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The TruEnd-procedure: Treating trailing zero-valued balances in credit data

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  • Arno Botha
  • Tanja Verster
  • Roelinde Bester

Abstract

A novel procedure is presented for finding the true but latent endpoints within the repayment histories of individual loans. The monthly observations beyond these true endpoints are false, largely due to operational failures that delay account closure, thereby corrupting some loans in the dataset with `false' observations. Detecting these false observations is difficult at scale since each affected loan history might have a different sequence of zero (or very small) month-end balances that persist towards the end. Identifying these trails of diminutive balances would require an exact definition of a "small balance", which can be found using our so-called TruEnd-procedure. We demonstrate this procedure and isolate the ideal small-balance definition using residential mortgages from a large South African bank. Evidently, corrupted loans are remarkably prevalent and have excess histories that are surprisingly long, which ruin the timing of certain risk events and compromise any subsequent time-to-event model such as survival analysis. Excess histories can be discarded using the ideal small-balance definition, which demonstrably improves the accuracy of both the predicted timing and severity of risk events, without materially impacting the monetary value of the portfolio. The resulting estimates of credit losses are lower and less biased, which augurs well for raising accurate credit impairments under the IFRS 9 accounting standard. Our work therefore addresses a pernicious data error, which highlights the pivotal role of data preparation in producing credible forecasts of credit risk.

Suggested Citation

  • Arno Botha & Tanja Verster & Roelinde Bester, 2024. "The TruEnd-procedure: Treating trailing zero-valued balances in credit data," Papers 2404.17008, arXiv.org, revised Nov 2024.
  • Handle: RePEc:arx:papers:2404.17008
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    References listed on IDEAS

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    1. Zoltán Novotny-Farkas, 2016. "The Interaction of the IFRS 9 Expected Loss Approach with Supervisory Rules and Implications for Financial Stability," Accounting in Europe, Taylor & Francis Journals, vol. 13(2), pages 197-227, May.
    2. Arno Botha & Conrad Beyers & Pieter de Villiers, 2020. "Simulation-based optimisation of the timing of loan recovery across different portfolios," Papers 2009.11064, arXiv.org, revised Apr 2021.
    3. Janette Larney & James Samuel Allison & Gerrit Lodewicus Grobler & Marius Smuts, 2023. "Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    4. Gürtler, Marc & Hibbeln, Martin, 2013. "Improvements in loss given default forecasts for bank loans," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2354-2366.
    5. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    6. Bellotti, Tony & Crook, Jonathan, 2013. "Forecasting and stress testing credit card default using dynamic models," International Journal of Forecasting, Elsevier, vol. 29(4), pages 563-574.
    7. Tony Bellotti & Jonathan Crook, 2014. "Retail credit stress testing using a discrete hazard model with macroeconomic factors," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 340-350, March.
    8. Jonathan Crook & Tony Bellotti, 2010. "Time varying and dynamic models for default risk in consumer loans," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 283-305, April.
    9. Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 903-911, May.
    10. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    11. Jiří Witzany & Michal Rychnovský & Pavel Charamza, 2012. "Survival Analysis in LGD Modeling," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2012(1), pages 6-27.
    12. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
    13. Skoglund, Jimmy, 2017. "Credit risk term-structures for lifetime impairment forecasting: A practical guide," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 10(2), pages 177-195, April.
    14. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    15. J Banasik & J N Crook & L C Thomas, 1999. "Not if but when will borrowers default," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(12), pages 1185-1190, December.
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