As businesses prepare to welcome the last quarter of 2021 with hope and silver linings, COVID-19 pandemic-related uncertainties and challenges persist to evolve and disrupt the balance that businesses are trying to strike. The pandemic did not spare financial accounting and reporting of all corporate entities as well. It has further elevated the use of accounting judgements and estimates due to its fluid nature and limited experience in addressing its financial impact. The COVID-19 pandemic is the first economic crisis since the Philippine Financial Reporting Standard (PFRS) 9, Financial Instruments, was adopted and applied for the first time in 2018. PFRS 9 specifies how an entity should calculate expected credit loss (ECL)the estimated expected cash shortfalls on credit exposures such as receivables and other financial assets.

This article focuses on the expectations on how entities will measure ECL moving towards December 31, 2021. We reflect on what we have observed and learned from the 2020 financial reporting cycle and other relevant experiences and visualize how entities would use them in their ECL assessments. We do not discount that these expectations may evolve as the COVID-19 situation continues to unravel. New operational and application challenges will continue to rise, and the extent may depend on the underlying facts and circumstances. ECL models need also to evolve in response to these developments to avoid creation of false sense of financial performance.

Unbiased probability weightings

Unbiased probability weightings, one of PFRS 9’s required ECL measurement parameters, are used to account for the range of possible credit loss outcomes that an entity expects in the future. Entities usually allocate the total weightings into three mutually exclusive scenarios: the base, upside, and downside. However, in recent experience, we encounter actual market data outside the forecast band of economic managers and consensus data of analysts. For example, according to the recent Philippine Statistics Authority Inflation Report, inflation at the national level was at 4.9 percent in August 2021, which is far beyond the Bangko Sentral ng Pilipinas’ target band of 2.0 to 4.0 percent for 2021 and 4.3 to 4.4 percent consensus data from analysts. This might give us a hint of the possibility that extreme scenarios are not being considered and accounted for in measuring ECL. Hence, entities may need to consider allocating an “extreme downside” as the fourth mutually exclusive scenario in the usual total weightings to also reflect the risk of extreme volatility of macroeconomic data and high uncertainty in economic outlook.

Portfolio segmentation

ECL is generally measured based on type of receivable or by portfolio. Generally, entities group financial instruments based on shared credit risk characteristics and reasonable and supportable information available on a portfolio basis. However, as we now know, the pandemic has not affected exposures based on such grouping. Its impact differs in significance based on industry sectors. Therefore, entities should consider re-segmenting portfolios that would reasonably reflect shared credit risk characteristics in the pandemic setting.

Post-model and in-model adjustments

The COVID-19 pandemic has developed rapidly in 2020 and left entities no sufficient time to incorporate the specific effects of the crisis in their existing ECL models. These existing models are built on historical data with no comparable information on COVID-19. Consequently, for the 2020 reporting cycle, most entities have introduced post-model overlays to capture such specific effects. This necessitated the application of more judgments in all key steps in estimating ECL, which would result in an increased the risk of error in judgment, and resulted in varying increases in ECL measurements and even potential double counting of ECL.

For the 2021 financial reporting cycle, more in-model adjustments are expected. This means re-calibrating identified model limitations and deficiencies noted in 2020. The adjustments on expected delinquency on loans enrolled in financial support schemes must be considered because of the potential suppressed and delayed effect on default and amplifying industry sector idiosyncrasies to differentiate the severity of projected default rates. Entities may need also to update correlations and assumptions incorporating the effect of government and other similar support schemes, and the timing of vaccination rollout, which is linked to the opening and recovery of the economy. 

Significant increase in credit risk (SICR)

In 2020, most entities have isolated the effects of the pandemic in measuring ECL. However, it is now common knowledge that its effects will stay on longer than initially expected; hence, these became more embedded in the ECL measurement for 2021.  The pandemic has challenged most entities’ judgement of what triggers SICR. Specifically, banks could not rely on the usual risk indicators, such as movements in liquid assets, arrears, and restructuring measures, as much as they used to pre-pandemic. Entities may need to consider the following qualitative factors: (1) identification of vulnerable industry sectors fine-tuned based on recent developments; (2) a clear differentiation of borrowers facing temporary liquidity issues from borrowers facing permanent or lasting financial difficulties; and (3) more sensitive risk indicators for borrowers enrolled in financial support schemes.

Sensitivity analysis

Philippine Accounting Standards 1, Presentation of Financial Statements, paragraph 129 (b) requires entities to disclose sensitivity of carrying amounts to the methods, assumptions, and estimates underlying their calculations, including the reasons for the sensitivity. Based on the publicly available 2020 audited financial statements of several universal banks in the Philippines, there were some that disclosed its sensitivity analysis for ECL measurement. For those who have a sensitivity analysis disclosure, the single-factor analysis (i.e., varying only one of the inputs in isolation) and multi-factor sensitivity analysis (i.e., varying multiple inputs within a scenario at the same time) were used.

Given the elevated uncertainty, entities need to improve on the level of disclosure in this area to allow primary users of financial statements to better understand the degree and range of estimation uncertainty at the reporting date. The periodic exercise of measuring ECL gives greater reason to adopt a multi-dimensional and multi-directional point of view; that is, thinking outside the box and creating new dimensions and directions that did not exist before.

With the above expectations, one interesting question floats – does expecting the unexpected credit loss (UCL) make it expected? Do entities need not only measure ECL but also the UCL? The answer is a categorical “yes,” even though PFRS 9 only mentioned ECL. If this question boggled the mind and seems hanging, it is because it connotes a deeper meaning and saying yes is just not enough. It is another way of saying, be prepared for anything even if it is unknown; hence, making the unexpected, expected.


As published in The Manila Times, dated 22 September 2021