IDEAS home Printed from https://ideas.repec.org/a/eee/quaeco/v79y2021icp260-271.html
   My bibliography  Save this article

Non-capital calibration of bureau scorecards

Author

Listed:
  • Kritzinger, Nico
  • van Vuuren, Gary Wayne

Abstract

Application scorecards play a critical part in determining the creditworthiness of applicants for acquisition purposes. However, the level of bad rate in a downturn period and upturn period although monotonic are different across the scores due to procyclicality (which arises from the fluctuation of financial characteristics around a trend in an economic cycle). The procyclicality effect from an acquisition perspective on a bureau scorecard is investigated with emphasis on South African retail banking data, and performance between downturn and upturn periods is compared. This paper contributes by proposing a methodology which incorporates a Bayesian calibration approach to adjust to future expected bad rates: the comparison indicates that calibration is essential to account for procyclicality.

Suggested Citation

  • Kritzinger, Nico & van Vuuren, Gary Wayne, 2021. "Non-capital calibration of bureau scorecards," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 260-271.
    • Handle: RePEc:eee:quaeco:v:79:y:2021:i:c:p:260-271
    DOI: 10.1016/j.qref.2020.06.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062976920300776
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.qref.2020.06.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. S Ingolfsson & B T Elvarsson, 2010. "Cyclical adjustment of point-in-time PD," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 374-380, March.
    3. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    4. Medema, Lydian & Koning, Ruud H. & Lensink, Robert, 2009. "A practical approach to validating a PD model," Journal of Banking & Finance, Elsevier, vol. 33(4), pages 701-708, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    2. Douw Gerbrand Breed & Niel van Jaarsveld & Carsten Gerken & Tanja Verster & Helgard Raubenheimer, 2021. "Development of an Impairment Point in Time Probability of Default Model for Revolving Retail Credit Products: South African Case Study," Risks, MDPI, vol. 9(11), pages 1-22, November.
    3. Nehrebecka Natalia, 2018. "An Evaluation of the Discriminatory Power of Selected Polish Bankruptcy Prediction Models As Part of the Validation Process," Financial Sciences. Nauki o Finansach, Sciendo, vol. 23(4), pages 63-88, December.
    4. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    5. Marcin Chlebus, 2014. "One-day prediction of state of turbulence for financial instrument based on models for binary dependent variable," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 37.
    6. Raffaele Manini & Oriol Amat, 2018. "Credit scoring for the supermarket and retailing industry: analysis and application proposal," Economics Working Papers 1614, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Enrique Batiz‐Zuk & Fabrizio López‐Gallo & Abdulkadir Mohamed & Fátima Sánchez‐Cajal, 2022. "Determinants of loan survival rates for small and medium‐sized enterprises: Evidence from an emerging economy," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4741-4755, October.
    8. A?da Kammoun & Imen Triki, 2016. "Credit Scoring Models for a Tunisian Microfinance Institution: Comparison between Artificial Neural Network and Logistic Regression," Review of Economics & Finance, Better Advances Press, Canada, vol. 6, pages 61-78, February.
    9. Zhiyong Li & Xinyi Hu & Ke Li & Fanyin Zhou & Feng Shen, 2020. "Inferring the outcomes of rejected loans: an application of semisupervised clustering," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 631-654, February.
    10. Tsukahara, Fábio Yasuhiro & Kimura, Herbert & Sobreiro, Vinicius Amorim & Zambrano, Juan Carlos Arismendi, 2016. "Validation of default probability models: A stress testing approach," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 70-85.
    11. Christopoulos, Andreas D., 2017. "The composition of CMBS risk," Journal of Banking & Finance, Elsevier, vol. 76(C), pages 215-239.
    12. George Xianzhi Yuan & Huiqi Wang, 2019. "The general dynamic risk assessment for the enterprise by the hologram approach in financial technology," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-48, March.
    13. Kroot, Jan & Giouvris, Evangelos, 2016. "Dutch mortgages: Impact of the crisis on probability of default," Finance Research Letters, Elsevier, vol. 18(C), pages 205-217.
    14. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    15. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    16. Kiviat, Barbara, 2019. "Credit Scoring in the United States," economic sociology. perspectives and conversations, Max Planck Institute for the Study of Societies, vol. 21(1), pages 33-42.
    17. Singh, Ramendra Pratap & Singh, Ramendra & Mishra, Prashant, 2021. "Does managing customer accounts receivable impact customer relationships, and sales performance? An empirical investigation," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    18. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    19. Aussenegg, Wolfgang & Resch, Florian & Winkler, Gerhard, 2011. "Pitfalls and remedies in testing the calibration quality of rating systems," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 698-708, March.
    20. Karol Przanowski, 2014. "Credit acceptance process strategy case studies - the power of Credit Scoring," Papers 1403.6531, arXiv.org.

    Corrections

    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:eee:quaeco:v:79:y:2021:i:c:p:260-271. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620167 .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.