Modeling climate risk, and analyzing your risk of default, is extremely difficult – particularly if you are an electricity provider. All one has to do to realize the enormity of this challenge, and the potential havoc climate change can wreak on a business’ bottom line, is to consider recent events on either side of the Pacific.
It is now clear that a deep COVID-19 recession will hit the global economy this year. This event, completely distinct from anything that has happened in living memory, will bring a new set of challenges, and a host of opportunities, for risk modelers around the world.
Those engaged in social science – a category that includes most forms of risk modeling – are often accused of being bad scientists. The accusations generally come from physical scientists, who enjoy the luxury of access to repeatable experiments with few real-world consequences. However, most would rightly frown on a bank offering loans to those who can’t afford them just to gather data to improve their models for future use.
Can disruptive technologies ever truly and completely replace human judgment in finance?
We can begin our search for answers by considering automation progress in other industries. In the world of self-driving car technology, for example, it is common to talk of five levels of automation.
Since the issue of climate change stress testing burst onto the radar screen, I’ve become a little obsessed by Hurricane Katrina. That event, in 2005, not only caused widespread human suffering and enormous insurance losses but also dramatically altered economic growth prospects for New Orleans, Louisiana and – more generally – Gulf Coast communities.
When banks manage risk, conservatism is a virtue. We, as citizens, want banks to hold slightly more capital than strictly necessary and to make, at the margin, more provisions for potential loan losses. Moreover, we want them to be generally cautious in their underwriting.
My team did a big validation project for a financial institution a few years ago. We were actually the backup, external validators called in to resolve a disagreement between the model build team and the internal validation group.
The industry is currently a hive of CECL-related activity. Many banks are busily testing their systems or finalizing their preparations for the go-live date, which is either in January 2020 or somewhat later, depending on the organization. Some are still making plans for implementation, and the rest are worried that they should be.
Last year, the US Congress and President Trump enacted major revisions to the Dodd-Frank Act that dramatically reduced the scope of stress-testing regulations. Now, with the Comprehensive Capital Analysis and Review (CCAR)/Dodd-Frank Act Stress Test (DFAST) season in full swing for the first time since the changes, it is a good occasion to step back and assess the health of the stress testing firmament.
For risk modelers at lending institutions, climate change represents an enormous challenge. Until now, stress testing has focused on acute short- and medium-term recession risk and has avoided questions of long-term strategic risk management. Climate change is a slow-moving train wreck that has been happening, very gradually, across the span of most bank databases.