Ty Lambert, Chief Data Analytics Officer, BancorpSouth
Could you please tell our readers a little bit about yourself, your experience and what your current professional focus is?
I joined BancorpSouth in 2006 and have held a variety of management positions with responsibilities including balance sheet management, credit risk management, capital stress testing, and corporate planning. As the Company’s Chief Data Analytics Officer, my team is responsible for providing strategic guidance and driving data-related change across the organization. Prior to joining BancorpSouth, I was an investment portfolio manager. I received my bachelor and MBA degrees from Mississippi State University and the University of Mississippi, respectively.
What, for you, are the benefits of attending a conference like the CECL Congress and what can attendees expect to learn from your session?
The CECL Congress is an ideal setting to allow a “brain trust” of subject matter experts in the industry to discuss a variety of topics relating to a sound CECL implementation. Often times, the practitioners can offer the most clarity as institutions work toward a common standard. One of the many challenging topics includes a thoughtful approach to generating a timely, repeatable, and supportable economic forecast from which to derive expected credit losses for each quarterly loan loss reserve estimate. As a result, we have constructed a dedicated session to this very topic in an effort to highlight key considerations for developing an economic forecast for CECL, including scenario generation, scenario application, length of time horizon, and impact to other qualitative elements of the reserve calculation.
You will be presenting on scenario generation for forecasting and the approaches for successful implementation, in your opinion what does successful implementation look like & how can scenarios be leveraged under CECL?
The economic indicators included in an institution’s scenario design should be relevant to the institution’s risk profile, thereby showing evidence of how changes in selected economic indicators impact credit losses. This means that there should be some historical correlation between the economic drivers and the institution’s credit loss experience as well as a reasonable expectation that this same relationship would manifest in the future, granted that the magnitude of impact to credit losses may not be on par with historical experience. In addition, the scenario design should be mindful of significant changes in the overall credit profile or related exposure for an institution’s loan portfolio. This is typically the result of activity with respect to mergers and acquisitions. Finally, the forecast should be flexible enough to accommodate multiple stages of an economic cycle, allowing for extremes when warranted.
What are the key challenges financial institutions face in regards to forecasting and how should institutions approach determining a sufficient number of scenarios?
Forecasting is certainly as much art as it is science, so the notion of settling on a consensus forecast often considers the possibility of having variation within expectations. A common approach to addressing this concern is by way of probability weighting possible outcomes to derive a blended economic forecast that preserves its integrity for being reasonable and supportable.