Article
How to maximize the value of a CECL model validation
June 27, 2025 · Authored by Ivan Cilik, Sean Statz, Sam Hoffman
Current Expected Credit Losses (CECL) models generally represent high risk compared to other models that lending institutions use. Not only does the CECL estimate go directly into an entity’s financial statement, but it is likely to be subject to audit and regulatory review. A model validation can help ensure you’ve completed all CECL requirements, while also preparing you for future regulatory requirements. It provides valuable insights related to sensitive inputs and assumptions in addition to testing model logic and algorithms. Even if you have already had a validation completed on your CECL model, it is still vital to ensure you are getting the most out of your validation and tie up the loose ends. Double check assumptions, focus on stress testing and back testing, while also preparing for regulatory exams.
What makes a model validation effective?
Financial institutions are often hyper-focused on external risks, however internal risks are just as important to catch and mitigate. The use of models invariably produces model risk, which is the potential for adverse consequences from decisions based on incorrect or misused model outputs and reports. While models themselves can be valuable management tools, they can pose risk if their output incorrectly informs important decisions that your financial institution makes which can lead to financial loss and reputation damage.
An independent model validation should be performed for all models developed in-house or by a third party. By completing an evaluation of your model’s conceptual soundness, you will be able to assess the quality of your model’s design and construction. This step in the model validation process should ensure that any decisions made during the process of model design and construction are well informed and consistent. Financial institutions should employ sensitivity analysis in model development and validation to check the impact of small changes in input and parameter values on model outputs to make sure they fall within an expected range. There are four important steps in the evaluation of your model’s conceptual soundness:
- Assessment: Assess the quality of model design and construction
- Comparison: Compare the model to alternative theories and approaches
- Limitations: Inspect for potential limitations
- Relevance: Check the relevance of the data used to build the model
Ongoing monitoring confirms that the model is appropriately implemented, is being used properly and is performing as intended. This step in the validation process is essential because it will allow you to evaluate whether changes in market conditions, clients or activity necessitate adjustment, redevelopment or replacement of the model. It will also allow you to verify that any extension of the model beyond its original scope is valid. With process verification, all components will be tested to ensure they are functioning as designed. Along with process verification, benchmarking can be used to compare a given model’s inputs and outputs to estimates from alternative internal or external data or models.
Outcomes analysis involves the comparison of model outputs to corresponding actual outcomes. Back testing is one form of outcomes analysis. Specifically, it involves the comparison of actual outcomes with model forecasts during a sample time period not used in model development and at an observation frequency that matches the forecast horizon or performance window of the model.
