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Getting ready for leap into CECL

Coming accounting rules on loan loss allowance demand critical mindset adjustments

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  • Written by  Dalton Sirmans, Main Street Technologies
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  • Comments:   DISQUS_COMMENTS
The upcoming Current Expected Credit Loss standard for calculating reserves poses organizational and "philosophical" challenges amid the accounting issues. The upcoming Current Expected Credit Loss standard for calculating reserves poses organizational and "philosophical" challenges amid the accounting issues.

Few community bank CEOs would take a pass on the opportunity to shape what is yet to occur, but, given their responsibility for managing earnings and capital, how many are willing to stand before their board and say, “We must control our own fate”?

Bank CEOs no longer have an option but to take this bold position. Thanks to the Financial Accounting Standards Board (FASB) and the new Current Expected Credit Loss (CECL) standard for calculating reserves, banks must look forward.

They must consider the risk of loss for all financial assets. This includes, as defined by FASB, “loans, debt securities, trade receivables, net investments in leases, off-balance-sheet credit exposures, reinsurance receivables, and any other financial assets not excluded from the scope that have the contractual right to receive cash.”

Auditors and regulators are currently working to shape what this means in practice. However, CEOs and boards cannot wait. because the allowance for loan and lease losses relates directly to overall performance. Understanding how CECL requirements will affect the allowance is essential to managing capital, earnings volatility, and overall.

Under CECL or “expected loss,” the institution will require more and better data, effective processes, and appropriate methodologies to yield an estimate of loss that is worthy of a CEO’s confidence. Such an estimate will be required for directional consistency in managing future performance and improving the bank’s position.

A well-crafted loan loss estimation process under CECL could give the CEO control over the factors that influence performance, and consequently the fate of the banks (as well as his or her own).

And here is the best news. Most CEOs already know where to begin with CECL. It’s already part of their job.

What’s new? Why is it important?

Instead of relying almost solely on historical loss data, which is required by the current GAAP-compliant incurred loss standard, the future becomes a fundamental consideration in calculating the ALLL under CECL.

As expected, credit loss is examined from the CEO’s chair, estimating the allowance begins to look much like the process of creating a revenue and expense budget, where expectations are quantified. CECL requires judgment, based on experience. As such, the CEO should be concerned about how the new financial standard is going to be implemented. The CEO is in the best position to provide direction on the processes and practices that will lead to the adoption of the bank’s approach to estimating the allowance under CECL.

As CECL was under development, projections suggested that it would increase an institution’s loan loss provision up to 50%. Opponents predicted CECL have as much impact on banks as the Dodd-Frank Act. Some suggested that mergers and acquisitions would be fueled by the expense associated with compliance.

Is there truth in these and other worrisome claims? The answers begin with the CEO.

 At a macro level, CEOs are best positioned to direct and question quantification of future performance, as they are the “big picture” experts. So, too, the process for developing the estimation under CECL is more relevant when various perspectives, including the CEO’s, are considered.

The CEO’s years of experience are invaluable to minimizing any upheaval, especially at a time when junior members of the staff are facing their first major amendment to accounting standards. Many CEOs have learned lessons dealing with past accounting and regulatory changes; have an intimate understanding of the bank’s processes for such things as budgeting revenue and expense. And they enjoy the C-suite’s connection to the vision, direction, and performance of the bank. These put the CEO in the perfect to direct the CECL planning and initial implementation process.

As part of the expected credit loss estimating process the institution must make assumptions, and regulators and auditors will examine those assumptions closely.

Nothing new here, as they commonly request improved supporting documentation relative to the ALLL. What will likely be a “new normal” is a demand for alignment of the assumptions among the institution’s various departments.

Are expectations in the current budget, such as loan growth, consistent with expectations in the allowance calculation? Are they supported by documentation? Meeting these demands also creates an opportunity for improvement: institutions can use the data developed for the loan loss provision to augment other management metrics and validate or invalidate expectations created at the highest levels of leadership.

Budgeting, CECL, and the ALLL

Creating a P&L budget is more a management than accounting exercise, taking a global view of the institution, the loan portfolio, and the marketplace, and the factors that influence all three. The CEO and senior leadership, from their unique vantage point, apply their experience, insight, and expertise to come up with expectations for the budgeted period.

Budgeting and planning begin with assumptions. Before the first calculation is executed, a general understanding of anticipated developments, outcomes, and concerns for the coming year and beyond must be developed.

Considerations include:

• Expectations based solely on historical performance

• Variables that have impacted historical performance

• Relevant current conditions

• Key internal indicators

• Key external indicators

• Future plans and initiatives

• Experience and expertise of key personnel

• Key products and services

This is hardly an exhaustive list, but assumptions developed from these considerations illustrate that the CEO knows where the financial institution is headed and trusts his or her knowledge. The same assumptions should be used as the first step in estimating the ALLL.

In fact, the budgeting exercise is a great way to start working toward a well-designed expected credit loss methodology. Clear communication of management goals, expectations, and concerns alert those responsible for the ALLL to what the C-suite has determined is important in terms of performance indicators.

If senior management believes unemployment will rise and has budgeted accordingly, but allowance documentation supports a different conclusion, there is room for criticism. If conclusions resulting from the loan loss provision are not reconciled with management expectations and exceptions are not resolved, then one or both will be inaccurate. An inadequate allowance translates to increased capital risk.

Management expectations could drive a CECL-compliant methodology—or methodologies, as some institutions will adopt different allowance analyses for different groups of financial instruments. The institution’s approach to a CECL-compliant loan loss provision will be determined by several factors, including the availability and quality of data; capabilities and resources, including staff; processes and technology; and segmentation of loans.

The CEO’s perspective could impact any or all of the variables, and as a result, the institution’s choice of methodologies, the most likely under CECL being vintage, cohort, probability of default/loss given default, and loss-rate.

Meeting the “reasonable and supportable” threshold will be demanding at several levels within a financial institution. But an appropriate starting point for the reserve estimate exercise are the expectations of the CEO and senior management, in turn allowing the allowance process to either validate or challenge their projections.

A tremendous amount of data and objective information is leveraged to develop the allowance. CECL requires even more data from even more sources to project the most likely performance of the lending portfolio. The true test of whether or not projections are accurate comes after the fact. Yet the data driving the ALLL can strengthen or challenge the performance expectations of management before they are revealed to the board and shareholders.

CEO’s experience is pivotal (until it’s not)

Absent intentional misconduct, even the highest-ranking bank officers make judgment errors. At the 2016 National ALLL Conference, Kelly Peters, an authority in behavioral economics, challenged an audience of financial industry professionals to consider that many conclusions, including the loan loss reserve, are shaped by the biases of the decision makers. Preconceived notions are the product of experience, background, education, and other influences.

Adam Grant, author of Originals: How Non-Conformists Move The World, explains in the 2016 bestseller how some of the most successful and innovative people of our time, including Steve Jobs and Jeff Bezos, were horribly wrong about a personal transportation invention called the Segway. Dean Kamen kept the curtain drawn on his electric, two-wheeled vehicle for months, building excitement with expert marketing, before the 2001 unveiling in 2001. In fact, none of Kamen’s aggressive sales projections were realized.

Originals assesses Steve Job’s myopia this way: “Three major forces left him overconfident about the Segway’s potential: domain inexperience, hubris, and enthusiasm.”

A bank CEO’s instincts and intuition can seem valuable assets—but the experiences and environments that fostered them may no longer be relevant. Consider how technology has changed the task of managing banks.

“Intuitions are only trustworthy when people build up experience making judgments in predictable environments,” explains Daniel Kahneman, author of Thinking Fast and Slow. Kahneman also points out in the book that, “in a rapidly changing world, the lessons of experience can easily point us in the wrong direction.”

Every CEO can understand Steve Jobs trusting his intuition or instincts. But just as the axiomatic disclaimer about investments cautions us that past success does not guarantee future performance, leaning solely on an individual’s intuition that results from a very narrow and personal set of experiences is dangerous.

Another concern emanates from a practice known as “confirmation bias.” We tend to look for evidence that confirms our existing beliefs. Recognizing that these and other biases can impede effective decision-making is a positive step. One strategy to consider for estimating the loan loss reserve under CECL: Have senior management develop assumptions, like those used for budgeting and planning, and determine what these beliefs will mean to capital, dividends, and other performance metrics.

Next, include the ALLL team in an exercise to disprove each of them. Bias can be reduced by involving multiple individuals and departments in the linked budgeting and loan loss estimating processes. Communication of goals, challenges based on data, and alignment of objective expectations throughout the organization should render the future performance of the institution more predictable, and help the CEO guard against a narrow, biased view.

Income statement budgeting and CECL loan segmentation

The “kumbaya moment” around CECL seems to be the realization that many banks lack enough of the correct data to deal with the expected loss standard. Experts agree that data collection is the first job in preparing for CECL.

Just as words are essential to writing, data is necessary to tell the story of a quarterly allowance. However, it takes a writer to assemble written language in a meaningful manner. And thus, data by itself is useless.

Data cannot simply be collected and held until needed. The extent of loan-level data variations is proportional to such factors as look-back period, volatility in personnel, market conditions, lending policies, and underwriting. Historical data requires testing, analysis, and study.

Filling servers with data is not helpful if the information does not properly relate to both the current and expected future conditions of the bank.  

As well, data itself is not the end-all be-all. Grouping loans by similar risk characteristics, “segmentation,” is vital.

"The difference in allowance could be substantial based on how you segment your loans," according to the 2016 National Conference presentation by Moss Adam's Mike Thronson and Gabe Nachand. "Segment according to loan type but further by credit metrics, rate tiers, geography . . . You will need more segmentation than by call reports."

So, too, financial statements group similar revenue and expense items into meaningful categories. This makes sense, not only when reviewing performance, but also when establishing a budget for the coming period. The effectiveness of budgeting as a management tool would be diminished if income expectations were stated in terms of individual customers, products, or services. The same is true for expenses, although this lends itself to a bit more meaningful granularity.

The structure of an income statement is telling. For instance, the effectiveness of a team at a branch could be measured by comparing total employee expense to revenue generated only at the single location. However, you will likely reach an entirely different set of conclusions if the same employees were categorized not by location but by responsibility, grouped with similar employees across the entire organization.

In both examples, employee costs at the branch are identical; how and why these expenses are grouped and compared are the keys to divining the most impactful evidence of financial performance.

The thought process behind organizing a P&L is consistent with segmenting loans under CECL, where loans are to be pooled based on similar risk characteristics. Is it the same as grouping loans under ASC 450-20 (FAS 5)? Although this is the place to start, something more sophisticated than ASC 450-20 pooling will be expected under CECL.

The current incurred loss standard relies on historical loan data, adjusted based on management discretion. A loss has to occur before an increase in the allowance is warranted. This encourages specific types of pool structures.

Under CECL, an allowance should be estimated even if the risk of loss is remote, and reserves are calculated accordingly. The new process with different expectations suggests existing pooling is not likely to deliver an estimation that captures the most appropriate level of risk.

Thousands of loans might have the same product name, but there are multiple variables in each borrower’s risk profile—credit score, type of collateral, term, loan amount, interest rate, location, and origination date, to name a few. Analyzing the impact of these and other relevant factors is useful in determining criteria for identifying commonality among loan types for the purpose of segmenting, then managing and estimating a defensible loan loss reserve.

Appropriate segmentation may not be obvious. Each iteration will require testing, analysis, and more testing. A different segmentation will produce a different estimate, and an equally unique risk profile. Effective management of this part of the process is the key factor in the eventual allowance amount.

Both the quality and quantity of data are important in this process. The accuracy and amount of information is directly related to the number of segmentation options. This work is not simply about compliance, but diagramming risk as it relates to lending.

Thus, the allowance estimation process provides more than the required quarterly summary; it delivers actionable analytics.

Call it a budget for risk.

CECL’s “reasonable and supportable” forecast

Budgeting and forecasting are linked, yet different. The first rests on historical performance, while a forecast combines budget assumptions with other factors expected to influence the numbers.

Bank leaders develop a sense of what will derail their best-made plans. Some variables can be influenced at the local level, such as staff expertise and experience, policies, promotion, and the discontinuation or creation of new products and services. Interest rates and regulation are beyond management’s control. Some might argue the latter category can have more impact.

Thus, creating a “reasonable and supportable forecast” is a requirement of the new expected loss standard that causes much head scratching. It has been suggested that justifying assumptions about future performance to the point of quantifying risk at the loan level will lead to meaningless numbers—though perhaps what’s causing the heartburn is the CEO’s doubts in the forecasting process.

While there is cause for concern, FASB and others with intimate knowledge of the new standard are shedding light on the meaning and practice of forecasting. Understanding lies within the two very important words, “reasonable” and “supportable.”

Again, consider the creation of a budget. Preparing an expectation of financial performance is rooted in combining independent revenues and expenses into meaningful groups. As influencing factors are considered and their impact calculated, the budget becomes plausible. Actual outcomes will differ, of course, but when a budget is based on trusted data and a reliable process, then the projections are reasonable.

The same holds true for a reasonable forecast as prescribed by CECL. The underlying data for the loan loss provision should be abundant and of unquestionable quality. Quantity and quality work together as more good data produce better pooling and methodology choices.

Most financial institutions do not have a staff economist—nor does CECL require one. In fact, the institution has access to far more economic data—global, national, state, and even local level data—than it will ever need in Federal Reserve Economic Data (FRED). The Federal Open Market Committee (FOMC) of the Federal Reserve also provides a respected economic outlook. Determining the data that are most meaningful to the institution’s future performance—and those that aren’t—then becomes the key to a reasonable and supportable forecast.

Tom Cunningham, retired vice-president and senior economist for the Atlanta Federal Reserve District Bank, suggests comparing measures of employment, housing, pricing, and real gross domestic product to loan performance as a starting point. There are other economic measures that vary in importance depending on the institution (e.g.: oil prices for community banks in Texas), but Dr. Cunningham’s four recommended measurements are widely accepted determinants, and thus easily pass the “reasonable” test.

Supporting a forecast becomes less of a struggle when institution-level data is accurate—rooted in several years of history and economic data derived from unimpeachable sources like FRED and the FOMC.

This is another area where the allowance estimation process can serve to test management’s future expectations. For example, compare the FOMC’s outlook on unemployment and gross domestic product to budget revealing where the Federal Reserve and institution management agree and where there are differences. Then use the differences to improve both budgeting and allowance processes, for more substantial conclusions and better support.

How far into the future are financial institutions expected to forecast? FOMC looks ahead for each of the subsequent three years with a “longer run” forecast that suggests the long-run steady state. But a bank’s decision relates directly to its average life of a loan, and again, the quality and quantity of its data.

Consider loans that have been segmented by origination date, that is, by vintage.

Assume the average loan life is five years. The institution’s experience is that defaults are more likely to occur in the first and second years, and decrease steadily in years three to five. The evidence is strong, backed by several quarters of experience and a statistically meaningful number of loans. Management might therefore be confident applying the loss experience from this example and create a five-year forecast, adjusted according to expectations for the U.S. economy.

Regardless of method, a reasonable and supportable forecast does not require a crystal ball—and guessing isn’t part of the process. A healthy amount of good data and a thorough process will combine for calculations that make sense.

Bringing it back to the C-suite

A financial institution’s ALLL expert or team must quantify considerations, assumptions and expected outcomes. But expectations begin at the C-suite.

Because estimating the loan loss is driven by copious amounts of quality data and trusted sources of external information, the allowance process can validate or challenge the outlook of leadership.

CEOs, even those without experience estimating the allowance, can apply their knowledge of the budgeting process and play a significant role in developing and implementing a CECL-compliant methodology.

Like budgeting, creating a loan loss forecast is not a prediction of a future that can’t be altered. On the contrary, understanding the expected performance of the institution and the associated risk leads to a better understanding of how to impact both. And while the goal might be increased profitability, mitigating the influence of the unexpected is also a worthy aspiration.

About the author

Dalton T. Sirmans is a co-founder of MainStreet Technologies, Inc. (MST), developer of the Loan Loss Analyzer portfolio risk management software platform for banks and credit unions. MST also guides financial institutions in their transition to CECL.

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