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Credit-Scoring Methods (in English)

Author

Listed:
  • Martin Vojtek

    () (CERGE-EI, Prague)

  • Evžen Koèenda

    () (CERGE-EI, Prague)

Abstract

The paper reviews the best-developed and most frequently applied methods of credit scoring employed by commercial banks when evaluating loan applications. The authors concentrate on retail loans – applied research in this segment is limited, though there has been a sharp increase in the volume of loans to retail clients in recent years. Logit analysis is identified as the most frequent credit-scoring method used by banks. However, other nonparametric methods are widespread in terms of pattern recognition. The methods reviewed have potential for application in post-transition countries.

Suggested Citation

  • Martin Vojtek & Evžen Koèenda, 2006. "Credit-Scoring Methods (in English)," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 56(3-4), pages 152-167, March.
    • Handle: RePEc:fau:fauart:v:56:y:2006:i:3-4:p:152-167
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    References listed on IDEAS

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    1. Hubert Gabrisch & Lucjan T. Orlowski, 2010. "Interest Rate Convergence in Euro-Candidate Countries: Volatility Dynamics of Sovereign Bond Yields," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 46(6), pages 69-85, November.
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    5. Ceyla Pazarbasioglu & Gudrun Johnsen & Paul Louis Ceriel Hilbers & Inci Ötker, 2005. "Assessing and Managing Rapid Credit Growth and the Role of Supervisory and Prudential Policies," IMF Working Papers 05/151, International Monetary Fund.
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    8. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
    9. Ranjula Bali Swain, 2007. "The demand and supply of credit for households," Applied Economics, Taylor & Francis Journals, vol. 39(21), pages 2681-2692.
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    Citations

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    Cited by:

    1. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    2. Martin Rezac & Frantisek Rezac, 2011. "How to Measure the Quality of Credit Scoring Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 486-507, November.
    3. Sanela Pasic & Adisa Omerbegovic Arapovic, 2016. "What Triggers Loan Repayment Failure of Consumer Loans – Evidence from Bosnia and Herzegovina," Eurasian Journal of Business and Management, , vol. 4(1), pages 11-22.
    4. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris Nanterre, EconomiX.
    5. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris Nanterre, EconomiX.
    6. 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.
    7. Aneta Dzik-Walczak & Mateusz Heba, 2019. "A comparison of credit scoring techniques in Peer-to-Peer lending," Working Papers 2019-16, Faculty of Economic Sciences, University of Warsaw.
    8. Enrique Marshall, 2015. "Reflexiones sobre la Práctica del Ahorro en Chile," Economic Policy Papers Central Bank of Chile 54, Central Bank of Chile.
    9. Jan Bruha & Evžen Kocenda, 2017. "Financial Stability in Europe: Banking and Sovereign Risk," CESifo Working Paper Series 6453, CESifo.
    10. Brůha, Jan & Kočenda, Evžen, 2018. "Financial stability in Europe: Banking and sovereign risk," Journal of Financial Stability, Elsevier, vol. 36(C), pages 305-321.
    11. Selcuk Bayraci, 2017. "Application of profit-based credit scoring models using R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 3-28, December.
    12. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank.

    More about this item

    Keywords

    banking sector; credit scoring; discrimination analysis; pattern recognition; retail loans;

    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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