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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

    as
    1. Eisenbeis, Robert A, 1977. "Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics," Journal of Finance, American Finance Association, vol. 32(3), pages 875-900, June.
    2. Ceyla Pazarbasioglu & Miss Gudrun Johnsen & Mr. Paul Louis Ceriel Hilbers & Ms. Inci Ötker, 2005. "Assessing and Managing Rapid Credit Growth and the Role of Supervisory and Prudential Policies," IMF Working Papers 2005/151, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Brůha, Jan & Kočenda, Evžen, 2018. "Financial stability in Europe: Banking and sovereign risk," Journal of Financial Stability, Elsevier, vol. 36(C), pages 305-321.
    2. Sanjay Kumar & Rafeeq Ahmed & Salil Bharany & Mohammed Shuaib & Tauseef Ahmad & Elsayed Tag Eldin & Ateeq Ur Rehman & Muhammad Shafiq, 2022. "Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior," Sustainability, MDPI, vol. 14(21), pages 1-24, October.
    3. 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.
    4. 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.
    5. 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.
    6. Carlos Giner-Baixauli & Juan Tinguaro Rodríguez & Alejandro Álvaro-Meca & Daniel Vélez, 2021. "Modelling Interaction Effects by Using Extended WOE Variables with Applications to Credit Scoring," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    7. 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

    banking sector; credit scoring; discrimination analysis; pattern recognition; retail loans;
    All these keywords.

    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 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Finance; Public Finance

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