Credit Scoring – General Approach in the IFRS 9 Context

Luminita-Georgiana Achim (), Elena Mitoi (), Marian Valentin Moldoveanu () and Codrut-Ioan Turlea ()
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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

The Audit Financiar journal, 2021, vol. 19, issue 162, 384

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.

Keywords: IFRS 9; credit scoring; statistic tests; financial institutions (search for similar items in EconPapers)
JEL-codes: M21 M41 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:aud:audfin:v:19:y:2021:i:162:p:384

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