IDEAS home Printed from https://ideas.repec.org/a/url/izvest/v19y2018i2p24-35.html
   My bibliography  Save this article

Dependence of a Loan Portfolio Structure on a Cut-Off Level in a Scoring Model

Author

Listed:
  • Galina A. Timofeeva

    (Ural State University of Railway Transport)

  • Yana A. Bozhalkina

    (Ural State University of Railway Transport)

Abstract

The study aims to validate a mathematical model of influence of applications’ selection process on a loan portfolio structure expected by the end of a planned period. When predicting risks and profitability of a loan portfolio, many authors use a mathematical model of a loan portfolio in the form of a Markov chain with discrete time. This model usually does not consider the process of attracting new customers. The present paper proposes a more complete model for changing a loan portfolio structure in the form of a Markov chain taking into account a procedure of attracting new customers and selecting them based on the credit rating. The main advantage of this scheme is that it allows taking into consideration the change in a cut-off level when using a scoring model of customer selection. This provides an opportunity to predict dynamics of the volume and structure of a loan portfolio depending on the selected cut-off level under sufficiently stable economic conditions

Suggested Citation

  • Galina A. Timofeeva & Yana A. Bozhalkina, 2018. "Dependence of a Loan Portfolio Structure on a Cut-Off Level in a Scoring Model," Journal of New Economy, Ural State University of Economics, vol. 19(2), pages 24-35, April.
    • Handle: RePEc:url:izvest:v:19:y:2018:i:2:p:24-35
    DOI: 10.29141/2073-1019-2018-19-2-2
    as

    Download full text from publisher

    File URL: http://izvestia.usue.ru/images/download/76/2.pdf
    Download Restriction: no
    File URL: http://izvestia.usue.ru/en/-2018/722
    Download Restriction: no
    File URL: https://libkey.io/10.29141/2073-1019-2018-19-2-2?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
    ---><---

    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. Antonella Foglia, 2008. "Stress testing credit risk: a survey of authorities' approaches," Questioni di Economia e Finanza (Occasional Papers) 37, Bank of Italy, Economic Research and International Relations Area.
    3. Anisa Caja & Quentin Guibert & Frédéric Planchet, 2015. "Influence of Economic Factors on the Credit Rating Transitions and Defaults of Credit Insurance Business," Working Papers hal-01178812, HAL.
    4. L Quirini & L Vannucci, 2014. "Creditworthiness dynamics and Hidden Markov Models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 323-330, March.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    12. Pierre-Emmanuel Darpeix, 2015. "Systemic risk and insurance," Working Papers halshs-01227969, HAL.
    13. Karol Przanowski, 2014. "Credit acceptance process strategy case studies - the power of Credit Scoring," Papers 1403.6531, arXiv.org.
    14. 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.
    15. Jackelyn Hwang & Elizabeth Kneebone & Vasudha Kumar, 2023. "Recent Findings on Residential Instability in Oakland," Community Development Research Brief, Federal Reserve Bank of San Francisco, vol. 2023(02), pages 1-33, February.
    16. Areski Cousin & Jérôme Lelong & Tom Picard, 2023. "Rating transitions forecasting: a filtering approach," Post-Print hal-03347521, HAL.
    17. Schuermann, Til, 2014. "Stress testing banks," International Journal of Forecasting, Elsevier, vol. 30(3), pages 717-728.
    18. Douw Gerbrand Breed & Tanja Verster & Willem D. Schutte & Naeem Siddiqi, 2019. "Developing an Impairment Loss Given Default Model Using Weighted Logistic Regression Illustrated on a Secured Retail Bank Portfolio," Risks, MDPI, vol. 7(4), pages 1-16, December.
    19. Jairaj Gupta & Andros Gregoriou & Jerome Healy, 2015. "Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 845-869, November.
    20. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.

    More about this item

    Keywords

    loan portfolio; risk management; profitability; Markov model; scoring;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    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:url:izvest:v:19:y:2018:i:2:p:24-35. 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: Victor Blaginin (email available below). General contact details of provider: https://edirc.repec.org/data/usueeru.html .

    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.