As it turns out, model validation managers at regional banks didn’t get much time to contemplate what they would do with all their newly discovered free time. Passage of the Economic Growth, Regulatory Relief, and Consumer Protection Act appears to have relieved many model validators of the annual DFAST burden. But as one class of models exits the inventory, a new class enters—CECL models.

Banks everywhere are nearing the end of a multi-year scramble to implement a raft of new credit models designed to forecast life-of-loan performance for the purpose of determining appropriate credit-loss allowances under the Financial Accounting Standards Board’s new Current Expected Credit Loss (CECL) standard, which takes full effect in 2020 for public filers and 2021 for others.

The number of new models CECL adds to each bank’s inventory will depend on the diversity of asset portfolios. More asset classes and more segmentation will mean more models to validate. Generally model risk managers should count on having to validate at least one CECL model for every loan and debt security type (residential mortgage, CRE, plus all the various subcategories of consumer and C&I loans) plus potentially any challenger models the bank may have developed.

In many respects, tomorrow’s CECL model validations will simply replace today’s allowance for loan and lease losses (ALLL) model validations. But CECL models differ from traditional allowance models. Under the current standard, allowance models typically forecast losses over a one-to-two-year horizon. CECL requires a life-of-loan forecast, and a model’s inputs are explicitly constrained by the standard. Accounting rules also dictate how a bank may translate the modeled performance of a financial asset (the CECL model’s outputs) into an allowance. Model validators need to be just as familiar with the standards governing how these inputs and outputs are handled as they are with the conceptual soundness and mathematical theory of the credit models themselves.

CECL Model Inputs – And the Magic of Mean Reversion

Not unlike DFAST models, CECL models rely on a combination of loan-level characteristics and macroeconomic assumptions. Macroeconomic assumptions are problematic with a life-of-loan credit loss model (particularly with long-lived assets—mortgages, for instance) because no one can reasonably forecast what the economy is going to look like six years from now. (No one really knows what it will look like six months from now, either, but we need to start somewhere.) The CECL standard accounts for this reality by requiring modelers to consider macroeconomic input assumptions in two separate phases: 1) a “reasonable and supportable” forecast covering the time frame over which the entity can make or obtain such a forecast (two or three years is emerging as common practice for this time frame), and 2) a “mean reversion” forecast based on long-term historical averages for the out years. As an alternative to mean reverting by the inputs, entities may instead bypass their models in the out years and revert to long-term average performance outcomes by the relevant loan characteristics.

Assessing these assumptions (and others like them) requires a model validator to simultaneously wear a “conceptual soundness” testing hat and an “accounting policy” compliance hat. Because the purpose of the CECL model is to prove an accounting answer and satisfy an accounting requirement, what can validators reasonably conclude when confronted with an assumption that may seem unsound from purely statistical point of view but nevertheless satisfies the accounting standard?

Taking the mean reversion requirement as an example, the projected performance of loans and securities beyond the “reasonable and supportable” period is permitted to revert to the mean in one of two ways: 1) modelers can feed long-term history into the model by supplying average values for macroeconomic inputs, allowing modeled results to revert to long-term means in that way, or 2) modelers can mean revert “by the outputs” – bypassing the model and populating the remainder of the forecast with long-term average performance outcomes (prepayment, default, recovery and/or loss rates depending on the methodology). Either of these approaches could conceivably result in a modeler relying on assumptions that may be defensible from an accounting perspective despite being statistically dubious, but the first is particularly likely to raise a validator’s eyebrow. The loss rates that a model will predict when fed “average” macroeconomic input assumptions are always going to be uncharacteristically low. (Because credit losses are generally large in bad macroeconomic environments and low in average and good environments, long-term average credit losses are higher than the credit losses that occur during average environments. A model tuned to this reality—and fed one path of “average” macroeconomic inputs—will return credit losses substantially lower than long-term average credit losses.) A credit risk modeler is likely to think that these are not particularly realistic projections, but an auditor following the letter of the standard may choose not find any fault with them. In such situations, validators need to fall somewhere in between these two extremes—keeping in mind that the underlying purpose of CECL models is to reasonably fulfill an accounting requirement—before hastily issuing a series of high-risk validation findings.

CECL Model Outputs: What are they?

CECL models differ from some other models in that the allowance (the figure that modelers are ultimately tasked with getting to) is not itself a direct output of the underlying credit models being validated. The expected losses that emerge from the model must be subject to a further calculation in order to arrive at the appropriate allowance figure. Whether these subsequent calculations are considered within the scope of a CECL model validation is ultimately going to be an institutional policy question, but it stands to reason that they would be.

Under the CECL standard, banks will have two alternatives for calculating the allowance for credit losses: 1) the allowance can be set equal to the sum of the expected credit losses (as projected by the model), or 2) the allowance can be set equal to the cost basis of the loan minus the present value of expected cash flows. While a validator would theoretically not be in a position to comment on whether the selected approach is better or worse than the alternative, principles of process verification would dictate that the validator ought to determine whether the selected approach is consistent with internal policy and that it was computed accurately.

When Policy Trumps Statistics

The selection of a mean reversion approach is not the only area in which a modeler may make a statistically dubious choice in favor of complying with accounting policy.

Discount Rates

Translating expected losses into an allowance using the present-value-of-future-cash-flows approach (option 2—above) obviously requires selecting an appropriate discount rate. What should it be? The standard stipulates the use of the financial asset’s Effective Interest Rate (or “yield,” i.e., the rate of return that equates an instrument’s cash flows with its amortized cost basis). Subsequent accounting guidance affords quite a bit a flexibility in how this rate is calculated. Institutions may use the yield that equates contractual cash flows with the amortized cost basis (we can call this “contractual yield”), or the rate of return that equates cash flows adjusted for prepayment expectations with the cost basis (“prepayment-adjusted yield”).

The use of the contractual yield (which has been adjusted for neither prepayments nor credit events) to discount cash flows that have been adjusted for both prepayments and credit events will allow the impact of prepayment risk to be commingled with the allowance number. For any instruments where the cost basis is greater than unpaid principal balance (a mortgage instrument purchased at 102, for instance) prepayment risk will exacerbate the allowance. For any instruments where the cost basis is less than the unpaid principal balance, accelerations in repayment will offset the allowance. This flaw has been documented by FASB staff, with the FASB Board subsequently allowing but not requiring the use of a prepay-adjusted yield.

Multiple Scenarios

The accounting standard neither prohibits nor requires the use of multiple scenarios to forecast credit losses. Using multiple scenarios is likely more supportable from a statistical and model validation perspective, but it may be challenging for a validator to determine whether the various scenarios have been weighted properly to arrive at the correct, blended, “expected” outcome.

Macroeconomic Assumptions During the “Reasonable and Supportable” Period

Attempting to quantitatively support the macro assumptions during the “reasonable and supportable” forecast window (usually two to three years) is likely to be problematic both for the modeler and the validator. Such forecasts tend to be more art than science and validators are likely best off trying to benchmark them against what others are using than attempting to justify them using elaborately contrived quantitative methods. The data that is mostly likely to be used may turn out to be simply the data that is available. Validators must balance skepticism of such approaches with pragmatism. Modelers have to use something, and they can only use the data they have.

Internal Data vs. Industry Data

The standard allows for modeling using internal data or industry proxy data. Banks often operate under the dogma that internal data (when available) is always preferable to industry data. This seems reasonable on its face, but it only really makes sense for institutions with internal data that is sufficiently robust in terms of quantity and history. And the threshold for what constitutes “sufficiently robust” is not always obvious. Is one business cycle long enough? Is 10,000 loans enough? These questions do not have hard and fast answers.


Many questions pertaining to CECL model validations do not yet have hard and fast answers. In some cases, the answers will vary by institution as different banks adopt different policies. Industry best practices will doubtless emerge in response to others. For the rest, model validators will need to rely on judgment, sometimes having to balance statistical principles with accounting policy realities. The first CECL model validations are around the corner. It’s not too early to begin thinking about how to address these questions.