As the world at large enters the second year of the Coronavirus disease 2019 (COVID-19) pandemic, people, governments, and businesses are still trying to get a handle on the devastating impact of the pandemic on people and the economy. Governments across the world continue to impose significant restrictions on the movement of people and goods across borders and jurisdictions in an effort to contain the pandemic. Businesses are dealing with significant disruptions in supply chains, altered operating processes and lost revenue as they try to soldier on through this extended COVID-19 environment. While various governments have provided both financial and non-financial assistance to people and businesses in their respective countries, millions of workers have lost their jobs, many businesses, particularly those whose operations call for close in-person contact, have not survived the pandemic. Vaccinations, which are an essential tool to fight the pandemic, are gaining steam across the world, allowing for limited relaxation of travel and public restrictions in some countries. However, the emergence of new COVID-19 variants has brought new challenges to the situation, adding more uncertainty to the economy.
In this article we briefly discuss some of the COVID-19 induced considerations on IFRS 9’s Expected Credit Loss (ECL) reporting requirements. IFRS 9: Financial Instruments requires expected credit losses to be assessed on a forward-looking basis, measured as an unbiased, probability-weighted amount, using reasonable and supportable information that is available without undue cost or effort at the reporting date about past events, current conditions, and forecasts of future economic conditions. [IFRS 9.5-17].
Decoupling of historic data indicators from current conditions and expected future economic conditions
COVID-19 has significantly impaired the usefulness of past events data that is used in tools to estimate future economic conditions. Businesses with customers that had good credit history and good credit ratings prior to COVID-19 in industries such as the hospitality industry now cannot wholly rely on such historic data as COVID-19 has dealt a significant blow to such customers. Many small to medium sized businesses are going under with very short notices, while large businesses are grappling with significant cashflow challenges evidenced by the tighter prioritization of which suppliers get paid on time and the almost wholesale renegotiations to extend payment terms. Existing ECL models use historic credit data to derive links between changes in economic conditions and customer behaviour, and other ECL parameters such as loss rates, probabilities of default and loss given default etc. COVID-19 conditions have significantly impaired these historic data tools such that businesses need to revisit their ECL models to make appropriate updates.
COVID-19 Debt Moratoria
Most governments across the world have promulgated various forms for debt moratoria during the COVID-19 period, barring businesses from collecting outstanding amounts from people and businesses deemed to be adversely affected by the effects of COVID-19. COVID-19 moratoria on various kinds of rents are good examples; many governments have barred businesses from collecting outstanding rent accounts or barred evictions of individuals or business tenants who fail to pay rent during the COVID-19 period. While this development was expected to remain largely contained if COVID-19 lasted for one year or less, now that we are entering the second year of the pandemic with a highly uncertain outlook, this development has now grown into a significant credit risk. These are new COVID-19 specific debt protections that have a significant impact on the calculation of expected credit losses that businesses should factor into their ECL models.
Disruptions in the traditional ways of identifying SICR
The COVID-19 environment has adversely affected data that is used by many businesses and economic institutions to identify the “Significant Increase in Credit Risk” (SICR). Some elements of SICR data have become unusable, while in other areas there have been significant delays in the timely availability of important SICR data. Current models were built to handle an economic downturn, but not a sudden halt in both supply chains and demand side of economic activity across the world. The acuteness of this impact is unprecedented such that responses from significant consumer sectors are at best partially guided by psychological fear, making it difficult to predict otherwise rational decisions, such as labor supply and consumption of services, involving close proximity to others. This has rendered some traditional credit risk indicators that are used in ECL models unreliable or has significantly delayed availability of some of these indicators as economic institutions and authorities take time to try and make sense of the data before they make it available to the public. For example, credit indicators such as delinquency data for certain industries now need qualification under this COVID-19 environment due to the impact of government mandated debt moratoria, and other debt forbearance programs that are masking delinquency indicators.
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