Event Profile

Even with robust vendor solutions, Current Expected Credit Losses (CECL) models can struggle in periods of uncertainty.

Forecast‑based models can become too sensitive to economic assumptions, while current‑conditions models may miss turning points. Qualitative adjustments can swing each period to make up for these gaps—undermining confidence just when clarity is needed most.

Darling Consulting Group experts share real-world case studies from their validation work, highlighting the most common model limitations and propose solutions that you can use at your institution to ensure your CECL model remains balanced and fit-for-use.

Learning Objectives:

• Identify common limitations found in CECL model methodologies.
• Describe how model sensitivity to economic forecasts and current conditions can affect CECL results.
• Recognize when qualitative adjustments may be compensating for quantitative model weaknesses.
• Apply practical solutions to keep your institution’s CECL model balanced, reliable, and fit-for-use in changing environments.

Duration: 60-minutes

Presented live and recorded on 10/14/26.