of Argentina)

  • Fernando Castelpoggi

    (Central Bank of Argentina)


We study the problem arising from the lack of information on some debtors’ behavior in the databases used to develop credit scoring models, and the use of the behavioral information stored at a Credit Register as a potential solution to the problem. To this purpose, we use yearly information provided by the “Central de Deudores del Sistema Financiero” (Credit Register) of the Argentine Central Bank. A limitation of this Register is the removal of a significant number of debtors, on a regular and widespread basis, without recording the reasons for such removal, which may be due to two opposite situations: debt cancellation or default (and the decision by the bank not to continue with the collection procedures). The goal of this paper is not to model the process of missing data, but to focus on: (i) estimating the risk of the removed debtors and (ii) taking advantage of the additional behavioral information of other creditor institutions –recorded in the Credit Register– so as to estimate the risk mentioned in (i) and improve the prediction of the score. In this way, we can also assess the impact of not considering this missing information when the portfolios’ risk of credit institutions is assessed. The main strategy consists in using the behavioral data of other entities and comparing the results provided by three methods: 1) ignoring the records with missing data (listwise deletion), 2) using direct imputation (i.e., in the case of missing information, imputing to behavior the value applicable to the worst behavior of that particular debtor in the system) and 3) using a fractionally weighted imputation method. The paper proves that the commonly-used procedure of removing from the sample the debtors that are no longer in the database, when the reasons behind their disappearance are unknown and cannot be modeled, is not innocuous. The bias that may be introduced is difficult to correct and, even when a correction is attempted, its accuracy cannot be known for certain. Therefore, we underline the importance of making sure that the design of credit risk databases rules out any “holes” that might hinder the follow-up of individuals. In addition, the comparison among the different methods being explored seems to indicate conclusively that the risk is overestimated when direct imputation of behavior is applied, using the worst status observed in other institutions. The model that appears to be more precise is the so-called “fractionally weighted multiple imputation” model, whereby the behavioral information of the Credit Register is used in an imputation model using a logit regression. This approach is innovative in scoring literature and apparently preferable to direct imputation. However, we cannot be conclusive regarding the convenience of using it in all cases. In particular, the analysis of the Argentine “Central de Deudores” suggests that a calibration adjustment to a simpler model using the listwise deletion method may solve an important part of these deficiencies. But this conclusion will depend on the case and the period under analysis. The specific data used in this study refer to an exceptionally good period in the domestic default rates. In addition, as in previous studies, there is evidence that the outcomes from scoring models developed on the basis of public credit information are very good, despite the limited selection of explanatory variables. The results of this study are of interest to the banking industry, supervisors and researchers, since it is common practice to develop models from a sample where a group of debtors has been removed because their information is incomplete, is of poor quality, or shows other deficiencies.

Suggested Citation

  • Verónica Balzarotti & Fernando Castelpoggi, 2009. "Credit Scoring Models: Missing Information and the Use of Data from a Credit Register," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(56), pages 95-156, October -.
  • Handle: RePEc:bcr:ensayo:v:1:y:2009:i:56:p:95-156

    Download full text from publisher

    File URL:
    File Function: Spanish version (versión en Español)
    Download Restriction: no

    References listed on IDEAS

    1. Carling, Kenneth & Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2007. "Corporate credit risk modeling and the macroeconomy," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 845-868, March.
    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. 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.
    4. G. Verstraeten & D. Van Den Poel, 2004. "The Impact of Sample Bias on Consumer Credit Scoring Performance and Profitability," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/232, Ghent University, Faculty of Economics and Business Administration.
    5. Verónica Balzarotti & Christian Castro & Andrew Powell, 2004. "Reforming Capital Requirements in Emerging Countries: Calibrating Basel II using Historical Argentine Credit Bureau Data and CreditRisk+," Business School Working Papers capitalreqemerging, Universidad Torcuato Di Tella.
    6. Ricardo Schechtman & Valéria Salomão Garcia & Sergio Mikio Koyama & Guilherme Cronemberger Parente, 2004. "Credit Risk Measurement and the Regulation of Bank Capital and Provision Requirements in Brazil - A Corporate Analysis," Working Papers Series 91, Central Bank of Brazil, Research Department.
    7. Andrew Powell & Verónica Balzarotti & Christian Castro, 2002. "Reforming Capital Requirements in Emerging Countries," Business School Working Papers diecinueve, Universidad Torcuato Di Tella.
    Full references (including those not matched with items on IDEAS)

    More about this item


    credit registers; credit risk; credit scoring; imputation; information sharing; missing data;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions


    Access and download statistics


    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:bcr:ensayo:v:1:y:2009:i:56:p:95-156. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Federico Grillo). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.