Auditors and examiners care deeply about this feedback loop, because it ties your lifetime-loss estimates directly to what actually occurred. In addition, the reality check provided by backtesting gives financial institutions the ability to refine CECL methodology and bolster risk management to reduce volatility in reserves.
CECL backtesting: What it is & how to avoid 6 common mistakes
July 15, 2025
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Support accurate and defensible allowance estimates
Backtesting the estimate for credit losses can build confidence in the CECL model and ensure it reflects an institution's credit risk. However, be careful to avoid common backtesting mistakes.
Key topics covered in this post:
What is CECL backtesting?
The current expected credit loss (CECL) model fundamentally changed how financial institutions forecast expected credit losses, so accurate and defensible allowance estimates are critical. Backtesting the CECL model is one of the most practical tools community financial institutions can use to make sure their allowance models are holding up.
CECL backtesting, part of any CECL model validation process, examines what the institution projected for credit losses and shows how that estimate compares to losses actually incurred. By comparing “expected” credit losses from a past reporting period to the charge-offs and recoveries that ultimately occurred, financial institutions can learn exactly where a methodology might have over- or under-estimated risk.
You might also like this resource: “A banker’s guide for CECL compliance and backtesting.”
downloadThe process of backtesting CECL: Common steps
To monitor model accuracy and performance trends, some typical steps in CECL backtesting include:
- Selecting a defined period for comparing outcomes with model forecasts. The period should not be one used to develop the methodology.
- Comparing the actual to predicted losses to assess variances.
- Analyzing variances to determine how much of the difference was due to a change in the historical loss rate rather than other factors, such as changes in loan volume, changes to policies, or changes in the value of underlying collateral.
- Identifying any other factors that could have affected the allowance.
Remember, backtesting is aimed at ensuring the CECL model is still performing as expected and reflects your risk profile. It’s a good idea to periodically review the methodology, assumptions, and inputs, including forecast inputs and sources. Have you added new lines of business, or is a particular segment evolving or growing? Those are among the reasons an institution may need a change in methodology or the addition of a new one.
The allowance is especially material to your balance sheet, so you need to document your controls, monitoring, and testing to ensure confidence in the process and program in place for developing this estimate.
What areas should be backtested?
If backtesting the bank or credit union’s CECL calculation is on your radar, consider prioritizing these areas for comparisons:
- Historical loss projections vs. actual outcomes: Determine the model’s predictive accuracy by regularly comparing past predictions to actual charge-offs and recoveries
- Loan segments and risk profiles: Drill into the portfolio to look for trouble spots. Examine performance across diverse loan categories, borrower profiles, and risk ratings to make sure the model is capturing differences in loss behavior.
- Macroeconomic assumptions: Assess the impact of factors such as unemployment rates, interest-rate fluctuations, and GDP trends on the CECL model’s performance. Were the assumptions accurate and were that gaps?
- Model sensitivity: Test how the model adapts to economic changes and stress scenarios (such as accelerated prepayment speeds) to ensure it remains robust under varying conditions.
Short- vs. long-term backtesting:
- Short-term backtesting typically spans one year. It can quickly identify discrepancies between forecasts and actual losses. It can also allow timely adjustments to model inputs and assumptions based on recent economic conditions and credit trends.
- Long-term backtesting covers multiple years to evaluate model performance over extended periods. This view validates the accuracy of long-term macroeconomic and life-of-loan assumptions, uncovers undue reliance on historical data, and ensures the model is consistent across both growth and downturn cycles
On the surface, backtesting seems simple. But in practice, Abrigo CECL advisors see that comparing historical allowance forecasts against actual losses incurred often trips up institutions. Indeed, more than 1 in 5 attendees of a recent Abrigo webinar named model validation and backtesting as their biggest challenge related to managing the allowance.
CECL backtesting mistakes & how to avoid them
Not backtesting frequently enough
Many institutions treat backtesting as a once-a-year compliance checkbox. But CECL models don’t operate in a vacuum. They’re impacted by changing portfolios, shifting economic conditions, and internal decision-making. Waiting too long between backtests increases the risk of undetected issues, including model drift and outdated assumptions.
TIP: Build a regular backtesting cadence. Quarterly is a great goal. Frequent reviews help spot early warning signs and ensure that assumptions stay aligned with actual performance.
Using inconsistent data sets
This one comes up often. A backtest might look off, but when you dig in, the issue is simply that the model and the actual loss comparison used different data sources or definitions. Inconsistencies in timeframes, segmentation, or inputs can make results unreliable or—even worse—misleading. CECL backtesting provides the ongoing monitoring to catch those issues.
Tip: Align data definitions, timeframes, and segmentation across modeling and backtesting. Small inconsistencies can skew results, so consistency provides a clearer picture. 
Ignoring loan segmentation differences
At the portfolio level, backtesting results might look fine until they’re broken down into individual loan segments. That’s where trouble spots tend to appear. It might be commercial real estate, indirect auto, or another niche that behaves differently than expected.
Tip: Always review model output against actual loss rates by loan segment. Even without the resources to dig into more granular breakdowns like geography or risk grade, segment-level analysis often reveals areas where model assumptions need attention. 
Overlooking the impact of macroeconomic assumptions
When models include economic forecasts or qualitative overlays, those assumptions should be part of the backtesting analysis. Skipping a review of macroeconomic assumptions in your model is a missed opportunity to understand what’s really driving results.
Tip: During backtesting, step back and look at how macro assumptions held up. If the model expected unemployment to rise and it didn’t, or vice versa, what was the impact? These insights often lead to meaningful refinements. 
Failing to document and act on findings
One of the biggest gaps isn’t in the analysis, it’s in what happens after. Some institutions run the numbers, find discrepancies, and then...do nothing. Either the findings aren’t documented properly, or the follow-up just doesn’t happen. Failing to act on insights can undermine a model’s credibility and regulatory standing.
Tip: Create a process for documenting everything, including what was tested, what was found, and any model changes made (or why no changes were made). As CECL governance matures, setting clear thresholds for when a model change is required or when holding steady is reasonable is becoming more important. Examiners want to see thoughtful, consistent decision-making in model documentation. 
Relying on small sample sizes
Many community financial institutions Abrigo’s Advisory team works with have portfolios with very few charge-offs. That’s great from a credit standpoint, but it makes backtesting more difficult. Drawing conclusions from limited data can lead to misleading results.
Tip: When CECL data is sparse, try expanding the historical window or using peer data for additional context. If qualitative factors (qualitative adjustments to the CECL calculation) are needed, make sure the rationale is well-documented and tied to what the data is showing.
Improve CECL model accuracy
Backtesting isn’t about achieving perfection. Results often don’t match forecasts exactly, and that’s perfectly fine. In many cases, institutions land on the conservative side, with allowances exceeding actual losses. That can be entirely appropriate when supported by good documentation and sound reasoning, such as economic uncertainty or limited data.
Ultimately, the value of backtesting lies in the insights it provides for your CECL allowance. It reveals how the model is performing, supports stronger governance, and improves conversations with both internal stakeholders and regulators. Done thoughtfully, it becomes more than just a compliance step. It becomes a tool for building confidence in the CECL model and ensuring the model reflects the true nature of a financial institution’s credit risk.
This blog was written with the assistance of ChatGPT, an AI large language model. It was reviewed and revised by Abrigo's subject-matter expert for accuracy and additional insight.
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