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Default Predictors and Credit Scoring Models for Retail Banking

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  • Evžen Kocenda
  • Martin Vojtek

Abstract

This paper develops a specification of the credit scoring model with high discriminatory power to analyze data on loans at the retail banking market. Parametric and non- parametric approaches are employed to produce three models using logistic regression (parametric) and one model using Classification and Regression Trees (CART, nonparametric). The models are compared in terms of efficiency and power to discriminate between low and high risk clients by employing data from a new European Union economy. We are able to detect the most important characteristics of default behavior: the amount of resources the client has, the level of education, marital status, the purpose of the loan, and the number of years the client has had an account with the bank. Both methods are robust: they found similar variables as determinants. We therefore show that parametric as well as non-parametric methods can produce successful models. We are able to obtain similar results even when excluding a key financial variable (amount of own resources). The policy conclusion is that socio-demographic variables are important in the process of granting credit and therefore such variables should not be excluded from credit scoring model specification.

Suggested Citation

  • Evžen Kocenda & Martin Vojtek, 2009. "Default Predictors and Credit Scoring Models for Retail Banking," CESifo Working Paper Series 2862, CESifo.
  • Handle: RePEc:ces:ceswps:_2862
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    Cited by:

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    2. Gabriela Kuvikova, 2015. "Does Loan Maturity Matter in Risk-Based Pricing? Evidence from Consumer Loan Data," CERGE-EI Working Papers wp538, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    3. Hela Ben hassine khalladi, 2017. "Financial crises management by the International Monetary Fund: Was external and public debt sustainable ?," Economics Bulletin, AccessEcon, vol. 37(1), pages 118-136.
    4. Natalia Nehrebecka, 2016. "Approach to the assessment of credit risk for non-financial corporations. Evidence from Poland," IFC Bulletins chapters, in: Bank for International Settlements (ed.),Combining micro and macro data for financial stability analysis, volume 41, Bank for International Settlements.
    5. Dorfleitner, G. & Just-Marx, S. & Priberny, C., 2017. "What drives the repayment of agricultural micro loans? Evidence from Nicaragua," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 89-100.
    6. Martin Řezáč & Lukáš Toma, 2013. "Indeterminate values of target variable in development of credit scoring models," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(7), pages 2709-2716.
    7. Gabriela Kuvikova, 2015. "Loans for Better Living: The Role of Informal Collateral," CERGE-EI Working Papers wp541, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    8. Fidrmuc, Jarko & Hainz, Christa, 2010. "Default rates in the loan market for SMEs: Evidence from Slovakia," Economic Systems, Elsevier, vol. 34(2), pages 133-147, June.
    9. Yaseen Ghulam & Kamini Dhruva & Sana Naseem & Sophie Hill, 2018. "The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans," Risks, MDPI, Open Access Journal, vol. 6(3), pages 1-1, September.
    10. Timotej Jagric & Vita Jagric & Davorin Kracun, 2011. "Does Non-linearity Matter in Retail Credit Risk Modeling?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(4), pages 384-402, August.
    11. NUCU, Anca Elena, 2011. "Managementul riscului de creditare: realizari actuale, analiza critica, sugestii [Credit risk management: current achievements, critical analysis, suggestions]," MPRA Paper 27932, University Library of Munich, Germany.
    12. Yaseen Ghulam & Sophie Hill, 2017. "Distinguishing between Good and Bad Subprime Auto Loans Borrowers: The Role of Demographic, Region and Loan Characteristics," Review of Economics & Finance, Better Advances Press, Canada, vol. 10, pages 49-62, November.
    13. Ben Hassine Khalladi, hela, 2015. "Financial Crisis Management in Emerging Countries: Optimal Level of International Reserves and Ex Ante Conditions for an International Lender of Last Resort Intervention," MPRA Paper 96151, University Library of Munich, Germany.
    14. Enrique Marshall, 2015. "Reflexiones sobre la Práctica del Ahorro en Chile," Economic Policy Papers Central Bank of Chile 54, Central Bank of Chile.

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    More about this item

    Keywords

    credit scoring; discrimination analysis; banking sector; pattern recognition; retail loans; CART; European Union;

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • P43 - Economic Systems - - Other Economic Systems - - - Finance; Public Finance

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