A Perfect Storm: COVID 19, Financial Services & Model Risk
As the world transitions toward a “new normal” of co-existing with COVID-19, the economic implications of the outbreak continue to emerge.
For financial institutions – banks, insurers and asset managers – the diverse range of potential economic outcomes is proving very complex to negotiate.
They face many questions. Will there be a second spike? How many furloughed workers will ultimately be able to return to work? Will the recovery be as swift as the slowdown? Will interest rates go negative? How will governments change their tax and spending priorities to fund their extraordinary interventions?
For financial institutions, these are hugely important issues, with profound implications for their balance sheets, their customers, their shareholders, their regulators, and ultimately their profitability.
Now, more than ever, financial services are looking to their modeling teams to help chart a course through the choppy waters, towards the smoother waters beyond. Modelers are helping their senior management to understand how various outcomes will affect their institutions, and help them generate options that mitigate the risks.
Models must contend with unprecedented challenges
One challenge for modelers is that some of the measures and outcomes are so extreme – millions signing on for unemployment each week, potentially negative nominal interest rates, widespread mortgage non-payments for example – that they fall well outside the normal assumptions used to model GDP growth, default rates, and interest rate risk, as just a few examples. Some models will be able to handle these extremes and produce valuable results; others may not.
Another challenge is the absence of reliable data to use in models. For example? Currently, in the UK, banks have introduced payment holidays on mortgages, personal loans, and credit card debt. While welcome news for hard-pressed borrowers, the absence of default data means trying to assess the impact on default rates during the pandemic is almost impossible to estimate. It will only become clear once the situation returns to somewhere near normal.
To get around these issues, and provide insight and options for senior decision-makers, model teams are having to develop new models – at speed – that reflect the new reality and provide fresh answers. Where their existing models are not proving adequate, they are developing End-User Computing (EUC) applications using platforms like Python, R, MATLAB, and even highly complex spreadsheets.
These can be set up quickly and can accommodate new assumptions and new types of inputs to create meaningful insights into areas like portfolio performance, credit risk, market risk, as well as wider decision-making options.
Putting proper controls in place over EUCs
While hugely valuable, their development outside the standard corporate IT environment, with its checks, controls, documentation and full transparency, means that institutions are exposing themselves to model risk.
Without proper controls in place – even in these exceptional circumstances – institutions are facing operational, reputational commercial, and regulatory risks. There has been little indication that model regulations like the UK’s SS3/18 or the US SR 11 7 are being relaxed in the current circumstances.
Somehow, and quickly, model teams need to find a way of ensuring that controls, documentation, and checks are in place for these powerful and complex applications, that staff have commissioned themselves, probably from their sofa.
The current situation is making exceptional demands of modelers to address a range of extreme outcomes, any of which seem possible at any given moment. Their skill, ingenuity, and expertise will see them through, but care needs to be taken to ensure that the absence of controls and transparency do not compromise their efforts and results.