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Microenvironment-specific Effects in the Application Credit Scoring Model

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  • Khudnitskaya, Alesia S.

Abstract

Paper introduces the improved version of a credit scoring model which assesses credit worthiness of applicants for a loan. The scorecard has a two-level multilevel structure which nests applicants for a loan within microenvironments. Paper discusses several versions of the multilevel scorecards which includes random-intercept, random-coefficients and group-level variables. The primary benefit of the multilevel scorecard compared to a conventional scoring model is a higher accuracy of the model predictions.

Suggested Citation

  • Khudnitskaya, Alesia S., 2009. "Microenvironment-specific Effects in the Application Credit Scoring Model," MPRA Paper 23175, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:23175
    as

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    File URL: https://mpra.ub.uni-muenchen.de/23175/1/MPRA_paper_23175.pdf
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    References listed on IDEAS

    as
    1. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
    2. Harvey Goldstein & Jon Rasbash, 1996. "Improved Approximations for Multilevel Models with Binary Responses," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 505-513, May.
    3. Harvey Goldstein & Simon Burgess & Brendon McConnell, 2007. "Modelling the effect of pupil mobility on school differences in educational achievement," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 941-954, October.
    4. Durrant, Gabriele B. & Steele, Fiona, 2009. "Multilevel modelling of refusal and non-contact in household surveys: evidence from six UK Government surveys," LSE Research Online Documents on Economics 50112, London School of Economics and Political Science, LSE Library.
    5. Gabriele B. Durrant & Fiona Steele, 2009. "Multilevel modelling of refusal and non‐contact in household surveys: evidence from six UK Government surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 361-381, April.
    6. S. Rabe-Hesketh & A. Skrondal, 2001. "Parameterization of Multivariate Random Effects Models for Categorical Data," Biometrics, The International Biometric Society, vol. 57(4), pages 1256-1263, December.
    7. German Rodriguez & Irma Elo, 2003. "Intra-class correlation in random-effects models for binary data," Stata Journal, StataCorp LP, vol. 3(1), pages 32-46, March.
    8. William H. Greene, 1992. "A Statistical Model for Credit Scoring," Working Papers 92-29, New York University, Leonard N. Stern School of Business, Department of Economics.
    9. Harvey Goldstein & Simon Burgess & Brendon McConnell, 2006. "Modelling the Impact of Pupil Mobility on School Differences in Educational Achievement," The Centre for Market and Public Organisation 06/156, The Centre for Market and Public Organisation, University of Bristol, UK.
    10. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "Generalized multilevel structural equation modeling," Psychometrika, Springer;The Psychometric Society, vol. 69(2), pages 167-190, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Credit scoring; Hierarchical clustering; Multilevel model; Random-coefficient; Random-intercept; Monte Carlo Markov chain;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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