Download FASB`s CECL Model: Navigating the Changes

FASB’s CECL Model: Navigating the Changes
Planning for Current Expected Credit Losses (CECL)
By R. Chad Kellar, CPA, and Matthew A. Schell, CPA, CFA
Audit | Tax | Advisory | Risk | Performance
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Crowe Horwath LLP
The final deliberations by the Financial Accounting
Standards Board (FASB) on its project related
to the impairment of financial instruments1 are
drawing to a close, and the board is expected to
issue a final standard in 2015. Of course, prior
to deciding to issue a final standard, the board
members, as a matter of due process, will ask
themselves whether re-exposure is warranted.
While the deliberations are not yet complete and
the wording from the proposal will be tweaked, the
CECL model, which was proposed2 in December
2012, will remove the “probable” threshold that
exists today and requires the development of an
estimate of all contractual cash flows not expected
to be collected.3 Given the pervasive impact of
the CECL model, many financial institutions are
beginning to think about the impact the new model
is likely to have on their allowance methodologies.
FASB, “Accounting for Financial Instruments – Credit Impairment,” a joint project of the FASB and International Accounting Standards Board,
http://www.fasb.org/jsp/FASB/FASBContent_C/ProjectUpdatePage&cid=1176159268094
1
For background about the project, see Sydney Garmong, “Is the Third Time the Charm? The FASB Proposes Major Changes for Credit Losses,”
Crowe Horwath LLP, January 2013, http://www.crowehorwath.com/ContentDetails.aspx?id=5611
2
Sept. 17, 2013, joint board meeting minutes, “Accounting for Financial Instruments: Impairment,”
http://www.fasb.org/cs/ContentServer?c=Document_C&pagename=FASB/Document_C/DocumentPage&cid=1176163467502
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FASB’s CECL Model:
Navigating the Changes
Table of Contents
Evaluate the Scope..............................................4
Develop an Estimate of Expected
Credit Losses for the CECL Model....................5
■■ Inputs (Expected Credit Loss
Drivers and Expected Life)............................................ 5
■■ Unit of Account.............................................................. 6
■■ Probability or Path......................................................... 7
Examine Methodologies
Commonly Used Today.......................................8
■■ Discounted Cash Flow Analysis.................................... 8
■■ Average Charge-Off Method......................................... 9
■■ Vintage Analysis ............................................................ 9
■■ Static Pool Analysis .....................................................10
■■ Roll-Rate Method (Migration Analysis).........................10
■■ Probability-of-Default Method......................................11
■■ Regression Analysis.....................................................11
Some Considerations for How
Current Methodologies Will
Change Under the CECL Model.......................12
■■ Discounted Cash Flow Analysis...................................12
■■ Average Charge-Off Method........................................12
■■ Vintage Analysis............................................................13
■■ Static Pool Analysis......................................................13
■■ Roll-Rate Method (Migration Analysis).........................13
■■ Probability-of-Default Method......................................14
■■ Regression Analysis.....................................................14
Conclusion..........................................................14
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The new standard is expected to be flexible enough to allow financial institutions to use different methodologies to determine their
allowance. One possibility that is being discussed in practice is that the implementation of the CECL model could be achieved by
incorporating existing risk management practices into the allowance methodology, which would provide entities with a starting
foundation. The first thing management must do is evaluate the current data and methodologies to determine what new information
will need to be acquired and what changes will have to be instituted to transition to the CECL model.
To understand what changes a financial institution might need to make, it is important to consider a few factors:
■■ What instruments will be included in the scope of the new CECL model?
■■ How will expected credit losses be evaluated? What methodologies will be allowed?
■■ What changes to the current methodology and available data will be necessary?
The first thing management must do is evaluate the current data and methodologies to
determine what new information will need to be acquired and what changes will have to
be instituted to transition to the CECL model.
Evaluate the Scope
The FASB concluded during its re-deliberations that entities will apply the CECL model to financial assets measured at amortized
cost.4 Financial assets measured at amortized cost for a typical financial institution include more than just loans; they will include other
financial assets such as debt securities that are classified as held to maturity (HTM). Certain types of financial assets measured at
amortized cost, such as related party loans and receivables between entities under common control, will be excluded from the scope
of the CECL model.5 In addition, the FASB has discussed that entities will need to evaluate expected credit losses on other types of
instruments including (1) loan commitments that are not classified at fair value through net income, (2) financial guarantee contracts not
accounting for as insurance or at fair value through net income, (3) reinsurance receivables, and (4) lease receivables.6
Debt securities that are classified as available for sale (AFS) will be excluded from the CECL model. The accounting for impairment
of AFS debt securities will follow a new modified impairment process that is a combination of the current other-than-temporary
impairment (OTTI) approach in FASB Accounting Standards Codification (ASC) 3207 and the new CECL model. The new modified
impairment approach no longer will require an entity to consider the length of time that the fair value of the security has been
below amortized cost; this requirement has been part of the current OTTI analysis. In addition, entities will record an allowance for
expected credit losses, rather than a direct write-down, on AFS debt securities that can be reversed immediately in the period of
recovery in expected cash flows. However, an allowance for an AFS security would not be recorded “if the financial asset’s fair value
equals or exceeds its amortized cost basis.”8
March 12, 2014, board meeting minutes, “Accounting for Financial Instruments: Impairment,”
http://www.fasb.org/cs/ContentServer?c=Document_C&pagename=FASB%2FDocument_C%2FDocumentPage&cid=1176163908510
4
Oct. 29, 2014, board meeting minutes, “Accounting for Financial Instruments: Impairment,”
http://www.fasb.org/cs/ContentServer?c=Document_C&pagename=FASB%2FDocument_C%2FDocumentPage&cid=1176164536964
5
Ibid.
6
FASB ASC 320-10-35, “Investments – Debt and Equity Securities – Overall – Subsequent Measurement.”
7
March 12, 2014, board meeting minutes, “Accounting for Financial Instruments: Impairment,”
http://www.fasb.org/cs/ContentServer?c=Document_C&pagename=FASB%2FDocument_C%2FDocumentPage&cid=1176163908510
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FASB’s CECL Model:
Navigating the Changes
Equity securities, also excluded from the scope of the CECL model, will be accounted for either at fair value with changes
recognized in net income or under other appropriate accounting (the equity method or the practical expedient for equities without a
readily determinable fair value, for example).
Crowe Observations:
■■ Including HTM debt securities in the CECL model will result in a change in practice. Previously these securities had been
evaluated using the OTTI model.
■■ The change for all HTM or AFS debt securities to use an allowance, rather than recording a direct write-down (basis adjustment),
is positive. By using an allowance, improvement in credit may be recognized immediately.
■■ Purchased credit-impaired (PCI) financial assets and certain beneficial interests in securitized financial assets, if measured at
amortized cost, will record an allowance for expected credit losses under the CECL model upon acquisition (which is a change
from current practice) and in subsequent periods. (Note that the FASB plans to simplify the current PCI model by recording the
face (par) amount, allowance, and noncredit discount such that current loan subledgers may be used.9)
■■ Some types of securities that are PCI or beneficial interests in securitized financial assets that would have historically qualified to
be accounted for as AFS will not be accounted for under the CECL model because they are not measured at amortized cost. At
this juncture, the board has not published tentative decisions on how to handle these types of instruments.
Develop an Estimate of Expected Credit Losses for the CECL Model
When determining how expected credit losses will be evaluated and estimated, entities should examine several factors,
including inputs, unit of account, and probability or path.
Inputs (Expected Credit Loss Drivers and Expected Life)
For financial assets measured at amortized cost, a current estimate of all contractual cash flows not expected to be collected should
be recorded as an allowance for expected credit losses. Entities should consider “past events, current conditions, and reasonable
and supportable forecasts when developing their estimate of contractual cash flows over the life of a related financial asset.” Entities
also should consider “relevant quantitative and qualitative factors” that exist in their business environment and similar factors that
relate to their borrowers (underwriting standards, for example).10
Entities will need to determine estimates of the expected life of a financial asset by considering expected prepayments but not
considering expected extensions, renewals, or modifications – unless the entities anticipate executing a troubled debt restructuring
with the borrower.
The process for determining the expected life of a loan commitment not measured at fair value through net income depends on
the type of loan commitment (funded versus unfunded). For funded loan commitments, the FASB board decided that expected
credit losses should be estimated similar to other loans. That would require entities to consider cash flows over the expected life
including prepayments but excluding extensions, renewals, or modifications unless the entity anticipates it will execute a troubled
debt restructuring. For unfunded loan commitments, the FASB board decided that the expected credit losses for unfunded loan
commitments that are not unconditionally cancelable should reflect the full contractual period of the commitment.11
Feb. 19, 2014, board meeting minutes, “Accounting for Financial Instruments: Impairment,”
http://www.fasb.org/cs/ContentServer?c=Document_C&pagename=FASB%2FDocument_C%2FDocumentPage&cid=1176163851560
9
Oct. 29, 2014, board meeting minutes, “Accounting for Financial Instruments: Impairment,”
http://www.fasb.org/cs/ContentServer?c=Document_C&pagename=FASB%2FDocument_C%2FDocumentPage&cid=1176164536964
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11
Sept. 3, 2014, board meeting minutes, “Accounting for Financial Instruments: Impairment,”
http://www.fasb.org/cs/ContentServer?c=Document_C&pagename=FASB%2FDocument_C%2FDocumentPage&cid=1176164388432
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Crowe Observations:
■■ The shift – from the incurred loss model, which required a loss to be “probable” before it was recognized, to the CECL model,
which represents all contractual cash flows not expected to be collected – will be a significant change that will require alterations
to an entity’s allowance methodologies and data needs.
■■ The quantitative and qualitative factors already noted could be from either internally or externally available sources.
■■ With the change to an expected loss model, considering prepayments for the expected life of longer-term financial assets, such
as 30-year mortgages, can be important depending on how the allowance methodology functions.
■■ Consideration of some of the OTTI criteria for HTM securities will no longer be required. However, the evaluations of expected
credit losses for some debt securities, such as residential mortgage-backed securities (RMBS), are likely to be similar to those
previously used in practice – with the exception of the potential for required pooling of HTM debt securities. In current practice,
entities evaluating OTTI often model both HTM and AFS RMBS debt securities based on available internal and external statistics
for similar instruments.
■■ Historically, existing U.S. generally accepted accounting principles (GAAP) and regulatory guidance have not addressed payment
turnover for lines of credit and credit card loans (that is, if the average life is determined based on gross payments or net payments).
The FASB concluded in its re-deliberations that loan commitments will be addressed in the CECL model, with the funded portion
being similar to existing loans. If unfunded commitments cannot be unconditionally canceled by the lender, expected credit
losses will reflect the full contractual period. It remains to be seen if the FASB will provide an illustration of the mechanics or
further discussion in the basis for conclusions.
Unit of Account
In its re-deliberations, the FASB concluded that entities will be required to evaluate expected credit losses on financial assets on a
pool basis if the assets share similar risk characteristics and are measured at amortized cost. At times, an institution might not have
multiple assets with similar risk characteristics; in that case, it would then evaluate those financial assets on an individual basis. The
evaluation of individual financial assets under the CECL model “should consider relevant internal information and should not ignore
relevant external information”12 (for example, credit ratings and credit loss information for financial assets of similar credit quality).
Crowe Observations:
■■ For financial assets evaluated on an individual basis, the removal of the “best estimate” notion and the inclusion of relevant
internal and external information are likely to encourage institutions to consider, based on expectations of losses for pools of
similar assets that might be externally available, the possibility of expected losses on that individual asset. Said another way,
where a previous conclusion might have resulted in the best estimate of a zero loss on an individually evaluated loan, a different
answer might result when considering the available external information indicating that a probability of a loss exists.
■■ The requirement to measure impairment under the CECL model first by pooling financial assets with similar risk characteristics
would apply to all financial assets that are in scope – generally, financial assets measured at amortized cost. Accordingly, HTM
securities that are measured at amortized cost will be in scope and be required to be measured for impairment in a pool with
securities that share similar risk characteristics – which would be a change from current practice.
“Accounting for Financial Instruments: Impairment Tentative Board Decisions to Date During Redeliberations” as of Oct. 29, 2014,
http://www.fasb.org/cs/ContentServer?c=Document_C&pagename=FASB%2FDocument_C%2FDocumentPage&cid=1176163819556
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FASB’s CECL Model:
Navigating the Changes
Probability or Path
In its re-deliberations, the FASB concluded that entities “should always reflect the risk of loss, even when that risk of loss is remote.”
However, entities would not be required to recognize a loss in the event that there is a probability of default but the amount of the
loss severity would be zero (that is, there is adequate collateral in the event of default). In other words, if the amount of collateral is
such that no loss would be recognized in the event of default, a loss need not be recognized.
Entities will need to develop an estimate of the expected credit losses, and one method might include starting with the historical
losses and then adjusting for differences based on current conditions and reasonable and supportable forecasts. However, the
FASB clarified that entities will not be required to forecast conditions and make related adjustments to the historical loss patterns
for the expected life of the financial asset; instead “an entity should revert to unadjusted historical credit loss experience for future
periods beyond which the entity is able to make or obtain reasonable and supportable forecasts.”13 Commonly, this is referred
to as “reversion to the mean.” Reversion to the mean or mean reversion is a mathematical theory often used in various financial
applications such as stock investing. In simple terms, a variable is “mean reverting” if over time it tends to return to a particular level
following periods of increases (or decreases) above (or below) that level.
The FASB provided two alternatives to accomplish reversion to the mean: (1) by reverting over the financial asset’s estimated life
on a straight-line basis or (2) by reverting over a period and in a pattern that reflects the entity’s assumptions about expected credit
losses over that period. As an example, assume that an entity has a 30-year residential mortgage with an estimated life of seven
years. Management can reasonably estimate expected credit losses for the next two years, but after that, management does not
have reasonable and supportable forecasts to determine expected credit losses. One option to establish expected credit losses in
the final five years would be to revert on a straight-line basis to the unadjusted average credit losses for the remaining period. In this
example, if expected losses over the next two years are 10 basis points (bps) and the unadjusted historical loss experience is
15 bps, the straight-line reversion would be as follows:
Year
1
2
3
4
5
6
7
Expected Losses
10 bps
10 bps
11 bps
12 bps
13 bps
14 bps
15 bps
Crowe Observations:
■■ The FASB’s characterization of “risk of loss” establishes a high hurdle for not recording an expected credit loss. Institutions will
be required to consider the likelihood of nonpayment or a loss based on all available information, but information indicating a
probability of default may be offset by the impact of collateral and other available sources of repayment.
■■ Entities will be expected to make projections about the expected losses as far as they can reasonably estimate into the future.
The FASB has provided a practical expedient by allowing entities to assume that over the remaining term of the financial asset,
expected credit losses should return to their unadjusted average historical credit losses. Entities will have an option to select
either pattern of reversion, which would need to be disclosed. Changes in the reversion period would represent a change in
estimate rather than a change in accounting policy. (A change in accounting policy would require the change to be preferable.)14
Accounting For Financial Instruments: Impairment – Tentative Board Decisions to Date During Redeliberations” as of Oct. 29, 2014,
http://www.fasb.org/cs/ContentServer?c=Document_C&pagename=FASB%2FDocument_C%2FDocumentPage&cid=1176163819556
13
FASB ASC 250-10-45, “Change in Accounting Principle.”
14
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Examine Methodologies Commonly Used Today
Providing flexibility, the FASB concluded that there will not be restrictions on the types of methodologies used to develop an
estimate of expected credit losses. Specifically, it said that entities will not be prohibited from using discounted cash flow, lossrate, probability-of-default, or provision matrix models when developing their estimates. Many models currently used by financial
institutions would fit into one of these categories and be capable of assisting in the development of an expected credit loss estimate.
At times, different models are used on different asset types, or combined to use on one asset type, to develop an estimate of credit
losses. Examples of some models used in practice today include:
■■ Discounted cash flow analysis
■■ Average charge-off method
■■ Vintage analysis
■■ Static pool analysis
■■ Roll-rate method (migration analysis)
■■ Probability-of-default method
■■ Regression analysis
Average charge-off, vintage, and static pool analysis are examples of loss rate methods, while regression analysis may be used to
establish relationships in historical data that can assist in projecting losses within various acceptable methodologies. Following is some
background on how each of these examples is used today in the incurred-loss model for the allowance for loan losses.
Discounted Cash Flow Analysis
As described in ASC 310-10-35,15 a discounted cash flow analysis is based on the present value of expected future cash flows
discounted at the loan’s effective interest rate. This type of analysis is one of the currently prescribed methods for measuring
impairment on an individual impaired loan. (Alternatives include the practical expedient of applying the collateral-dependent method
or the observable market price approach.) Expected cash flow assumptions used in the discounted cash flow analysis are based on
an institution’s best estimate of “reasonable and supportable assumptions and projections.”
Crowe Observation:
■■ Although the collateral-dependent method is not within the scope of this article, it is worth mentioning here that the FASB plans to
change the definition of “collateral-dependent,” which may have an effect on when the application of this method is appropriate.
Providing flexibility, the FASB concluded that there will not be restrictions on the types of
methodologies used to develop an estimate of expected credit losses.
FASB ASC 310-10-35, “Receivables – Overall – Subsequent Measurement.”
15
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FASB’s CECL Model:
Navigating the Changes
Average Charge-Off Method
The average charge-off method is generally the most commonly used approach for evaluating impairment on pools of financial
assets and is fairly straightforward relative to many other approaches. This method calculates an estimate of losses primarily based
on past experience. Generally, an institution starts by dividing the financial asset portfolio into segments and then determines a
historical look-back period (which, depending on the asset class, could be a number of months or a number of years) that is long
enough to develop an accurate estimate of incurred losses for the segment. Next, the institution calculates an average charge-off
ratio for each segment and makes necessary adjustments to that historical average charge-off ratio to reflect the impact of
differences in various quantitative or qualitative factors. In addition, institutions often “weight” the data in the look-back periods in an
effort to develop an appropriate estimate of probable incurred losses that exist at the point of measurement.
Depending on the portfolio, segmentation can be achieved in many ways by identifying similar risk characteristics (for example,
financial asset type, collateral type, size, credit score, geography), and the data needs of this method are modest compared to
those of other methods.
Vintage Analysis
Vintage analysis measures impairment based on the age of the accounts and the historical asset performance of assets with
similar risk characteristics. This methodology works well with types of financial assets that follow patterns or loss curves that
are comparable and predictive for subsequent generations of financial assets (indirect auto loans, for example). First, an entity
determines an appropriate type of financial assets that share similar risk characteristics, and then the entity develops a cumulative
loss curve for the applicable financial assets based on historical data. It is common for different “vintages” to be analyzed by year of
origination, assuming the pool of loans is homogenous.
For vintage analysis, adjustments may be made for differences in quantitative or qualitative factors from period to period, but generally
the financial asset would be assigned a loss factor based on the point on the loss curve that correlates to the financial asset’s age.
For example, a pool of similar five-year financial assets might show loss experience as follows:
Loss Experience by Year Following Origination
Year 1
Year 2
Year 3
Year 4
Year 5
0.25%
0.50%
1.00%
0.75%
0.00%
Typically, the incurred losses for a pool of assets in year three would be 1 percent. However, based on the historical loss experience
shown in the table, the total expected losses for the life of the pool of assets would be 2.5 percent, which is the accumulation of
the five-year loss experience. When such loss curves (which are used to generate loss estimates based on the age or seasoning
of the loan portfolio) are further broken down into year of origination, the loss rates are more granular and lend themselves easily
to regression analysis in order to establish relationships between loan underwriting (such as credit score or loan-to-value ratio)
and economic variables (such as unemployment and housing price index for mortgage loans). This makes it easier to track if, for
example, loans originated five years ago have had a very different first-year loss experience compared to loans originated last year.
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Static Pool Analysis
Vintage analysis and static pool analysis are commonly interchangeable terms; however, “static pool” simply means segmenting
and tracking assets over a period of time based on similar risk characteristics. In practice, the main difference between vintage and
static pool analysis is that vintage analysis is based on the year of origination and/or the age of the asset while static pool analysis is
based on a type of shared pooling criterion and assets originated in a similar time period.
Static pools often are formulated by aligning common risk characteristics within existing segments or classes (cohorts) of loans.
Static pools often are segmented by similar risk characteristics such as collateral type, loan structure, credit risk indicators such as
risk rating or consumer credit scores, and loan-to-value ratio for assets originated in the same period. Commonly used to track loss
rates, static pools also can be used to track other assumptions affecting credit loss and timing – assumptions about prepayment
rates, cumulative default probabilities and default curves, and loss severity, for example. Thus, static pools often are used to support
many components of the various acceptable methodologies.
The main difference between vintage and static pool analysis is that vintage analysis is
based on the year of origination or the age of the asset while static pool analysis is based
on a type of shared pooling criterion and assets originated in a similar time period.
Roll-Rate Method (Migration Analysis)
The roll-rate method is often referred to as “migration analysis” or “flow model” and is based on determining a prediction of credit
losses based on segmentation (by delinquency or risk rating, for example) of a portfolio of financial assets. No standard roll-rate
model is used throughout the financial institutions industry, but most of the models used are based on similar underlying principles.
For example, the portfolio could be divided into different risk ratings. Once segmented, the percentages of assets that will “roll” or
“migrate” to a more severe risk rating are measured and are referred to as “roll rates.” Financial institutions might incorporate an
averaging technique over time in order to develop an average roll rate for each segment that could be adjusted for quantitative or
qualitative factors. After the roll rate is determined for each segment, each respective roll-rate percentage is applied to the balance
in each category to arrive at an estimate of the amount that will migrate to the next category. The total migration for each category is
aggregated to determine the allowance.
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FASB’s CECL Model:
Navigating the Changes
Probability-of-Default Method
The probability-of-default method is used to estimate credit losses by considering three components: (1) probability of default, (2)
exposure at default, and (3) loss given default. The method is also used by many risk management systems and within the Basel II
and Basel III frameworks.
The three components generally are defined as follows:
1. Probability of default (PD) – Probability of default over a given time period
2. Exposure at default (EAD) – Balance of the relationship at default
3. Loss given default (LGD) – Ratio of loss relative to the EAD at default
In this simple illustration, assuming none of the three components is correlated with any other component, expected credit losses
would be expressed by the following equation:
Credit losses = PD × EAD × LGD
First, a financial institution must segment its portfolio by risk characteristics and develop estimates of these three components
based on uniform definitions of “default” and “loss.” The development of each estimate generally is completed as follows:
■■ PD could be a simple average, externally acquired and mapped to the specific segments analyzed, or it could be based on
various default probability models on a by-borrower or by-dollar basis.
■■ EAD could be the balance of the financial asset today or a higher or lower balance depending on the type of product (amortizing,
nonamortizing, or revolving).
■■ LGD – similar to PD – could be a simple average, externally acquired, or based on models.
After the portfolio is segmented and these factors are developed, further adjustments can be made based on correlations that might
exist among the factors. While beyond the scope of this paper, one consideration for a probability-of-default model is the impact of
correlation between the components. For example, during a recession it is common for probability of default to rise and loss given
default also to rise. An entity should consider appropriate adjustments for the correlation of these components to avoid misstating
the amount of credit loss.
Regression Analysis
Regression analysis uses economic data such as unemployment rates, bankruptcy rates, and the consumer debt-to-income ratio to
estimate a relationship between this data and losses in a portfolio. Essentially, an institution uses statistics to determine an estimate
of credit losses (the dependent variable) based on one or multiple inputs (independent variables). Because of the complexity of the
models, the data requirements, and the need for highly trained personnel, regression analysis is not widely used in practice, but it is
used at times in combination with some of the other methodologies.
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Some Considerations for How Current Methodologies
Will Change Under the CECL Model
Regardless of how allowance amounts are calculated, generally the CECL model will incorporate one significant change based on
the previously discussed methodologies. Specifically, the CECL model will require a change to the allowance methodology from
today’s incurred loss model to an expected credit loss model, which is a lifetime estimate. Because of that fundamental change,
institutions will have to develop estimates that are clearly more forward-looking than they were in the past.
Institutions will have to change their methodology (by either modifying their existing methodology or making a wholesale change
in methodology) to implement the CECL model. However, the models do not need to be unnecessarily complex, and only relevant
factors to the underlying financial assets should be used. Entities might need to re-evaluate the current primary drivers of loss when
revising their methodologies. While it’s likely more than one driver of expected losses exists for each portfolio, factors that do not
demonstrate a correlation with expected losses should not be incorporated. While institutions may use existing risk management
practices or systems to develop this forward-looking estimate, many of those systems may not have been subjected to financial
statement and internal control audits, and entities should consider this as they develop a plan to implement the CECL model.
Fundamentally, entities will see changes in the data needed to implement the CECL model. For example, entities might need to
develop and construct loan pools to analyze historical performance. These loan pools likely will need to include longer look-back
periods and new data to enable the analysis of new factors such as prepayments. Changes in the methodologies implemented or
the risk characteristics used to organize the portfolio also could require new data to be historically gathered as well as prospectively
tracked (examples include credit scores or other underwriting criteria).
Discounted Cash Flow Analysis
Discounted cash flow methods are expected to change under the CECL model due to the removal of the “best estimate” notion
and a requirement to consider at least some risk of loss. Accordingly, new data might have to be developed or obtained to support
the cash flow expectations, especially for individual assets, given that analysis cannot “ignore relevant external information such as
credit ratings and credit loss information for financial assets of similar credit quality” that may indicate an expected credit loss.16
Average Charge-Off Method
Historically, average charge-off methods have incorporated a look-back period during which an average charge-off percentage is
developed. One consideration when applying the CECL model will be changing from what frequently was an annual average charge-off
rate to a lifetime charge-off rate. The FASB’s 2012 exposure draft did not allow simple multiplication of the average annualized charge-off
factor for the expected life of the asset when developing an expected loss estimate, so a different analysis might be required. Options for
deriving the expected credit losses might include static pool analysis or the application of dynamic annual charge-off rates in conjunction
with dynamic annualized prepayment expectations (in other words, vectored assumptions that change over time in response to factors
that have an impact on those assumptions) to a pool of financial assets for the remainder of their life. Dynamic assumptions are typically
used today in modeling RMBS OTTI and are generally supported by current pool performance and historical vintage analysis.
16
Accounting For Financial Instruments: Impairment Tentative Board Decisions to Date During Redeliberations” as of Oct. 29, 2014,
http://www.fasb.org/cs/ContentServer?c=Document_C&pagename=FASB%2FDocument_C%2FDocumentPage&cid=1176163819556
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FASB’s CECL Model:
Navigating the Changes
One additional consideration for average charge-off methods is that the base percentage is grounded in historical data, but average
charge-off methods frequently require that subjective adjustments be made to reflect changes. Typically, these adjustments consist
of one aggregated adjustment supported by several factors that are often difficult to quantify and support. Our expectation is that
qualitative adjustments to average charge-off models will continue under the CECL model. Some of the other methodologies (such
as probability-of-default methods) incorporate individual subjective adjustments that can be supported at the factor level (such as
prepayment speeds or collateral value changes) and derive changes in estimates accordingly.
Vintage Analysis
Vintage analysis is based on loss curves that include expectations of losses at each point in the life of a financial asset. Accordingly, the
main change to this method under the CECL model is that the allowance will no longer be reflected by a point on the loss curve; rather,
it will be reflected by the remaining area under the loss curve (that is, the expected credit losses on the remaining life of the asset).
Static Pool Analysis
It is important to understand that whatever methodology is used to forecast expected losses, a baseline expectation of portfolio
performance based on history will need to be established. Institutions likely will resort to use of a static pool concept, also referred to as a
“cohort.” Static pools generally are formulated by aligning common risk characteristics within existing segments or classes of loans.
Establishing static pools based on origination dates (same month, quarter, or year) will allow institutions to track life-to-date loss
rates and other performance characteristics that, as tracked over the life of the loans, will generate a baseline for lifetime of loss
estimates. Institutions will need to assess their ability to perform this type of analysis looking back over several years of origination
and collection data for the initial implementation of the CECL model.
Roll-Rate Method (Migration Analysis)
With the roll-rate method, a financial institution will need to assess the primary attributes that most appropriately predict loss and
take into account significant historical data sets. For example, some institutions might believe that implementing a CECL roll-rate
method based on risk rating will be the most predictive of expected losses. However, analysis of various data sets is needed before
the final assessment can be made about what might be most predictive. Often roll-rate models based on risk ratings are not the best
predictive measure because they require regular and timely updates to credit risk ratings for all assets.
Default or loss migrations should be assembled to reflect various economic cycles and tested through those cycles to assess the
reliability of the model. Limitations on time series length, data integrity, and population sizes may need to be supplemented with
judgments and further calibrated over time to improve precision. Ultimately, it will take time for an institution to make these final
determinations before deciding to implement such a methodology.
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Probability-of-Default Method
In order to develop a model driven by the probability-of-default method, the institution must consider significant attributes that
underlie the various pools of assets and demonstrate the strong predictive power of the model through continual back-testing.
Institutions applying a probability-of-default method for the first time will need to assess the reliability and accessibility of historical
data sets that may be used to build the cumulative default probabilities and loss given default.
The institution will first need to assess a standard definition of default and paths to default that might occur within a product line.
Various industry sources of data can be used to assess probabilities of default over various economic cycles to supplement the
institution’s own experience. The performance of commercial mortgage-backed securities and RMBS, reflecting defaults, prepayment
activity, and severity assumptions, for example, can be obtained from various ratings agencies and servicer reports. However, an
institution using industry data must demonstrate comparability among the portfolios being measured. Whether default probabilities are
driven by risk rating, past-due status, consumer credit scores, loan-to-value ratio, or something else, before the model is implemented
it will need to be tested over a significant period of time to prove its predictive power.
Regression Analysis
Regression analysis can be a strong statistical tool to quantify or assess the predictive power of a particular set of assumptions. In
particular, this type of analysis can be useful for developing support for quantitative associations between macroeconomic factors
and losses. For example, one could use regression analysis on economic data such as unemployment and bankruptcy rates to
forecast loss rates on consumer loan products. Using collateral pricing curves, one might use regression analysis on actual lossseverity data to assess the predictive power of a particular assumption (such as the impact of changes in home price indexes to
change in loss given default).
Institutions must assess the confidence level or imprecision acceptable with the use of statistical models. They should understand and
assess imprecision in the models relative to the materiality impact of the allowance calculation and continually calibrate the models
for actual performance. Given the specialized skills needed to interpret and test the results driven by statistical analyses, institutions
might need to purchase additional quantitative tools or acquire new talent to implement these more complex methodologies.
Conclusion
There are several methods available to financial institutions to comply with the CECL model. While the final standard is not yet
issued and the effective date has yet to be set, it’s not too early for financial institutions to think about the methodologies available
and how their existing allowance methodologies would convert to lifetime expected credit losses under the CECL model. Financial
institutions will certainly need time to develop their sources of data, whether internal or external, and to subject their planned
approach to adequate testing so that it will be robust enough to use well into the future – and the FASB will take that into account
when determining the effective date.
14
FASB’s CECL Model:
Navigating the Changes
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Contact Information
Chad Kellar is with Crowe Horwath LLP
in the Indianapolis office. He can
be reached at 317.208.2431 or
[email protected].
Matthew Schell is a partner with
Crowe in the Washington, D.C., office.
He can be reached at 202.779.9930 or
[email protected].
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Published in December 2014.
Crowe Horwath LLP is an independent member of Crowe Horwath International, a Swiss verein. Each member firm of Crowe Horwath International is a separate and independent legal entity.
Crowe Horwath LLP and its affiliates are not responsible or liable for any acts or omissions of Crowe Horwath International or any other member of Crowe Horwath International and specifically
disclaim any and all responsibility or liability for acts or omissions of Crowe Horwath International or any other Crowe Horwath International member. Accountancy services in Kansas and North
Carolina are rendered by Crowe Chizek LLP, which is not a member of Crowe Horwath International. This material is for informational purposes only and should not be construed as financial or
legal advice. Please seek guidance specific to your organization from qualified advisers in your jurisdiction. © 2014 Crowe Horwath LLP
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