TACKLING THE CREDIT RISK CHALLENGES OF COVID-19 AND IFRS 9

Georges Bory, Managing Director, ActiveViam

 

Financial institutions are facing challenges from two fronts with regard to the way credit risk engines work. One is the economic turbulence caused by the Coronavirus pandemic; the other is the requirements of the international accounting standard, International Financial Reporting Standard 9 (IFRS 9). Although vastly different in nature, each has an urgent need for real-time data and analytics to be built into credit risk processes.

 

Impact of pandemic

The impact of COVID-19 has caused ongoing disruption to the global economy. GDP in the Eurozone, for example, shrunk by 3.6 percent in the first quarter of 2020 as a result of the global pandemic, with the ECB expecting an overall contraction of 8.7 percent for the year.

Such a sharp economic downturn means banks will need to assess credit risk in a very different way to the previous year. With more data variables to consider, calculating Expected Credit Loss (ECL) will be much harder for the foreseeable future. Many more circumstances across the entire credit risk lifecycle – from loan initiation to implementation and management – now require multi-dimensional data analytics in order to provide an accurate view of the credit risk picture.

Every loan book will have non-performing exposures that need to be addressed, for example. In addition, as a growing number of people and businesses find themselves in vulnerable financial circumstances, initial credit decisions will now require the introduction and modelling of new COVID-19 sector and sub-sector criteria, as well as alert and triage systems to isolate loans in trouble.

And these aren’t the only challenges around calculating ECL.

 

Managing IFRS 9

At the same time as having to deal with the fallout from the Coronavirus, financial institutions must manage the IFRS 9 standard, which also demands they look differently at credit risk decisions, and categorise them in a very specific way.

Financial institutions would once have looked at aggregated data when provisioning their default losses at the end of a fiscal year. Since the introduction of IFRS 9, however, they must now use multi-dimensional data analysis to assess the ECL upfront, and continue monitoring critical assumptions as the ECL changes – understanding why it changes, and what’s driving that change.

IFRS 9 requires financial institutions to define, based on a combination of quantitative data analysis and qualitative judgement, whether a loan at a particular stage is showing signs of slipping into stage two or three of “days past due” – referred to as Significant Increase in Credit Risk (SICR). As risk increases, more collateral is required to secure against the loss.

 

A proactive approach

One thing we’ve learned from the pandemic is that it highlights the importance of being proactive about credit risk and default. Rather than a reactive approach, in which losses are made after the fact, preventative measures underpinned by real-time analytics are now needed to manage loss levels.

This approach requires banks to transform their credit risk engines in order to comply with IFRS 9, and understand SICR and the new ECL demands arising from the pandemic. Here, then, are some best practices to maximise the data analytics that lie at the heart of this transformation.

1 – Bring Risk and Finance Accounting together

While Credit Risk sits with a financial organisation’s Risk team, IFRS 9 compliance sits with its Finance Accounting team. By bringing everyone working across those teams into the same analytics environment, they can all share exactly the same calculations and insights.

2 – Enable multi-dimensional drill-down capacity

To ensure their calculations and categorisations are correct, it’s important that teams are able to drill down into huge data sets. And with each client record requiring at least 12 months’ worth of data, there needs to be capacity to carry out multi-dimensional views on such vast amounts of information. Spreadsheets aren’t sufficient, however, so smart investments need to be made in software that is.

3 – Create a notification system

Creating a notification system, and alerting risk analysts to when a change is occurring, will enable them to carry out the appropriate investigations and take action if and when necessary.

4 – Empower customer services

If customer services are to be proactive, it’s important to empower them by giving them the information they need to address the loans that are in trouble. The best way to do this is to set up relevant communication channels, and assign ownership where needed.

 

Challenging times

COVID-19 has had a big impact on calculating ECLs and, in doing so, has made the job of risk analysts much harder. Adding to this, the requirements of IFRS 9 mean credit risk engines are due a sizeable overhaul to ensure ongoing compliance. Those financial institutions that have recently applied the new standard will need to consider adjusting models to account for the economic effects of the Coronavirus and what they mean for the ECL metric in the longer term. These are, undoubtedly, challenging times but, by aligning teams and technology, institutions will be taking significant steps to improving their credit risk engines for an uncertain future.

 

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