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Credit Scoring – General Approach in the IFRS 9 Context

Author

Listed:
  • Luminita-Georgiana ACHIM

    (Bucharest University of Economic Studies, Romania)

  • Elena MITOI

    (Bucharest University of Economic Studies, Romania)

  • Marian Valentin MOLDOVEANU

    (Bucharest University of Economic Studies, Romania)

  • Codrut-Ioan TURLEA

    (Bucharest University of Economic Studies, Romania)

Abstract

With the coming into force of the standard IFRS 9 – Financial Instruments, in January 2018, financial institutions passed from an incurred loss model to a forward-looking model for the computation of impairment losses. As such, the IFRS 9 models use point-in-time, estimates of Probability of Default and Loss Given Default and provide a more faithful representation of the credit risk at a given as they are based on past experiences as well as the most recent and forecasted economic conditions. However, given the short-term fluctuations in the macroeconomic conditions, the final outcome of the Expected credit loss models is highly volatile due to their sensitivity to the business cycle. With regard to Probability of Default estimation under IFRS 9, the most commonly methods are: Markov Chains, Survival Analysis and single-factor models (Vasicek and Z-Shift). The development of the score-cards is still the same as in the case of the Internal Ratings Based Probability of Default models, encouraging institutions to use the already available credit rating systems and perform adjustment to the calibration. This paper outlines a non-exhaustive list of quantitative validation tests would satisfy the requirements of the IFRS 9 standard.

Suggested Citation

  • Luminita-Georgiana ACHIM & Elena MITOI & Marian Valentin MOLDOVEANU & Codrut-Ioan TURLEA, 2021. "Credit Scoring – General Approach in the IFRS 9 Context," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 19(162), pages 384-384, May.
    • Handle: RePEc:aud:audfin:v:19:y:2021:i:162:p:384
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    References listed on IDEAS

    as
    1. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 119-149, January.
    2. 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.
    3. Pool, Sebastiaan & de Haan, Leo & Jacobs, Jan P.A.M., 2015. "Loan loss provisioning, bank credit and the real economy," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 124-136.
    4. Yang, Bill Huajian, 2017. "Point-in-time PD term structure models for multi-period scenario loss projection: Methodologies and implementations for IFRS 9 ECL and CCAR stress testing," MPRA Paper 76271, University Library of Munich, Germany.
    5. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
    6. Gunther Gebhardt & Zoltan Novotny-Farkas, 2011. "Mandatory IFRS Adoption and Accounting Quality of European Banks," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 38(3-4), pages 289-333, April.
    7. Robert M. Bushman & Christopher D. Williams, 2015. "Delayed Expected Loss Recognition and the Risk Profile of Banks," Journal of Accounting Research, Wiley Blackwell, vol. 53(3), pages 511-553, June.
    8. Claudio Borio & Philip Lowe, 2001. "To provision or not to provision," BIS Quarterly Review, Bank for International Settlements, September.
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    More about this item

    Keywords

    IFRS 9; credit scoring; statistic tests; financial institutions;
    All these keywords.

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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