blog Market Intelligence /marketintelligence/en/news-insights/blog/ifrs-9-and-the-covid-19-pandemic-important-considerations content esgSubNav
Log in to other products

Login to Market Intelligence Platform


Looking for more?

Contact Us
In This List

IFRS 9 And The COVID 19 Pandemic Important Considerations

Transcript: Coronavirus Insights - An Outlook on Corporate Credit Risk and IFRS 9 Implications

Part Three IFRS 9 Blog Series: The Importance of Efficiency and Transparency

Part Two IFRS 9 Blog Series: The Need to Upgrade Analytical Tools

IFRS 9 Blog Series: Tackling the Challenge of Calculating Impairment

IFRS 9 And The COVID 19 Pandemic Important Considerations

The International Financial Reporting Standard 9 (IFRS 9) requires institutions to estimate future expected credit losses (ECLs) when calculating provisions for investment portfolios, loan books, and trade receivables. This calls for predictive analytics, introducing challenges in terms of data availability, modeling, and reporting. This is especially problematic as we face uncertainty resulting from social and economic disruptions caused by COVID-19. There are a number of considerations we suggest institutions keep in mind when calculating ECLs during this uncertain time. We touch on three of these in this article:

  1. 1. How to choose weights for ECL scenarios.
  2. 2. The timing of an economic turnaround and the resulting impact on lifetime PDs.
  3. 3. The impact of government emergency financing and support.

1. How to choose weights for ECL scenarios

Any changes to ECLs that aim to reflect the impact of COVID-19 will undoubtedly be subject to extreme levels of uncertainty due to a lack of stable, reasonable, and supportable information. This is not only our view, but the view of regulatory authorities globally.[1] Given this, it is important to think of weights given to scenarios to reflect the likelihood that they will materialize, which is a key requirement of IFRS 9.

When there are many possible outcomes, a representative sample of the complete distribution of outcomes can be used to help determine weights. Each scenario is given a weight based on the subset of scenarios with similar outcomes. For example, with all possible scenarios placed in order of severity, the 100th percentile scenario is the best case and the 0th percentile is the worst, leaving the 20th percentile scenario as the ‘downside’ one.

Within the constraints of the discrete scenarios selected, if the 20th percentile scenario is considered to be representative of expected losses for all scenarios between the 0th and 35th percentiles (as the loss profiles are relatively flat between these percentiles), then that scenario would be given a 35% weighting for ECL measurement in order to be unbiased, not 20%. It is worth noting that the weights must add to 100%, since the selected scenarios are representative of the complete distribution.

The need for regular reviews of scenarios and weights is important given the rapidly developing impact of COVID-19 in the short-term, and the eventual economic rebound in the mid-term. Institutions should consider the interactions between scenarios and weights. For example, a worsening economic environment from one reporting date to the next may be captured in one of three ways:

  1. 1. Weights applied to the negative scenarios are increased, but scenarios remain unchanged.
  2. 2. Scenarios are altered to reflect the worsening conditions, but weights remain the same.
  3. 3. Both scenarios and weights are altered.

Institutions should be mindful of the potential for double counting in the current environment by drastically altering both scenarios and weights.

2. The timing of an economic turnaround and the resulting impact on lifetime PDs

With the economic and social disruption caused by COVID-19, it is difficult to forecast beyond 2020. However, given the asymmetric nature of this event and the profound levels of government support, economists expect a strong rebound once the peak of the virus has been passed and measures introduced to combat the spread are slowly lifted.[2] Institutions that are subject to IFRS 9 will need to consider this potential economic revival. In this context, the European Securities and Markets Authority (ESMA) highlighted the recent European Central Bank’s recommendation that, given the current state of uncertainty linked to the COVID-19 outbreak, issuers give a greater weight to a long-term stable outlook as evidenced by past experience and take into account the relief measures granted by public authorities, such as payment moratoria. [3] This will impact both ECL numbers and the assessment of whether or not a significant increase in credit risk has occurred.

S&P Global Ratings, like many other organizations, expects economies to rebound at the end of 2020.[4] The fact that we are almost through a third of the year is of particular relevance given that IFRS 9 ECLs have 12-month risk horizons. This means that ECL numbers should partially reflect both the depressed economic conditions occurring in 2020 and the rebound in 2021, with the latter featuring more heavily as we move through 2020.

The above can be achieved by using a pro-rata weighting method. This can be as simple as estimating PiT PDs for 2020 and 2021 and weighting them according to the number of days remaining in the year.

3. The impact of government emergency financing and support

Governments around the world have provided unprecedented support packages to economic players such as central banks and, in many cases, this has been directed to entities within the players’ respective systems. Multiple regulatory organizations have mentioned this support and the need to reflect it within ECL calculations. There are two principle elements in our CCPO that can be used to capture this support. The first element is via the input data to the forecast. The second element includes all support provided directly to individual entities or sectors that may be captured in a credit assessment, which is typically considered by analysts when undertaking credit assessments. In cases where there are exposures to unrated entities, a robust support methodology should be employed to reflect this support within internal credit ratings. Since a credit assessment is the starting point for the adjustment of LT PDs to PIT PDs, any government support considered within the assessment should be consistent with all other risk management purposes. That is, assessments should not be adjusted for the sole purpose of use within the CCPO (i.e. for impairment estimation only) to avoid inconsistencies with regulatory capital calculations, pricing, and more.

Estimating ECL is very challenging during these difficult times. The three considerations outlined above may help your institution as you look to use reasonable and supportable information to provide transparent and timely information about potential credit risks.

[1]For example: “Covid-19: IFRS 9, capital requirements and loan covenants”, Bank of England, March 26, 2020,

2 S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence. Lowercase nomenclature is used to distinguish the credit scores generated by the model from the actual S&P Global Ratings credit rating.

[2]“Covid-19: IFRS 9, capital requirements and loan covenants”, Bank of England, March 26, 2020,

[3]“Accounting implications of the COVID-19 outbreak on the calculation of expected credit losses in accordance with IFRS 9”, ESMA, March 25, 2020,

[4] “COVID-19: The Steepening Cost To The Eurozone And U.K. Economies”, S&P Global Market Intelligence, March 26, 2020,

For more information on our IFRS 9 solutions
please contact us here