Methods of Calculation of Expected Credit Losses Under Requirements of IFRS 9

The most important area of work for financial market regulators including International Accounting Standards Board is to clarify the metrics of credit assessment. This problem became particularly relevant after the financial crisis of 2008, when the insolvency of approaches to the assessment of credit risks adopted under the then international financial reporting standard IFRS (IAS) 39 became apparent, since credit losses on financial instruments were taken into account by the “loss model”, and therefore, the asset was recognized as financially impaired due to the fact of credit quality deterioration and significant time lag. From 1 January 2018 of a new international financial reporting standard IFRS9IFRS 9 is based on a different approach — the principle of “expected credit losses” (ECL).The transition to IFRS 9 is intended to strengthen the banking system by increasing reserves, the banking system’s stability can be increased also. The new business model radically changes the approach to the formation of reserves, including by taking into account the impact of macroeconomic indicators on their value. According to various estimates, the scale of increase in reserves ranges from 30% to 50%. The purpose of this article is to systematize the methodological principles and approaches that underlie the requirements of IFRS 9 (basic and simplified and POCI approaches), as well as a comparison of the main methods for assessing the probability of default and expected credit losses (Weibul distribution, migration matrix, generator matrix ) In the framework of this article, the authors formulated criteria for the transfer of assets between the stages of credit risk (stage), and also formulated the principles for calculating expected credit risks for each stage, taking into account macroeconomic factors. This article is of practical value, as it can be the basis for the development of methods for calculating the expected credit risks of corporate clients of commercial banks, and can also be used to improve credit risk management models.

Abstract licence: CC BY

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