Skip to Main Content


Implementing Q factors under CECL: Why your segmentation and other modeling choices matter


Read this if you are a financial institution.

Have you ever noticed how a countdown seems to want to pick up speed the closer you get to the finish? 10, 9, 8… The chanting gets louder, and excitement builds with each passing number: 7, 6, 5… For me, the adrenaline really kicks in below 5: at 4, 3, 2.

Whether it’s excitement or anxiousness you’re feeling as CECL adoption approaches, staying focused on the remaining decisions is vital to your success. One of those decisions—and a frequently asked question in recent discussions with 2023 adopters—is about implementing Q factors (qualitative adjustments). So let’s take a look at some key considerations for Q factors.

10, 9, 8…

The role and nature of Q factors are to adjust for certain things you know or think will be different about losses in the future than they were in the past. They are also intended to be transitory. Like a scientific hypothesis, each Q factor adjustment exists until it bears out and is picked up in your primary model output, or it doesn’t. Either way, the need for that particular adjustment would presumably end. The one adjustment that most often comes to mind is economic forecasts, a new component of CECL. Let’s assume you have a qualitative adjustment for an expected upcoming recession—depending on your chosen method, for instance, the difference in reserve calculated when using a more severe economic scenario, or using historical losses only from a time period that was recessionary. Regardless, one would not expect this to be a permanently held adjustment—at some point, the recession happens, or it does not, and the adjustment may no longer be warranted. Categories of Q factors give us a place to start thinking through what specifically is different or what is changing, whether it is something broad, like economic forecasts, or something specific like new products or geographies in which you lend money. The key here is that it is something not accounted for already in the design, structure, inputs, or output of your core model.1  

7, 6, 5…

Effectively implementing Q factors requires you understand how your segmentation (pools for collective analysis) and method selection affect your opportunity to make and support adjustments. What does this mean? 

Segmentation under CECL requires grouping loans based on similar risk characteristics. This is inherently about behavior that has to do with risk and loss. Grouping all first lien residential mortgage loans together is a common one. If you have a big enough portfolio with enough historical loss history to support it, you may be able to further sub-segment that pool by geography (region, state, county), or by nature of use (primary residence, vacation home, or investment property), or by collateral lien position (first lien, junior lien). You’d want to do this if the inherent risk of loss you face is different. This loss risk difference could be a matter of customer behavior, or a difference in market conditions.

But what if, through the model development stage, you realize (or are told) that you don’t have enough data or big enough pools to model effectively at your preferred level of segmentation? Or, what if the method you chose comes with a level of segmentation already built-in?

4, 3, 2…

Let’s stick with residential mortgages. If you’ve accepted a higher level of segmentation, for example at the product level (residential mortgages) or even higher at the call report code level (1c), you may be thinking you have lots of leeway to make qualitative adjustments for all the nuances underneath. Not so fast. Are there any other modelling choices you’ve made that may prevent you from doing that? What about the use external loan data?

A common modeling technique is to use peer data to help establish the mathematical relationship between historical losses and changes in the economy. Most peer data is only available at the call report code level, which means that all those nuances in your “1c segment” that you want to further adjust may already be embedded in your methodology because of the connection to peer data. This may be even further complicated by situations in which you have no say in more proprietary loan data sets model developers may be using to support their model design, assumptions, and formulas. External loan pool data is just one of many things to consider when evaluating when, and if there are opportunities to support a Q factor adjustment.

So what’s a responsible CECL adopter to do when considering how best to make qualitative adjustments? 

First, understand that the intention is to account for changes and differences, which inevitably is a moving target. Adjustments and their related reserve dollars are not fixed, which is why they should be subject to re-assessment each time you update your CECL calculation. Second, if you’re using a vendor’s model, it is imperative that you know how, when, and to what extent loan data other than yours is or has been used, both in developing the model and in its on-going operation. Third, you need to understand, document, and think through the interplay between the segmentation you have and model design or other assumptions made along the way. Finally, if in the normal course of operation, you change any of those assumptions or inputs, it should trigger a re-evaluation of your Q factors to ensure they remain relevant. 

Want help with your Q Factor approach? Are you struggling with CECL documentation or other elements of CECL? 

No matter what stage of CECL readiness you are in, we can help you navigate the requirements as efficiently and effectively as possible. For more information, visit the CECL consulting page on our website. If you would like specific answers to questions about your CECL implementation, please visit our Ask the Advisor page to submit your questions.

For more tips on documenting your CECL adoption, stay tuned for our next article in the series. You can also follow Susan Weber on LinkedIn.

1If you haven’t already, I highly recommend that you read these sections of the accounting standards codification (ASC) 326, commonly referred to as the CECL standard: 326-20-55-4, 326-20-30-7, 326-20-30-8, and 326-20-30-9.

Related Industries

Related Services


Business Advisory

Related Professionals


BerryDunn experts and consultants

Read this if you are at a financial institution. For more CECL information, tune in to the latest episode of BerryDunn’s CECL Radio podcast. It features Susan Weber and David Stone discussing how to handle unfunded commitments and debt securities during CECL preparation.

I love a big surprise! Of course, I mean the fun, uplifting kind—like birthday parties, a best friend’s unexpected visit, or that special anniversary gift. Not that other kind of surprise that’s more like biting into an apple only to find half a worm. Calculating a loss reserve for unfunded commitments is not a new concept, but the reach and significance of it may end up surprising institutions. How much? A review of 2020 public filings and disclosures shows that some adopters saw unfunded commitment reserves increase millions of dollars, from one percent of total reserves pre-adoption to six percent or more post-adoption. In this article, we take a close look at unfunded commitments under CECL, in an effort to help you avoid that “other kind” of surprise.  

Within the CECL standard (Accounting Standards Codification (ASC) 326 – Financial Instruments-Credit Losses), key considerations for estimating reserves tied to unfunded commitments are covered in section 326-20-30-11. The section lays out three key fundamentals: it applies to credit commitments that are not unconditionally cancellable, and that institutions should consider how likely the commitment is to be funded, and its expected life. 

First, let’s look at unconditionally cancellable—this essentially means that unless an institution can, at any time and for any reason, cancel its commitment to lend, then the commitment has to be included in this part of the estimate. Institutions may be surprised to discover that a portion of its commercial sales pipeline should now be included in unfunded commitment balances. Why? Because commitment letters issued to business loan applicants are often considered legally binding and they typically do not contain language that would make them unconditionally cancellable by the institution. This makes sense when you realize the primary goal of the commitment letter is to assure the applicant that the bank is committed to making the loan. This discovery, in turn, has led those involved in CECL implementation to develop (1) processes to ensure commercial loan pipelines are sufficiently detailed enough to know what, when, and how these commitments should be included in the calculation, and (2) internal controls that assure the accuracy, completeness, and timeliness of the information. 

Next up—how likely is it that the commitment will be funded? For unused portions of existing loans and lines, this may mean taking a look at average utilization rates. For in-scope pipeline commitments, institutions may find that they need to dig through information that is not commonly held in a central system to come up with a success or close rate. The likelihood of funding may vary widely between products or segments, and over its expected life. For example, the expected funding of a residential or commercial real estate construction line may approach 100%, whereas only 40% or less of a revolving line may ever be used. These funding rates become the basis for “discounting” the unfunded balances subject to reserve estimation and should be re-evaluated on some periodic basis, which can be detailed in the institution’s CECL model documentation related to governance and monitoring.

Finally, let’s look at the expected life of the loan component. This language and expectation are consistent with on-balance sheet credit, leading institutions to (1) make sure they are able to segment their off-balance sheet commitments in the same pools used for boarded loans, and (2) apply the appropriate pool reserve factor to unfunded commitments over the expected life of that type of loan. One-way institutions may accomplish this is by making sure that they are using the same fully adjusted reserve factor and expected life assumptions for unfunded pools as they do for their funded pool counterparts. 

You may discover that your CECL model or software vendor does not provide for unfunded commitment calculations, or only provides support for the available credit portion of loan facilities boarded to your core loan system. In either case, this means institutions must consider, support, and complete calculations outside of the model. Writing clear step-by-step instructions and ensuring a robust independent review/approval process will help off-set risks posed by such manual calculations.

Could you use an experienced resource to help you document or validate your CECL model?  

No matter what stage of CECL readiness you are in, we can help you navigate the requirements as efficiently and effectively as possible. For more information, visit the CECL page on our website. If you would like specific answers to questions about your CECL implementation, please visit our Ask the Advisor page to submit your questions.

For more tips on documenting your CECL adoption, stay tuned for our next article in the series. You can also follow Susan Weber on LinkedIn.

Unfunded commitments and CECL: You may be in for a big surprise

Read this if you are a financial institution.

One of the new components to CECL is the consideration of how future economic conditions may impact your estimate of expected losses. What forecasts do you need, how to find and evaluate forecasts, and what are the things you need to think about regarding the forecast choices you make are all excellent questions. Below are 10 things that may help you make, evaluate, and support your “reasonable and supportable” forecast decision:   

  1. Source(s): Even though many understand that economic forecasts, like their weather counterparts, are often wrong, reputation and experience of the forecaster(s) matter. Beyond name recognition, learning more about the people and process of how the forecast is derived will go a long way to helping you defend the source you’ve chosen.  
  2. Types: Forecasts may be produced by an individual, model(s), or group. You may have heard the term “consensus” to describe a forecast—literally, a forecast that results from the majority or average opinion of a group of contributors. An awareness of the type of forecast will help you identify the unique risks of each.    
  3. Assumptions: With any forecast, it is important to understand the specific assumptions on which the forecast is based. “This forecast assumes…,” or “The accuracy of this forecast is contingent upon….,” or “In developing this forecast we relied on….” are just a few examples of what to look for when reviewing introductions, footnotes, or disclaimers to the forecast(s).  
  4. Cost: Fee-based economic forecasts are not inherently more “reasonable and supportable” than free ones; remember, no forecast is perfect. Some fee-based providers may offer a version of their forecast for free. In these cases, having a clear understanding of what’s included in each version may be a deciding factor.
  5. Completeness: The more economic forecasts you read, the more you realize that what they are actually forecasting varies. Evaluating whether or not a forecast is complete is really about discovering if it forecasts all the economic conditions—or economic factors1—that you want to use in your model. For example, you may have mathematically shown that a local or regional unemployment rate is more informative about loss risk in your portfolio, but is there a forecast source for that local or regional rate?
  6. Frequency: Knowing how often a forecast is updated, and when it will be made available is crucial to the timing of your CECL reserve calculation, and the answers may affect more than just your decision of which forecast to use (see “Trade-offs”). For now, consider how reliably and consistently the forecast is available, and if you can count on it with enough time each quarter-end to use it in your reserve calculation.
  7. Future: With all the uncertainty of the past couple of years, it may seem optimistic to think about sources providing more than a one-year forecast. Yet, for some, this may be an important option. For this reason, finding out if a source does, can, or charges extra for providing one-, two-, and three-year (or longer) forecasts may be necessary.  
  8. Bias: It is important to be aware that forecasts can be biased, meaning that they always tend to be more or less enthusiastic about the future. Bias is not the same thing as providing alternative outlooks or scenarios—such as best case/worst case, or neutral/severe, which are more about the assumptions being made. Bias is often detected when over a period of time you compare sources against each other and discover that one source tends to forecast consistently higher or lower than others. This may be especially important when you consider how a forecast does or does not align with the views of the institution’s management.  
  9. Accuracy: Again, since all forecasts will be wrong to some degree, let’s set a new standard for thinking about accuracy. Perhaps the goal here is to find the less-wrong forecast. One way you might want to do this is to locate (or ask for) previous forecasts over different periods of time and compare them to what actually happened. This one is tricky because of assumptions and bias, but if you’ve narrowed it down to a few choices, you may find this kind of exercise helps you decide.
  10. Trade-offs: Given all the forecasting options and considerations, it may be re-assuring to hear that there is no one right answer. But in order to properly support your choices, it is important to think through and document what trade-offs have been made along the way, how they may affect your reserve calculation, and whether or not you feel it should be addressed elsewhere in your methodology.

In closing, two words of caution. First, while having a good single source for a forecast may sound like a great idea, have a back-up plan. Some forecast sources did not produce an update in March-April 2020, the time when first-wave adopters were trying to produce their first quarter CECL estimates, sending some folks scrambling. With this in mind, making use of several forecasts may help you mitigate single-source risk in the long-run. Second, having a process in place to ensure the same economic outlook is being used in all major functions is important. For example, relying on a “severe” future outlook to build loss reserves while at the same time using a “neutral” economic outlook for budgeting and ALM purposes could call into question the reasonableness of your CECL estimate. 

No matter what stage of CECL readiness you are in, we can help you navigate the requirements as efficiently and effectively as possible. For more information, visit the CECL consulting page on our website. If you would like specific answers to questions about your CECL implementation, please visit our Ask the Advisor page to submit your questions.

For more tips on managing your CECL adoption, stay tuned for our next article in the series. You can also follow Susan Weber on LinkedIn.

1In this context, economic factors” are measured conditions – such as unemployment, gross national product, consumer price index, etc. 

Reasonable and supportable forecasts, oh my! 

What the C-Suite should know about CECL and change management

Read this if you are at a financial institution. 

Some institutions are managing CECL implementation as a significant enterprise project, while others have assigned it to just one or two people. While these approaches may yield technical compliance, leadership may find they fail to realize any strategic benefits. In this article, Dan Vogt, Principal in BerryDunn’s Management and IT Consulting Practice, and Susan Weber, Senior Manager and CECL expert in BerryDunn’s Financial Services Practice, outline key actions leaders can take now to ensure CECL adoption success.  

Call it empathy, or just the need to take a break from the tactical and check in on the human experience, but on a recent call, I paused the typical readiness questions to ask, “How’s the mood around CECL adoption – what’s it been like getting others in the organization involved?” The three-word reply was simple, but powerful: “Kicking and screaming.”  

Earlier this year, by a vote of 5-2, the FASB (Financial Accounting Standards Board) closed the door to any further delays to CECL adoption, citing an overarching need to unify the industry under one standard. FASB’s decision also mercifully ended the on-again off-again cycle that has characterized CECL preparation efforts since early 2020. One might think the decision would have resulted in relief. But with so much change in the world over the past few years, is it any wonder institutions are instead feeling change-saturated?  

Organizational change

CECL has been heralded as the most significant change to bank accounting ever, replacing 40+ years of accounting and regulatory oversight practices. But the new standard does much more than that. Implementing CECL has an effect on everything from executive and board strategic discussions to interdepartmental workflows, systems, and controls. The introduction of new methods, data elements, and financial assets has helped usher in new software, processes, and responsibilities that directly affect the work of many people in the organization. CECL isn’t just accounting—it’s organizational change. 

Change management

Change management best practices often focus on leading from optimism—typically leadership and an executive sponsor talk about opportunities and the business reasons for change. Some examples of what this might sound like as it relates to CECL might include, by converting to lifetime loss expectations, the institution will be better prepared to weather economic downturns; or, by evolving data and modeling precision, an institution’s understanding and measure of credit risk is enhanced, resulting in more strategic growth, pricing, and risk management. 

But leading from optimism is sometimes hard to do because it isn’t always motivating—especially when the change is mandated rather than chosen.  

Perhaps a more judiciously used tactic is to focus on the risk, or potential penalty, of not changing. In the case of CECL, examples might include, your external auditor not being able to sign-off on your financials (or significant delays in doing so), regulatory criticism, inefficient/ineffective processes, control issues, tired and frustrated staff. These examples expose the institution to all kinds of key risks: compliance, operational, strategic, and reputational, among them.

CECL success and change management

With so much riding on CECL implementation and adoption going well, some organizations may be at heightened risk simply because the effort is being compartmentalized—isolated within a department, or assigned to only one or two people. How effectively leadership connects CECL implementation with tenets of change management, how quickly they understand, then together embrace, promote, and facilitate the related changes affecting people and their work, may prove to be the key factor in achieving success beyond compliance.  

One important step leaders can take is to perform an impact assessment to understand who in the organization is being affected by the transition to CECL, and how. An example of this is below. Identifying the departments and functions that will need to be changed or updated with CECL adoption might expose critical overlaps and reveal important new or enhanced collaborations. Adding in the number of people represented by each group gives leaders insight into the extent of the impact across the institution. By better understanding how these different groups are affected, leaders can work together to more effectively prioritize, identify and remove roadblocks, and support peoples’ efforts longer term.           

No matter where your institution is currently in its CECL implementation journey, it is not too late to course-correct. Leadership—unified in priority, message, and understanding—can achieve the type of success that produces efficient sustainable practices, and increases employee resilience and engagement.

For more information, visit the CECL page on our website. If you would like specific answers to questions about your CECL implementation, please visit our Ask the Advisor page to submit your questions. For more tips on documenting your CECL adoption, stay tuned for our next article in the series, revisit past articles, or tune in to our CECL Radio podcast. You can also follow Susan Weber on LinkedIn.

Implementing CECL: Kicking and screaming

Read this if you are a financial institution.

Choosing a method for estimating lifetime expected losses is a commitment. A commitment that signals, in spite of any other option, you’re certain this method is the right one for you—your segment, portfolio, and institution. While you might be able to support a change in method later, it is much more likely you’ll be living with this decision a good long while. So, how exactly does one know which method is the right one? Let’s take a few minutes to answer some frequently asked questions about selecting methods for CECL.

How many CECL methods are there?

This depends on who you ask. Section 326-20-30-3 of the standard names five (5) categories: discounted cash flow, loss-rate, roll-rate, probability of default, and aging schedule. Some categories, like loss-rate, have several methods. Additionally, some methods seem to be referred to by different names, giving people the impression that there are exponentially more options out there than there really are. With this in mind, I tend to think of two (2) broad categories, and seven (7) unique methods:  

  • Loss-rate methods
    • Snapshot (open pool, static pool, cumulative loss rate)
    • Remaining Life and Weighted Average Remaining Maturity (WARM)
    • Vintage
  • Other methods
    • Scaled CECL Allowance for Losses Estimator (SCALE) (option for banks with assets <$1 billion)
    • Discounted Cash Flow (DCF)
    • Probability of default 
    • Migration (roll rate, aging schedule)  

What’s the difference?

The loss-rate methods use actual historical net charge-off information in different ways to derive a loss rate that can then be used to calculate expected losses over the remaining life of a pool. In general, they do this by holding the mix of a group of loans constant (e.g., by year of origination) and then tracking net losses tied to that grouping over time. The “other” methods employ a variety of mathematical techniques and/or credit quality information to estimate expected lifetime losses. For a quick overview of each method and corresponding resources, access our CECL methodologies guide here.

How do I know which to use?

This is the CECL equivalent of the proverbial million-dollar question. Technically, any institution could use any one, or all of these methods. But there are considerations that make some of them a more or less likely fit. For example, if your institution has >$1 billion in assets, SCALE is not even an option for you, and you can cross it off the list. If you are not in a position to afford software, or lack the internal expertise to build a similar model internally, then discounted cash flow and probability of default methods would likely be extremely burdensome in the normal course of business. For that reason, you may need to cross those off your list. If you lack large pools with consistently diverse performance over time, then migration methods will be difficult to support. If you have a relatively stable loan mix, consistent credit culture, and a lot of reliable historical loss data—especially through multiple economic cycles—the loss-rate methods may be a good fit, with or without software. If your portfolio has undergone a lot of changes—products, underwriting standards, merger and acquisition activity—and/or there are significant gaps in key data that cannot be restored, then you might want to re-consider software and one of the “other” methods. 

What are the pros and cons of the various methods?

One pro of the loss-rate and SCALE methods is they have been shown to be manageable without software. Examples of all of these methods have been illustrated using Excel spreadsheets. The use of Excel is also potentially a con, given that more spreadsheets and, maybe more people, are likely going to be involved in computing the Allowance for Credit Losses (ACL). As a result, version control as well as validation of spreadsheet macros, inputs, formulas, math, and risk of accidentally overwriting or deleting values should be addressed. One pro of the discounted cash flow method is that it is a bottom-up approach, meaning each loan’s discounted cash flow (DCF) is computed and then rolled up to the segment level. Because of this, DCF can more easily handle mixed pools, e.g., loans of all vintages, sizes, terms, payment and amortization schedules, etc. A potential con of DCF is that it really requires software, staff trained to use the software appropriately, and an understanding of the vast array of choices, levers, and decisions that come with it.     

Does my choice of method affect my qualitative adjustment options?

How’s this for commitment: maybe. In general, I think it’s safe to say that CECL requires additional thought be given to the nature and degree of adjustments. This is especially true when you look at the combination of potential segmentation changes, new elements of the calculation, and the variety of methods now available. Consider the example of a bank using a loss-rate method and facing a potential economic downturn. If that bank has sufficient history and a relatively stable portfolio mix, credit culture, and geography, then it might elect to use a different time period—say, historical loss-rates observed from the last recession—rather than those more recently computed. In this case, the loss-rate method would already be using a recessionary experience. 

How then, would the bank approach additional qualitative adjustments for changing economic outlooks to ensure it is not layering (or double counting) reserve? Going back to the original “maybe” response, perhaps the answer is less about inherent conflicts between methods and qualitative adjustments. Rather, it’s about understanding that given your chosen method, you may be faced with even more decisions about if, where, and how much adjusting you are doing.

CECL adoption is required. Struggling to adopt isn’t. We can help.

No matter what stage of CECL readiness you are in, we can help you navigate the requirements as efficiently and effectively as possible. For more information, visit the CECL page on our website. If you would like specific answers to questions about your CECL implementation, please visit our Ask the Advisor page to submit your questions.

For more tips on documenting your CECL adoption, stay tuned for our next article in the series. You can also follow Susan Weber on LinkedIn.

Questions to ask when deciding your CECL Method

Read this if you are at a financial institution.

While documentation of your CECL implementation and ongoing practices is essential to a successful outcome, it can sometimes feel like a very tall order when you are building a new methodology from the ground up. It may help to think of your CECL documentation as your methodology blueprint. While others will want to see it, you really need it to ensure that what you are building is well-designed, structurally sound, appropriately supported, and will hold up to subsequent “renovations” (model changes or tweaks). To help you focus on what’s essential, consider these documentation tips:

Getting started

Like any good architect, you need to understand the expectations for your design—what auditors and regulators want to see in your documentation. Two resources that can really help are the AICPA Practice Aid: Allowance for credit losses-audit considerations1, and the Interagency Supervisory Guidance on Model Risk Management2. One way to actively use these guides is to take note of the various section/subject headers and the key points, ideas, and questions highlighted within each, and turn that into your documentation checklist. You’ll also want to think strategically about where to keep the working document, who needs access to it, and how to maintain version control. It is also a good idea to decide up-front how you will reference, catalog, and store the materials (e.g., data files, test results, analyses, committee minutes, presentations, approvals, etc.) that helped you make and capture final decisions. You can download our CECL Documentation checklist now.   

What to watch out for

What’s new under CECL are areas requiring documentation (e.g., broader scope of “financial assets,” prepayments, forecasts, reversion, etc.). But watch out for elements that seem familiar—they may now have a new twist (e.g., segmentation, external data, Q factors, etc.). It’s a good idea to challenge any documentation from the past that you feel could be re-purposed or “rolled into” your CECL documentation. Be prepared also to spend time explaining or customizing vendor-provided documents (e.g., model design and development, data analysis memos, software procedures, etc.). 

While this material can give you a running start, they will not on their own satisfy auditor and regulator expectations. Ultimately, your documentation will need to reflect your own understanding and conclusions: how you considered, challenged, and got comfortable with the vendor’s work; what validations and testing you did over that work, and how you’ve translated this into policies and procedures appropriate for your institution’s operations, workflows, governance, and controls. For more information on making the vendor decision, and for suggestions of vendor selection criteria, read our previous article “CECL Readiness: Vendor or no vendor?” 

Point of view

It is human nature, especially whenever entering new territory, to want to know how others are approaching the task at hand. Related to CECL, networking, joining peer discussion groups, researching what and how those who have already adopted CECL are disclosing, are all great ways to see possibilities, learn, and gain perspective. When it comes to CECL documentation, however, the most important point of view to communicate is that of your institution’s management. Consider the difference in these two documentation approaches: (a) we looked at what others are doing, this is what most of them seem to be doing, so we are too; or (b) this is what we did and why we feel this decision is the best for our portfolio/risk profile; as part of our decision-making process, we did this type of benchmarking and discovered this. Example b is stronger documentation: your point of view is the primary focus, making it clear you reached your own conclusions. 

Other elements for CECL documentation

Documenting your CECL implementation, methodology, and model details is critical, but not the only documentation expected as you transition to CECL. It has been said that CECL is a much more enterprise-wide methodology, meaning that some of the model decisions or inputs may require you use data and assumptions traditionally controlled in other departments and for other purposes. One common example of this is prepayments. Up to this point, prepayment data may have been something between management and a vendor and used for management discussion and planning, but not necessarily validated, tested, or controlled for in the same way as your loss model calculations. Under CECL, this changes specifically because it is now an input into the loss estimate that lands in your financial statements. As a result, prepayments would be subject to, for example, “accuracy and completeness” considerations, among others (for more information on these expectations, refer to our earlier articles on data and segmentation). Prepayments is just one example, but does illustrate how CECL adoption will likely trigger updates to policies, procedures, governance, and controls across multiple areas of the organization.    

One final note: There are some new financial statement disclosures required with CECL adoption. Beyond those, there may be other CECL-related information either you want to share, or your audit/tax firm recommends be disclosed. Consulting with your auditor at least a quarter prior to adoption will help make sure you aren’t scrambling last minute to draft new language or tables.  

Struggling with CECL documentation or other elements of CECL? 

No matter what stage of CECL readiness you are in, we can help you navigate the requirements as efficiently and effectively as possible. For more information, visit the CECL page on our website. If you would like specific answers to questions about your CECL implementation, please visit our Ask the Advisor page to submit your questions.

For more tips on documenting your CECL adoption, stay tuned for our next article in the series. You can also follow Susan Weber on LinkedIn.

1You can find the AICPA Practice Aid here.
2The interagency guidance was released as OCC Bulletin 2011-12, FRB SR 11-7, and as FDIC FIL 22-2017


CECL documentation: Your methodology blueprint

Read this if you are at a financial institution.

This article is part of our series on CECL implementation. You can read previous articles in the CECL series here

Segments, sub-segments, pools, cohorts—by whatever name you call it, grouping loans (and other financial instruments) for CECL1 is kind of a big deal. Like choosing an inner circle of friends, creating effective loan pools can have a lot of influence over your CECL experience, from methodology decisions to your allowance estimates. As a CECL adopter, you are expected to evaluate, support, and document segmentation choices (no such requirement for your inner circle of friends!), even if you plan to use the same segmentation in place today. To do so successfully, consider these segmentation ABCD’s:

A: Accuracy and completeness of data

The accuracy and completeness of data used to determine the most appropriate segmentation under CECL covers a lot of ground – everything from what information you considered to be relevant and why, to where the data came from and how it was determined to be valid (aka accurate and complete). CECL requires loans sharing similar risk characteristics2 to be pooled together for “collective evaluation”; examples include loans with similar terms and structures, lien position on collateral (e.g. first, or junior lien), or collateral use (e.g. owner-occupied or investment real estate). As a result, “accuracy and completeness” applies not only to the data you rely on to pool loans, but also to what you determined the common risk characteristics to be, why those, what others you identified but ultimately didn’t use, and why. Read our earlier article, CECL Adoption: The five W's of data, for more information on data considerations.

B: Balance between granularity and significance

Striking a balance between how many segments you create and the significance of doing so can be a little like trying to achieve the “just right” goal of Goldilocks. For example, is pooling all your consumer loans together most aligned with your past loss experience, or does the type of collateral also influence your risk of loss? How far is too far (real estate, cars, boats, RV’s, tractors)? At what point does it become difficult to consistently demonstrate or predict meaningful differences in risk of loss for each? Several sections of the standard address this need to balance detail with what is useful3. In this way pools should be small enough that the risk characteristics they share are relevant to estimating inherent risk, but not so small as to be confusing, misleading, or not able to be modeled consistently over time. Being aware of how small a pool is in terms of the number of loans it consistently contains may be one consideration for whether or not the segmentation is too granular. 

C: Controls over the selection of risk characteristics

Your segmentation choices will likely have far-reaching effects on other key decisions in your CECL methodology. Model selection, qualitative adjustments, and even if/what/how external or peer data may apply are examples of what could be impacted by your segmentation selection.  As a result, and in addition to the above, your auditors and regulators will want to see evidence the risk characteristics driving your segmentation choices were robustly reviewed, challenged, tested, and documented. Further, they will want to see that you have a similar systematic approach in place, and ongoing, to identify when a loan no longer shares the defined risk characteristics of its segment, resulting in its removal from the pool to be assessed individually.4  

D: Documentation tips

Documentation is like exercise—you know you should do it, but sometimes you don’t make it a priority. CECL opens the door for all kinds of documentation expectations, so coming up with a way to do this as you work through implementation can save you a lot of headache later. For segmentation, setting up a simple spreadsheet with the ABC’s to the left and columns to the right to list data, testing, key considerations, decisions, approvers, and even links to supporting evidence (data files, governance memos, etc.) is but one example of how you might keep track of these items as you work. Be sure to include any assumptions you had to make along the way (e.g. how you handled missing information on old or purchased loans), or aggregations (larger-level pools than you might have preferred) you accepted and why.

Finally, while you may be checking out what segmentation others in the industry are using—which will vary as it does today—what you’ll want to document most is why the choices you made are right for your institution.

For more tips on documenting your CECL adoption, stay tuned for our next article in the series on documentation. You can also follow Susan Weber on LinkedIn.

No matter what stage of CECL readiness you are in, our Financial Institutions team is here to help you navigate the requirements as efficiently and effectively as possible. If you would like specific answers to questions about your CECL implementation, please visit our Ask the Advisor page to submit your questions.

1Current Expected Credit Loss (CECL) methodology as provided for in the Financial Accounting Standards Board (FASB) Accounting Standards Update (ASU) Financial Instruments-Credit Losses Topic 326, commonly referred to as FASB ASU 326. A copy of the standard is available for download from the FASB website.
2Refer to FASB ASU 326-20-55-5
3Examples include FASB ASU 326-20-50-3, 326-20-55-10, and 326-20-55-11 (for financing receivables)
4Refer to FASB ASU 326-20-30-2

CECL implementation: Segmentation ABCD's

Read this if you are at a financial institution.

Data. Statistics. Analysis. Modeling. Whatever happened to good old-fashioned experience? When it comes to CECL (current expected credit loss), it’s going to take a combination of both—data and experience—to help you design an effective methodology. While “experience” sounds like a fun day of reminiscing, getting the “data” side can feel like a never-ending journey in the fog. If, in your CECL readiness journey data has you feeling a little lost, let the five W’s of data be your guide.

What data: Knowing what data you need is like having a map for the journey ahead. Understanding how CECL is different—in scope, requirements, definitions, and techniques—is the first step. One approach is to make a list of the differences. To the right of the list (or insert a column to your spreadsheet), add what data you will need to fulfill this part of the standard. For example, a new requirement for CECL is making adjustments for prepayments (new on-going data need). Some methods may use a prepayment rate which is, itself, a calculation requiring data and assumptions. It’s okay if this initial list is incomplete as capturing what’s new and different is an important start, and one that you can build upon as you go.

Where data: Next, you need to know where to get the data you need (sometimes it’s about even knowing if the data exists). This is where having key people from departments across the organization on the team can be very helpful. Time to add a new column to that spreadsheet: identify the system, department, person, and/or vendor responsible for the data you need. Find out where and how the data is stored. For example, is the data in a core system as a data point already, or is it manually calculated and recorded in a monthly memo going back 15 years? Where the data is located may be a key factor in decisions you will make about allocating resources, changing processes, and ensuring good controls over data.    

When data: The next essential question is, 'when is the data going to be needed and used?' In this regard, data falls into two broad categories: periodic and always. Examples of periodic data include data used to make model selection(s), support segmentation, or data that help you choose among several available techniques. Periodic data may be in the form of analysis and testing. Always data is the kind needed on an on-going basis, most typically to directly support the allowance calculation. Examples of always data are prepayments (from our earlier example), and the codes in the core system that allow you to group loans into their proper segments. Knowing when data will be needed and used will help keep your implementation moving forward.  

Why data: Data is often imperfect; it can be historically incomplete, somewhat dated, messy to compile, and may even be biased. Recognizing this upfront and throughout the process will not only reduce your stress; it will also help you document why certain data may have needed to be adjusted, modified, transformed, or ignored. Being equally inquisitive about why the data is in the shape it’s in may lead you to discover important ways to improve its quality, integrity, and the efficiency and effectiveness of collection and validation processes. Ensuring management has a good understanding of why assumptions or adjustments to data had to be made will help them fulfill their oversight responsibilities, pose credible challenge questions, and build context for understanding results.    

Whose data: Finally, whose data are you using? There are options for considering and using external data in your CECL methodology. When thinking through what this ultimately means to you in this process, it’s helpful to define external data as data provided by outside parties. In our journey analogy, think of these as alternate routes and, like alternate routes, there may be trade-offs to taking them that you’ll need to assess. Probably two of the most recognizable external data examples are economic forecasts and peer data. They may also include any data of yours that a vendor may have transformed and returned to you—a common example of this being prepayments. It is important to understand that from an auditor and regulator perspective, you remain responsible for the integrity and use of this data in your methodology. 

Once you have the five W’s of data locked in, you are well on your way to CECL implementation. No matter what stage of CECL readiness you are in, our Financial Institutions team is here to help you navigate the requirements as efficiently and effectively as possible. If you would like specific answers to questions about your CECL implementation, please visit our Ask the Advisor page to submit your questions.

CECL adoption: The five W's of data

Read this if you are at a financial institution.

For some financial institutions, it’s only 11 months to CECL adoption and they haven’t yet decided on whether or not to use vendor model software for their CECL calculation. If this sounds familiar, then take a few minutes to consider these five key factors to making this decision so you gain traction on your CECL readiness efforts:

  1. Criteria: Identify and list the specific criteria you and others will need to make the vendor/no vendor decision. Ask key decision-makers to weigh-in. Getting this consensus on the criteria up-front ensures you present enough of the right kind of information to make the final decision quickly.     
  2. Time: Create a timeline of what has to happen between now and the CECL adoption date for your organization to be ready and confident in your chosen method(s). Identify the staff and hours required to accomplish it. Clarifying both the time commitment and time constraints will help you assess if additional support or trade-offs are required. Get tips on creating or revising your CECL implementation timeline here. 
  3. Expertise: Define the level of expertise necessary to understand, develop, test, and document the new model(s). This work may involve model design, data flow, mathematical formulas, and the ability to document assumptions and limitations of each. If you’re not sure, ask others to help assess if the organization has the internal expertise necessary to do this work, understanding those resources may work in different departments. 
  4. Technology: Determine if you have access to, or enough of, the technology needed for CECL model development and testing. In this context, technology may include systems, programs, analytical software, processing speed, and secure access. Knowing what your current technology capabilities are helps you identify any limitations you may need to address in advance.     
  5. Risk: Understand the risks. Take time to think through the risks posed by using – and not using – vendor model software. For example, developing a model in-house may save you the cost of vendor software, but what risk does developing a model in-house pose to the organization in allocating the people resources to do so? Likewise, securing vendor software may reduce the strain on limited internal resources, but what risks to access, process, communication, and control are posed by having to manage the vendor? 

Questions about your CECL implementation?

No matter what stage of CECL readiness you are in, our Financial Institutions team is here to help you navigate the requirements as efficiently and effectively as possible. If you would like specific answers to questions about your CECL implementation, please visit our Ask the Advisor page to submit your questions. 

CECL readiness: Vendor or no vendor?