Executive summary

The Covid-19 pandemic is extracting a terrible toll on lives and the global economy. With growth plunging and unemployment levels darting up, the world is marching towards a recession.

Financial markets have been walloped with ~$5 trillion wiped off global equities since the last week of February 2020. Supply chains have snapped in many places, and crude oil prices have plummeted. A material decline in demand will likely create cash flow problems across economic functions, even as central banks respond by slashing interest rates significantly and injecting liquidity. To wit, on March 20, 2020, the Bank of England rolled out a slew of measures to offset rising stress.

The uncertain milieu also has severe implications for credit markets, including a likely surge in defaults. To be sure, some steps have been taken by banks to protect mortgage borrowers by offering them payment holidays, and to companies by restructuring distressed loans or temporarily freezing repayments on credit facilities.

But all such mitigation efforts impact credit-risk data collection and modelling, with banks requiring to make changes to their systems, processes and governance mechanisms.

While this can challenge lenders, they need to view this as a great opportunity to enable more robust model calibration, spurred by potentially higher obligor defaults.

In this paper, we provide an overview of the impact of the pandemic on credit-risk data collection and modelling, with respect to the definition of default, International Financial Reporting Standard 9 (IFRS 9) provisioning, and internal ratings-based (IRB) modelling.

We focus on the steps banks need to take on credit-risk data collection, process and governance to ensure they continue to meet regulatory requirements. We believe the learnings will help banks create more robust models and ensure operational resilience both during and after the pandemic.