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CECL’s Forward-Looking Requirements:
The Impact Could Be Substantial
Cristian deRitis PhD, Senior Director, Economics
Timothy Daly, Director, Sales Manager
Dr. Cristian deRitis
Senior Director, Consumer Credit Analytics
Cristian deRitis, PhD, is a Senior Director with Moody’s Analytics specializing in
consumer credit analysis, policy and U.S. economic conditions.
Timothy Daly
Director, Sales Manager of Economics and Consumer Credit Analytics
Tim Daly, is a Director with Moody’s Analytics specializing in helping clients meet their
economic and consumer credit needs.
Presenters
This
changes
everything.
Source: FASB
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 4
CECL in a
What’s it all about?
» The CECL standard will change how firms estimate their allowance for loan and lease losses
(ALLL).
» Addresses “too little too late” loss provisioning that occurred during the financial crisis.
» Replaces the current “incurred loss” standards–commonly known as FAS-5 and FAS-114.
» Applies to any entity issuing credit, including banks, credit unions and holding companies filing
under GAAP accounting standards.
» CECL will go into effect starting December 15, 2019, for public business entities that are U.S.
SEC filers.
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 5
» Under the incurred loss model, banks recognize losses when they reach a probable threshold
of loss.
» CECL removes the probable loss threshold and requires a lifetime credit loss allowance to
be established on day one of each exposure.
» Forecasting losses under CECL requires
- More granular data
- Enhanced credit models
- Forward-looking information or scenarios
Conceptual Differences
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 6
» Currently, losses are forecast over a loss emergence period.
» CECL is a lifetime loss estimate.
- Forecast losses over a reasonable and supportable horizon
- Extrapolate beyond this horizon using historical averages over the remaining life
» CECL standards are principles-based.
- Not prescriptive in how institutions address specific modeling challenges
- Flexibility to account for firms of different size and complexity
» Require increased transparency in assumptions and more disclosures to support the
allowance estimate.
» Selection of forecasts and assumptions will need quantitative support.
Biggest Change: Forecasting Losses
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 7
» CECL is more computationally intensive than the current method.
- Under the current standard, only a small subset of loans in the portfolio with a “high
probability of loss” are modeled
» Under CECL standard, we need to estimate and account for the potential losses from all
loans. CECL adds a probabilistic component.
» Given additional complexity, need efficiency gains to meet financial reporting deadlines:
- Better data management (centralized, scrubbed, QC checks)
- Enhanced execution performance (hardware, cloud)
- Models fit to purpose (optimize to produce required outputs)
- Specialization and outsourcing (buy vs. build trade-off)
Cost to Estimate ALLL Will Increase
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 8
“Allowances will double”
“Allowances will fall”
“You must build all of your own models”
“You can only forecast under one economic scenario”
“You must forecast under multiple economic scenarios”
“Auditors will demand more conservative estimates”
“CECL will force banks to only make short-term loans”
“CECL will cause all banks to be undercapitalized”
“CECL will push all lending to the shadow system”
“CECL will destroy the economy”
Dispelling Some CECL Myths
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 9
» Depends on a number of factors including
- Portfolio composition (longer-dated loans impacted more)
- Credit quality
- Geography
- Forward-looking forecast assumptions
- Stage of economic cycle
» As an exercise, we consider FDIC historical data on charge-offs and
allowances for all commercial banks
» Assume perfect foresight (that is, no model forecast error)
» Use residential mortgage vintage performance to calibrate lifetime loss
performance for CECL
» Just a thought exercise. Bank-specific results will vary!
How Will CECL Impact My Allowance?
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 10
Estimating the Impact: Default Rates Vary by Vintage Cohort5-yr cumulative net loss rate for first mortgage by origination qtr
Sources: CreditForecast.com, Moody’s Analytics
0
1
2
3
4
5
6
7
8
05 06 07 08 09 10 11 12
Avg=2.6%
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 11
0
50
100
150
200
250
300
05 06 07 08 09 10 11
By default yr
By origination yr
Poor quality and high
orig. volume in 2007
Charge-Offs Depend on Volume and PerformanceNet loan and lease charge-offs at commercial banks, $ bil
Sources: FDIC, Moody’s Analytics
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 12
0
50
100
150
200
250
05 06 07 08 09 10 11
Actual CECL estimate
Provision Expenses Front-Load With CECLProvision for loan and lease losses at commercial banks, $ bil
Sources: FDIC, Moody’s Analytics
Would provisions
have changed
behavior?
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 13
0
50
100
150
200
250
300
05 06 07 08 09 10 11
Actual CECL estimate
Allowance for loan and lease losses at commercial banks, $ bil
Sources: FDIC, Moody’s Analytics
ALLL should
better align with
credit quality
Allowance Balance (ALLL)
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 14
» Recognition of credit losses will
shift to an earlier point in the
credit cycle
» Current conditions will differ by
market (and type)
» The impact of conditions also
differ measurably
» Lack of data or lack of
quantitative support for
management estimates
Current Conditions
» Lifetime losses must be
recognized at origination, rather
than when default is deemed
likely
» Cumulative losses are non-
linear – how to measure?
» Prepayments are difficult to
measure, especially for
commercial portfolios
» Differences by loan types
Lifetime Losses
» Need to generate economic
scenarios
» Also, to appropriately link
scenarios to credit losses
» Ability to forecast beyond a
year or two into the future is a
challenge
» Unreliable forecasts will mean
greater volatility in loss
provisions
Supportable Forecasts
FASB’s 3 key considerations in estimating CECL
Biggest implementation challenge: Generating forward-looking scenarios
“The Biggest Change Ever” to Accounting
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 15
-5
-4
-3
-2
-1
0
1
2
3
4
5
15 16 17 18 19
BaselineS1S2S3S4S5S6
Real GDP, % change yr agoAvailable Scenarios
Standard-Driven
S1 Stronger Near-Term Rebound
BL Baseline Forecast
S2 Slower Near-Term Recovery
S3 Moderate Recession
S4 Protracted Slump
S5 Below-Trend Long-Term Growth
S6 Stagflation
S7 Next-Cycle Recession (U.S. Only)
S8 Low Oil Price
CS Constant Severity
CB Consensus Baseline (U.S. Only)
Compliance-Driven
FB Fed Baseline
FA Fed Adverse
FS Severely Adverse Scenario
BC Bank Specific Scenario
Manage Risk With Scenario AnalysisScenario service, monthly updates with narratives and probability weights
Source: Moody’s Analytics
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 16
Key Features
» Baseline forecast + eight alternative scenarios with probability weights
» Available for the U.S., all state and metro areas, as well as 60+ countries
» Coverage of more than 1,800 economic, financial and demographic variables
» Forecasts updated monthly, history updated in real-time, 30-year horizon
» Fully documented model methodology; scenario assumptions published monthly
» Back-testing, tracking and model validation reports available
Reasonable and supportable forecasts from Moody’s Analytics
Moody’s Analytics Scenarios
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 17
Based on sound, generally accepted economic and statistical theory
Incorporates inter-relationships and feedback effects among variables such
that a shock to one factor impacts all other factors over time
Provide information at varying levels of geographic aggregation to
capture local economic effects
Key considerations:
What Makes a Good Economic Forecast?
Our economic forecasting
model meets these criteria.
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 18
Approach used by use by Federal Reserve, IMF, Central Banks
Structural Forecast Model Methodology
Exchange rates
Investment
Wages and salaries
Po
pu
latio
n
Prices
GDP
Monetary policy rate
Imports
Government
Exports
Global GDP
Unemployment rate
Consumption
Labor force
Potential GDP
Banking sector
Import prices
10-yr yield
Global prices
Employment
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 19
Interrelationships between all key variables
Macro to Regional Linkages
Cost of Doing Business
Consumer Credit Quality
Output by Industry Employment by IndustryPopulation/Households
Labor Force/UnemploymentPersonal IncomeHousing
US MACRO MODEL
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 20
Use our collaborative forecasting platform
Want to Create Your Own Scenarios?
» Web-based application to develop your
own scenarios
» Uses Moody’s Analytics validated macro
models
» Forecast governance built into the
application core
– Audit trail of edits to forecast
assumptions
– Test edits in a sandbox environment
before committing them to the master
forecast
– Transparency of equations and
assumptions
» Collaborate with colleagues and/or
Moody’s Analytics on the same forecast
– Simultaneous read/write access
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 21
» No specific guidance – at least one forward look
– Could interpret as Baseline or Consensus scenario
» IFRS9 analogue to CECL
– Multiple, probability-weighted scenarios
» Measure loss under multiple scenarios
– Generate a distribution
» Given distribution of losses is skewed, multiple scenarios preferred
– “Average” economic forecast won’t generate the “average” level of losses
– Multiple scenarios approximate the distribution
Leverage percentile distributions using multiple scenarios
How Many Scenarios Do You Need?
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 22
3
4
5
6
7
8
9
10
11
12
13
0 12 24 36
Initial forecast
Realized
Update 1
Update 2
Unemployment rate, %
Sources: BLS, Moody’s Analytics
X-axis: mo from start of forecast
Single, Consensus Unemployment Forecasts
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 23
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 12 24 36
Consensus
Update 1
Update 2
Single Scenario: Loss Forecasts Volatile Under ConsensusLoss rate, %
X-axis: mo on book (age)
Sources: BLS, Moody’s Analytics
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 24
Multiple Scenarios: Probability Weighted Forecasts
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0 12 24 36 48 60
Baseline
S1: Optimistic
S3: Pessimistic
Prob Weighted
30% wgt
40% wgt30% wgt
Loss rate, %
X-axis: mo on book (age)
Sources: BLS, Moody’s Analytics
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 25
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0 12 24 36
Prob Weighted
Update 1
Update 2
Multiple Scenarios: Loss Forecast Updates Less VolatileLoss rate, %
Sources: BEA, Moody’s Analytics
X-axis: mo on book (age)
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 26
Recommendations
1. For the largest institutions:
» Multiple custom economic scenarios provide a wide range of estimates.
» Estimates less sensitive to decisions on forecast period and mean reversion.
» Multiple scenarios reduce volatility in quarter-to-quarter updates.
2. For midsize institutions:
» Standardized economic forecasts provide a reasonable solution.
» Multiple, risk-weighted scenarios provide a quantitative, defensible approach.
» Multiple scenarios reduce volatility in quarter-to-quarter updates.
3. For smaller institutions:
» A single scenario approach is reasonable.
» “Reasonable and supportable” forecast horizon at 2-3 years with gradual reversion..
» Run periodic sensitivity analysis to disclose potential volatility/risks to the forecast.
Different approaches for different situations
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 27
Moody’s Analytics CECL Solution Suite
moodysanalytics.com
United States
121 North Walnut Street
Suite 500
West Chester PA 19380
+1.610.235.5299
United Kingdom
One Canada Square
Canary Wharf
London E14 5FA
+44.20.7772.5454
Australia
Level 10
1 O'Connell Street
Sydney, NSW, 2000
Australia, +61.2.9270.8111
Prague
Washingtonova 17
110 00 Prague 1
Czech Republic
+420.22.422.2929
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 29
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CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 30
Selected Case Studies
INSTITUTION OVERVIEW
Large Multinational GSIB
(Asset Size = $386B)
Produced stressed BHC scenario based on counterparty default and three Fed scenariosthrough a network of ~1000 macroeconomic and global financial market factors.
Footprint: International, Market Risk, U.S. subnational specialty
CCAR Bank
(Asset Size = $180B)
Met with C suite to assess client’s risk profile including C&I concentrations and retail mix.Proposed three idiosyncratic scenarios for their CCAR submission (ex yield curve inverts,Fed tightens monetary policy too soon, nuclear reactor accident)
Footprint: U.S. and select States
Merchant Services
(Asset Size = $33B)
Partnered with client’s risk management team to build IFRS 9 compliant models for all oftheir portfolios and provided access to our probability-weighted scenarios for use in themodels. CECL modeling to start in 2018.
Footprint: U.S., select States and Metros, International
Captive Finance
(Asset Size = $180B)
Assisted with scenario and model development for IFRS 9 providing access to off-the-shelfscenarios and built IFRS 9 compliant model for auto loan portfolio.
Footprint: Canada, select Provinces and Metros in Canada
DFAST Bank
(Asset Size = $26B)
Partnering with client’s CECL working group and stress-test team to provide off-the-shelfscenarios highlighting key risks in CRE, C&I, and mortgages.
Footprint: U.S., select States and Metros in the Midwest
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 31
CECL Scope and Timeline
2017
Project Plan/
Governance
Structure
Mid-2017
Gap Analysis
Preliminary
Decisions
2019
Early Adoption?
2020
SEC Filers
Mid-2018
Models and
Methodology
2019
Parallel Run
2018
End-To-End
Solution
Implementation
» Scope: financial instruments measured at amortized cost basis
– Loans held for investment
– Debt securities held to maturity
– Net investment in leases
– Off balance sheet exposures (loan commitments, letters of credit)
– Other (trade and reinsurance receivables, off-balance-sheet exposures)
» AFS OTTI model is changed to Allowance model
» Measure expected credit losses over the life of financial asset based on:
– Past events, including historical experience
– Current conditions
– Reasonable and supportable forecasts
» New disclosure requirements: amortized cost by credit quality indicators and vintage
» Current processes can be leveraged, but there are a number of considerations
– Internal credit risk systems
– Incurred loss ALLL processes
– DFAST processes
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 32
Key Decisions for Computing CECL
• Which methods are acceptable?
• Can I leverage existing models?Data and
Methodology
• Contractual or behavioral life?
• Life of revolving account?Lifetime Definition
• What’s required? Best practice?
• What are options for retail credit?Model Backtesting and Benchmarking
• Which scenario is defensible?
• How many?Economic Scenarios
Source: Moody’s Analytics
CECL’s Forward-Looking Requirements: The Impact Could Be Substantial, Jan 2018 33
Industry Survey: What Is the Most Significant Challenge
Anticipated?Model-related issues consistently rank high
32%
27%
18%
11%
2%10%
February 2017
Data availability
ECL quantification
Scenario design
Qualitative overlay methodology
Performance (i.e. speed of execution)
Data and process governance
35%
18%
37%
10%
August 2017
Data availability
Scenario selection, design and support
Expected credit loss methodology
Process governance and controls