Previous economic downturns have been caused by shocks to the financial system, such as stock market crashes and credit crunches. This current economic crisis is different – a global pandemic and each government’s response have changed market forces in an unprecedented manner. As banks brace for the economic headwinds and potential increase in customer defaults, provisions for the associated expected losses continue to mount. Government interventions coupled with regulatory action will help to reduce the impacts on the sector.

Recent changes to accounting rules mean that the previous method of recording provisions on an incurred loss basis was changed to an expected loss approach as required under International Financial Reporting Standards 9 (IFRS 9). The expected loss approach requires modelling credit risk indicators to estimate losses that will emerge for current loans. 


Emerging themes  |  International perspective  |  Timing  |  Focus areas

Emerging themes

Forward-looking provisioning estimates require forecasts of a portfolio’s future state risk profile. Perfect forecasts don’t exist, so a number of forecasts, each with an associated likelihood of occurring, are used. The expectations around the forecast likelihood will change so provisioning levels will need to be continually adjusted. Generally, COVID-19 impacts are captured through adjustments to these forecasted model inputs and other out-of-model adjustments, rather than by changing model design.

Banks are now finding that previous behavioural trends indicating risk have been replaced by new risk indicators in response to external influences. In the context of COVID-19, the speed and levels of government intervention have changed the timing that we can expect customers to show elevated levels of risk. This reduces the level of reliance that can be placed on modelled outcomes when estimating loan loss provisions.

This has meant that human judgment is being used more in the risk modelling process. Using human judgement to establish key risk drivers alleviates some concerns around automated risk assessments biased on historical experience, but it does make it difficult to directly compare portfolio risk composition.

The increased importance of qualitative risk management and its effect on provisioning has meant there needs to be more work done to ensure clear and transparent reporting. However, differences between geographies and government responses to the crisis make direct comparisons challenging.

International perspective

Provision coverage ratio is a commonly used metric to assess a bank’s risk profile relative to peers. This captures the total impairment provisions held by a bank as a proportion of total gross loans and advances. It reflects total funds set aside for losses as a percentage of the principal amount of loans and advances written by the bank. Across the world, there have been substantial increases in provisioning coverage ratios, up to 2.5 times, owing to COVID-19 and the broader economic slowdown.

ECL provision coverage ratios
ECL provision coverage ratios

An international comparison of the impact of COVID-19 on coverage ratios for the last reporting period pre COVID-19 and the first reporting period post COVID-19 is shown for selected countries in the chart above. The relative levels and increases in coverage ratios have generally mirrored a bank’s portfolio mix and geographical location.

For a number of reasons including population growth, sustained property price increases and tax incentives, the Australian banking segment is heavily skewed to residential property lending. Given this comparatively low risk portfolio mix, Australian bank coverage ratios have typically been lower than those of international peers. The impact of COVID-19 has been very similar across the Australian majors.

The UK banks have a wide spread of portfolio types and have seen provisioning levels increase markedly. In France, provisioning coverage ratios increased the least due to the impact of COVID-19, albeit from a significantly higher base. This contrasts with their near neighbours in Switzerland where coverage ratios increased but remain comparatively low.

In January 2020, the provisioning approach of US banks moved from a one-year historical loss model to a lifetime expected loss model. This, coupled with the country’s economic conditions, has created the most pronounced increases in provisioning coverage ratios.

The similarity of the level of provisioning and impact of COVID-19 on expected credit losses within a country is clear but the cause is less so. In addition to credit quality considerations, other factors may include local regulatory interpretations, government stimulus action, benchmarking to domestic peer banks, cultural influences and the actual or perceived impact of COVID-19 within a country.

Timing considerations

KPMG conducted a survey of banks across Europe to gauge industry sentiment on the expected timing of the loan impairment peak, and by extension the impact that COVID-19 restrictions and the offsetting government interventions would have on impairment provisions.

The results showed the majority of respondents (53 percent) believed that impairment levels have not yet peaked, as potentially, relief measures made available are supporting businesses and masking the true impact. Another large portion (47 percent) of respondents thought the peak has already passed due to the speed with which governments enforced COVID-19 restrictions.

Expected peak in impairment levels in the EU
Expected peak in impairment levels in the EU

Focus areas moving forward

With the uncertainty of the COVID-19 crisis and its continuing effects on bank policies, risk modelling and accounting, it is important that banks have a robust, technically sound and agile system in place to quickly identify the models, data and outputs that are sensitive to COVID-19-related decisions. Banks also need to determine key modelling levers that can be used to stay on top of modelling challenges (for example in relation to recalibration or model overrides).

It will be critical to undertake targeted credit quality reviews and intelligently capture data in customer interactions when considering the potential timing of issues flowing through portfolios. This data will be valuable for banks in risk decision-making and mapping out customer strategies through the pandemic.

For banks to cope with this crisis as it evolves, banks will need a strong and thorough governance framework related to adjustments on credit risk models, accounting policies and decisions taken.

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