As financial institutions navigate the complexities of Current Expected Credit Loss (CECL), it is crucial to move beyond simply running the models and effectively test the model’s response to varying economic forecasts.
This session focuses on the practical techniques for designing and executing meaningful sensitivity tests. Furthermore, the webinar will emphasize how to develop a strong conceptual framework for understanding the expected relationships between your key model variables and their associated macroeconomic covariates.
We will provide a structured approach for selecting appropriate forecast adjustments (shocks). Attendees will leave with the ability to better interpret the resulting model impact on the Expected Credit Loss (ECL) reserve.
What You’ll Learn
- Decoding Variable Relationships:
Develop a strong conceptual framework for understanding the expected relationships between your key model variables (e.g., probability of default, loss given default) and their associated macroeconomic covariates (e.g., unemployment rate, GDP growth, housing prices).
- Selecting Meaningful Adjustments:
Learn a structured approach to identifying and selecting appropriate forecast adjustments (shocks) that effectively test the model’s response to various economic scenarios. We will discuss how to choose inputs that challenge the model’s underlying assumptions.
- Interpreting Model Impact:
Gain insight into how a selected forecast adjustment will translate into an impact on your final Expected Credit Loss (ECL) reserve, allowing you to better justify your CECL methodology to auditors and regulators.
Who Should Attend
This session is designed for credit risk, finance, and accounting professionals responsible for CECL modeling and allowance reporting. It is also valuable for risk managers, model validation teams, and leaders involved in regulatory and audit discussions.
