The RMA Journal April 2019 | Copyright 2019 by RMA 42

CECL IMPLEMENTATION

REQUIRES A

COMPLEX SET OF CHOICES

OP

ER

ATIO

NA

L R

ISK

April 2019 The RMA Journal 43

CECL IMPLEMENTATION

REQUIRES A

COMPLEX SET OF CHOICES

BY DANIELA THAKKAR, RICHARD CRECEL, AND SONER TUNAY

In OctOber 2018, a survey conducted by Accenture, Global Credit Data, and the Institute of International Finance assessed U.S. banks’ readiness to implement the new CECL accounting standard issued by the Financial Accounting Standards Board under Accounting Standard Update 2016-132.

Key Findings of the SurveyTwenty-six banks participated in the survey, representing over three-quarters of the U.S. lending market. The survey covered a wide range of modeling choices for CECL estimates, including lifetime expected credit loss (ECL), point in time (PiT) probability of default (PD), loss given default (LGD), and exposure at default (EAD). In addition to parameter choices, the survey asked questions about model execution, including technology/platform and data requirements. The survey results indicated the following:

Accenture, Global Credit Data, and the Institute of International Finance partnered to provide U.S. banks with a benchmark to help them assess their readiness to implement CECL. The re-sults offer insight into the challenges faced by the banks in the areas of data management, model development, and technology/implementation.

The RMA Journal April 2019 | Copyright 2019 by RMA 44

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time estimates, and they lack proper benchmarking before Day 1 implementation.

Some BackgroundCECL models currently being devel-oped indicate that bank profitability—and bank department profitability—will be affected as institutions charge for credit loss provisions on new loans and credit downgrades on existing loans.

The new accounting rules came about following the 2008 financial crisis when the International Ac-counting Standards Board (IASB) and the Financial Accounting Standards Board (FASB) jointly developed a forward-looking approach to account for credit losses. Both organizations intended to address concerns raised by a wide range of stakeholders.

In 2013, the methodologies of the two organizations split, and they is-sued separate but similar rules requir-ing banks to include reasonable and supportable forecasts in their credit loss estimates.

IASB published IFRS 9 in July 2014 with implementation required in 2018. FASB issued the current ex-pected credit losses (CECL) rule in June 2016. As noted above, CECL has an effective implementation date of 2020 for SEC-registered banks and 2021 for all others.

These rules represent a major shift in accounting rules for banks

• Banks are currently putting enor-mous effort into creating results that can be reviewed for reason-ableness, stability, and accuracy.

• Banks use different approaches, in-put parameters, and assumptions for modeling the CECL estimates. Based on our earlier experience in benchmarking IFRS 9 expected loss modeling and modeling re-quired for regulatory capital, we expect this modeling will lead to high variability in CECL estimates across banks.

• The 2019 test runs, and parallel runs, will require many institutions to accelerate their efforts if they are to meet their implementation dead-lines of 2020 for SEC-registered banks and 2021 for all others.

• Banks face challenges in back testing and benchmarking life-

CECL HAS AN EFFECTIVE

IMPLEMENTATION

DATE OF 2020

FOR SEC-

REGISTERED

BANKS AND 2021

FOR ALL OTHERS.

FIGURE 2: NUMBER OF FORWARD-LOOKING SCENARIOS

50%

40%

30%

20%

10%

0%

Percentage of Banks

1

2 to 4

5 to 10

Not yet decided

FIGURE 1: CECL PROJECT PROGRESS AT BANKS

Final approval

Parallel run

Reporting/governance

Test run based on draft methodological framework

Model calibration and testing

Begun adjustment of existing models

Development of new EL models

Definition of methodological framework

Gap analysis: Methodology

Gap analysis: Data

Project set-up

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

April 2019 The RMA Journal 45

that once relied on past events and current conditions to estimate credit losses. Implementation entails cross-functional changes to the end-to-end reserving process for financial assets measured at amortized cost, including bank loan books.

How Banks Are ProgressingOverall, the results of the survey showed progress in CECL implemen-tation choices but less progress in ac-tual implementation. Most banks were focused on conducting gap analysis, developing models, and adjusting existing models. A minority of banks may face delays in implementation. Very few banks had gone through final implementation and validation.

We saw a similar situation at this point with the implementation of IFRS 9, where increased efforts, spurred by regulatory concerns, were required to complete implementation. (See article

or 38%) have a forecasting horizon of two to three years, while 27% (seven banks) forecast only the next year or two, which is a shorter duration than the Compre-hensive Capital Analysis and Review (CCAR) forecast period of nine quarters. Banks with a very short forecast horizon are mostly midsize, regional banks with total assets of between $50 billion and $250 billion.

• Of the remaining banks, five use forecasts covering a longer period, three have not yet decided, and one uses a forecasting horizon dependent on the business cycle.

Scenarios used in CECL modeling • Banks are split on whether to reuse scenar-

ios already available from other processes such as budgeting or stress testing (12 banks, or 46%, do this) or whether to create special scenarios for CECL/IFRS 9 provi-sioning purposes (as done by 10 banks, or 38%). The four remaining banks had not yet decided at the time of the survey.

by Daniela Thakkar in the May 2018 issue of The RMA Journal.)

Diversity of MethodologiesFrameworks for models developed by institutions are diverse because they are adapted to the different risk dynamics of the various portfolio segments. Below are the survey’s key findings regarding methodological choices.

Reasonable and supportable (R&S) forecast • CECL requires banks to incorpo-

rate a forward-looking view into their estimates. The R&S forecast length defines the time for which a bank can forecast macroeconomic drivers based on sound economic theory and historical data.

• Banks differ in the length of their R&S forecast horizons. A plurality of banks (10 out of the 26 surveyed,

FIGURE 3: FORECAST LENGTH

50%

40%

30%

20%

10%

0%

Percentage of Banks

1-2 years

2-3 years

4-5 years

Dynamic

Other

FIGURE 4: TIME HORIZON FOR CECL PD ESTIMATES

Longer Term

4-5 years

3-4 years

1-2 years

0% 10% 20% 30% 40%

Retail

Wholesale/Non-retail

The RMA Journal April 2019 | Copyright 2019 by RMA 46

answers is possible), descaling the margins of conservatism and adding forward-looking features, often by fitting macroeconomic variables directly into the model components.

• In wholesale portfolios, the CECL calculation follows mainly the stress test segmentation along ge-ographies, industries, and so on. Most banks (24 out of 26) model each of the CECL components separately: PD, LGD, and EAD.

• Retail portfolios are mostly seg-mented by product type. Banks’ modeling techniques vary sig-nificantly, following a PD-LGD approach, a charge-off approach, a vintage analysis, or roll-rate models.

Probability of default modeling • Given CECL’s importance and im-

pact, it is not surprising that most banks (roughly 80%) develop their models in-house and do not con-sider vendor models.

• For wholesale portfolios, 15 institu-tions have developed separate PiT, forward-looking PD models, which will exist next to the through-the-cycle (TTC) PD models used for regulatory capital. Only seven banks transform TTC PDs into PiT models and three use PiT models to calculate their TTC PDs. For retail portfolios, banks are equally split between developing both PiT PD

• Banks also differ in the number of scenarios they use for model-ing expected losses under CECL. Of the banks surveyed, 10 (38%) use two to four forward-looking scenarios, six (23%) use just one scenario, and three use more sce-narios. Again, banks with just one scenario are mostly regional banks. In line with our earlier observation about the level of progress within banks, it is noteworthy that, at the time of the survey, about a quarter of the participating banks still had not decided on this key issue.

Segmentation: Wholesale versus retail • Banks’ products, portfolios, and

processes differ between wholesale and retail banking. This wholesale/retail distinction also dictates dif-ferences in methodologies and model architectures.

• Our survey found that, when avail-able, CECL models leverage the ex-isting CCAR/Dodd-Frank Act stress test models (22 out of 26 banks, or 85%) or the advanced internal ratings-based models (five out of 26 banks, though overlap between

FIGURE 5: OVERVIEW METHODOLOGY AND APPROACH

TEMPLATE

CECL Parameters

CECL Scenarios

Hypothetical Portfolio

CECL Model Suite of

Individual Bank

RESULTS SUBMITTED BY BANKS TO GCD

Lifetime PD

Lifetime LGD

Lifetime EAD

ECL

Collect, anonymize, and aggregate data

Return anonymized data and benchmarking reports back to banks

Data Analysis

Result Interpretation

IndustryAdvocacy

Socialize, Fine-tune, and Publish Study Report

FIGURE 6: HYPOTHETICAL PORTFOLIO

ASSET CLASS/BORROWER TYPE

Residential Mortgage

Heloc

Auto

Personal Unsecured

Credit Cards

Large Corporates

Mid-Market

Business Banking

CRE - IP, Construction

OBLIGOR

Credit Quality

Industry

Geography

EXPOSURE

EAD Range

Avg. Usage

O/S

Limit

Revolver/Term

Reference Rate

Maturity

FACILITY

LGD Range

Facility Type

Secured/Unsecured

PERMUTATIONS BY ASSET CLASS/BORROWER TYPE

Residential Mortgage 64

Heloc 16

Auto 32

Personal Unsecured 18

Credit Cards 64

Large Corporates 12

Mid-Market 12

Business Banking 12

CRE - Income-Producing, Construction

4

ILLUSTRATIVE

X X X

April 2019 The RMA Journal 47

models and TTC models or con-verting PiT estimates into TTC estimates.

• This approach differs from what we learned from our work on IFRS 9 in Europe, where many banks use their TTC-based capital models as a starting point for their PiT PD.

Aggregation of scenarios and quali-tative overlay • Banks using more than one sce-

nario are required to aggregate the calculated CECL estimate per sce-nario into one final CECL estimate.

• Assigning probabilities to a scenario is challenging. Of the 17 banks that have or will most likely implement various scenarios, eight (47%) choose for asymmetric probabili-ties—that is, a different probability for a downturn scenario than for an upturn scenario. Three banks are using symmetric probabilities, and the remainder still need to decide.

Overall, we see no strong pattern for model adjustments or overlay to account for model limitations toward extremely volatile scenarios and non-linear risks. At the time of the survey, it appeared that most institutions had not yet tackled that difficult issue.

Impact of Modeling Choices With CECL being a principles-based approach, it is expected that banks would differ in their modeling choic-es. Currently, only limited industry studies are available to measure the impact of the different modeling choices on CECL estimates.

When setting up their models, banks usually perform an analysis to understand the sensitivity of the CECL estimates to certain key assumptions, methods, and parameters, such as the scenarios, the reasonable and support-able time horizon, the mean reversion period, the mean reversion method, and/or the historical loss rate beyond the R&S forecast period. In doing so, banks not only look at the level of the

are ready, banks can participate in running a benchmark portfolio to compare results against their peers. This study can offer in-sight into the different approaches used by banks and help pinpoint areas that produce the largest differences in estimates. It can also alert regulators and auditors to possible vari-ances, which may encourage them to provide more guidance or standardization.

Once CECL is implemented, the work cannot stop. Validation and back testing will have to continue to ensure that mod-els stay robust and accurate. Unlike capital and stress testing, extra conservatism will not be a cure for uncertainty. CECL account-ing models must be accurate, which places extra emphasis on data quality, collection, and availability.

Lastly, concerns have been raised regard-ing the potential procyclicality of CECL. How a bank behaves during a downturn can have as much impact on estimates as a model or parameter choice.

A presentation detailing the benchmarking work (see Figure 5 and 6) is available on the Global Credit Data website, www.globalcreditdata.org.

CECL estimate as of a certain period, but also at the variation of the CECL estimate over time to see how the models react in worsening economic conditions.

Conclusion CECL implementation requires a complex set of choices for the bank-ing industry. Models, parameters, data sources, systems architecture, and economic scenarios must all be com-bined to produce timely and frequent CECL projections. Banks are currently putting enormous effort into creating results that can be reviewed for reason-ableness, stability, and accuracy.

The results of the October 2018 survey suggest that many banks have decided on how to approach these choices, but a significant minority have fallen behind. Moreover, several banks started our survey, but were unable to complete it because they had not pro-gressed far enough in their implemen-tation to answer the questions.

Survey responses indicate a differ-ence in choices among several dimen-sions. Scenario generation, the length and number of scenarios, differences in segmentation, and differences in PD, LGD, and EAD modeling choices will certainly drive variation in banks’ CECL calculations.

Global Credit Data’s earlier study of IFRS 9 implementation highlights the benefit of moving into parallel pro-duction mode earlier. Once systems

SONER TUNAY is head of quantitative analytics, finance, and risk services at Accenture Consulting. He can be reached at soner.tunay@accenture.com.

RICHARD CRECEL is executive director of Global Credit Data, a global consortium headquartered in The Netherlands. He can be reached at richard.crecel@globalcreditdata.org.

DANIELA THAKKAR is methodology and membership executive at Global Credit Data. She can be reached at Daniela.Thakkar@globalcreditdata.org.

ONCE CECL IS

IMPLEMENTED,

THE WORK

CANNOT STOP.