Checking in on CECL: What Matters in 2025

As we move through 2025, it’s a good time to revisit your current expected credit loss (CECL) model and processes to ensure compliance with industry best practices and to identify opportunities for enhancement and optimization. Auditors and validators continue to examine your organization’s CECL model and documentation, so it is important to stay ahead of leading practices. Reviewing recent hot topics in this area ensures that you get the most up-to-date insights and avoid regulatory headaches. All of that is beneficial to your organization, whether you’re preparing for an audit, a regulatory exam, model validation or simply assessing your CECL model.  

Leading Practices for CECL Model Governance
Common gaps identified include a lack of detailed process documentation, missing loan reconciliation at the segment level and insufficient internal controls, especially in vendor-based models.

• Clear oversight structure. Define roles and responsibilities related to model design, monitoring, validation and board reporting.
• Model development documentation. Detail methodology selection and segmentation logic.
• Data governance. Ensure data quality controls, assumptions and input reviews are documented.
• Validation and monitoring. Assess conceptual soundness and ensure models perform as intended on an ongoing basis.
• Stress testing and back testing frameworks. Include testing model volatility and accuracy procedures.

Refine Your Qualitative Factor Framework
This framework helps institutions streamline qualitative calculations and reduce bias in reserve estimates.

• Establish a ceiling for reserve adjustments based on historical or modeled data.
• Quantify risk levels (minor, moderate and major) using historical benchmarks.
• Anchor adjustments to max loss assumptions and apply weights based on factor relevance.
• Maintain clear records of assumptions, calculations and rationale and be able to support max loss assumptions, factor scales and factor weights.

Managing CECL Volatility
Volatility in CECL models can stem from a range of issues, including: 

• Assumptions and inputs, prepayment rates, average life and forecast methodologies.
• Q-factors or inconsistent frameworks that lead to unpredictable reserve swings. Also, tight factor scales may result in large swings over time.
• Model changes, like switching vendors or methodologies, introduce variability.

The first step in managing this volatility is to analyze historical data and understand trends. For example, institutions using long lookbacks that include Covid-19 era prepayments may see declining prepayment rates as those periods roll off, and it is important to know how that may impact CECL calculations.

Back Testing and Stress Testing
Back testing is now a best practice which includes validating model accuracy by comparing forecasts to actual outcomes or comparing the reserve to historical experience. Examples include:

• Coverage tests, which compare reserves to historical loss rates.
• Forecast accuracy or comparing predicted and actual losses.
• Assumption accuracy or testing prepayment rates and other input assumptions against historical experience.

Stress testing and scenario analysis complement this by modeling stressed scenarios to assess model sensitivity. Both techniques support regulatory expectations and internal governance.

Beyond stress testing, institutions can compare reserves and charge-off rates to industry peers as well as identify anomalies in reserve levels relative to performance metrics. These tools help institutions calibrate their models and justify assumptions during audits or validations.

As CECL continues to evolve in 2025, institutions must continue to stay ahead of expectations by reviewing their CECL procedures and documentation practices to identify areas to strengthen their models. In addition to CECL models, institutions can stop relying on a rotation of third-party validation vendors and consolidate model validation and risk advisory under a unified and strategic approach.