Key risk indicators to manage macro-economic uncertainty
David Buck, VP, Head of ERM Programs & Analytics, USAA
Below is an insight into what can be expected from David’s session at Balance Sheet Management USA.
The views and opinions expressed in this article are those of the thought leader as an individual, and are not attributed to CeFPro or any particular organization.
How can banks look to create agile financial plans and forecasts?
It is always best to start simple and evolve as you go, develop something that provides a minimum viable product and then continually enhance that process. Some banks have to start from scratch here, though banks already adjust forecasts to understand yield curve movements and other macroeconomic changes, particularly with the recent environment. Organizations should start with the most impactful drivers and then move to others before looking for ways to improve the timing and quality of that turnaround.
As an example, Banks will look to take a given forecast and understand how a potential fed rate hike will change that forecast. The quickest answer may be to take treasury’s interest rate sensitivity analysis result and use it to inform a net interest income overlay to the forecast. At the same time, the credit teams are also looking to see how those same factors might change allowance needs. If teams are thorough, product teams are also looking to see how those same factors could impact demand and/or balances. Having a forecast team that can assemble all those moving parts into a cohesive forecast, on a routine basis, is key.
This probably sounds familiar to some of the readers; it’s a very crude description of a stress testing process many banks go through every year (although those forecasts are hypothetical). For banks that have a stress testing infrastructure, there is an even better solution, though it may seem terrifying at first…why not merge the two functionalities? Leverage the models to help inform routine ‘scenarios’ to accompany forecasting processes. Models/estimations can significantly increase turnaround time and well-parameterized models could provide useful insights to your teams (but don’t forget the importance of managerial discretion in the process!).
How has increased inflation with low unemployment affect the macroeconomic environment?
That’s an incredibly difficult question to answer. The complication, to me at least, is that historically increased inflation causes the Fed to raise rates in an effort to cool economic growth in an attempt to bring down inflation; the usual byproduct of this is often increased unemployment. Having both coexist, presents several difficulties for policymakers.
It does not require an economist to notice that the recent combination of factors is causing a ‘rethink’ of some core economic assumptions. For example, Federal Reserve speeches have indicated that today’s 3.5% unemployment rate is below the natural rate and needs to rise to help reduce inflation. If you remember back in 2019, when unemployment was also around 3.5%, inflation was low and stable.
So, what has caused the nature of this employment/inflation relationship to change? As economists untangle that root cause analysis, it’s exciting to see how that could reshape our thinking on core economic principles.
Why should banks look to incorporate risk management practices in their strategic planning?
The answer is two-fold; first, the practice of assessing key processes for risks and then ensuring there are controls in place to mitigate those risks (often called RCSAs) is a great way to make sure the strategic plan process itself is sound. Disparate assumptions (economic or other), lack of business line review, unverified data, all could have significant impacts as to the quality of your plan. Hopefully all banks, regardless of size, have some assessment/control practices in place.
Secondly, as the strategic plan shapes into a forecast, it makes sense to understand what the key factors/drivers are that should cause that plan to go sideways. Drops in demand for certain products, changes in debit interchange regulations, reliance of certain industries etc. All these things could disrupt the ability to achieve a banks’ given plan.
Banks should understand just how much volatility in that plan they can handle before breaching risk appetite thresholds. It’s a good idea to have clear and regular sight into just how much they are absorbing before approaching those thresholds so predefined contingencies can be activated.
In what ways can banks adapt their process for different scenarios?
Banks shouldn’t wait for the ‘perfect’ solution to start building in scenarios. Assess the current processes AND CULTURE; what possible solutions would facilitate a smoother transition? Start there.
There are a few adaptations that will need to be considered.
- One; data and information flow. This means a set of assumptions that will feed into multiple forecasts, requiring a little more time for lines of business to gain alignment (at least initially).
- Two; tools and technology. As more information is gathered, banks will likely need better tools to do so. How to communicate the important details from numerous business lines and how to aggregate in a meaningful way is critical.
- Three; process and governance. How to assemble these forecasts, assess the differences (what they mean, how to incorporate into decision making, etc.) will take time. Also, how this information is conveyed to decision makers and the Board could also take time.
- Lastly, socialization. Agile forecasting is essentially looking to solve a series of ‘what if’ questions and then having the flexibility, information, and processes to adjust. Constructing an implementation that can recognize benefits while phasing in costs/effort will help keep moving forward.
How did the pandemic impact labor markets?
There are two main categories of impact from the pandemic: the distribution of the labor pool and the sentiment of those in it.
Service industries (mostly those tied to hospitality, travel/tourism, etc.) slashed employees when demand plummeted, causing many of those people to find other means to support themselves and their families. Now years after that initial impact, getting those workers back seems difficult. Our labor pool has adjusted; not inherently good or bad…but definitely different.
In addition, how we work (and want to work) is very different. Individuals have different expectations. Flexibility and non-monetary incentives have become more important (a trend that can also be attributable to factors prior to the pandemic as well).
On the supply side, many companies have realized previous geographical constraints may have been overly punitive. Companies have opened up to search for talent in areas beyond their HQ/office footprint. This relaxation is causing employers to be even more sensitive to their existing employee’s needs. With return to office (RTO) programs still ramping up for many large organizations, this will likely dissipate some, but the impact has already been felt and is unlikely to completely go away; there are benefits to a remote workforce.