Moody’s Analytics Webinar The Value of Granular Risk Rating Models for CECL November 15, 2016
Moody’s Analytics WebinarThe Value of Granular Risk Rating Models for CECL
November 15, 2016
2The Value of Granular Risk Rating Models for CECL
2
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tools for credit risk measurement and management
3The Value of Granular Risk Rating Models for CECL
Today’s Speakers
Senior Director | Enterprise Risk Solutions
Chris Henkel leads the risk measurement advisory team throughout the Americas and is an experienced credit practitioner.
Having spent most of his career in commercial banking, Chris has a unique blend of business and academic experience
across the financial services industry - including expertise in commercial credit and financial analysis, portfolio management,
asset quality, loan loss reserve methodologies, stress testing, credit administration, process redesign, safety & soundness
examinations, and credit risk modeling.
He received his master’s and undergraduate degree from the University of Texas and graduated Valedictorian from the
Southwestern Graduate School of Banking at Southern Methodist University.
Team Lead | Impairment, Capital Planning, and Stress Testing
Anna Krayn is a Senior Director and Team Lead, responsible for solution structuring across Moody’s Analytics products and
services focusing on impairment, stress testing and capital planning solutions. Her clients include a variety of financial
services institutions, including those in the banking and insurance sectors across Americas. Prior to her current role, she was
with Enterprise Risk Solutions as engagement manager leading projects with financial institutions across Americas in loss
estimation, enhancements in internal risk rating capabilities and counterparty credit risk management.
Anna holds a B.S. and MBA from Stern School of Business at New York University.
Presenters
Chris Henkel
Moderator
Anna Krayn
Managing Director | Economic Research
Tony Hughes oversees the company's credit analysis consulting projects for global lending institutions. An expert applied
econometrician, Dr. Hughes manages the Moody’s CreditCycle and CreditForecast.com products. He has helped develop
approaches to stress testing and loss forecasting in retail, C&I, and commercial real estate portfolios. Lately he has introduced
a methodology for stress-testing a bank’s deposit book. Currently he is developing ways to streamline the economic scenario
building process and exploring ways to simulate economic paths more effectively.
A native Australian, Dr. Hughes was formerly the lead Asia-Pacific economist for Moody’s Analytics, before which he held
academic positions at the University of Adelaide, the University of New South Wales, and Vanderbilt University. He has been
published in leading statistics and economics journals as well as several major industry publications. He received his PhD in
econometrics from Monash University in Melbourne, Australia.Tony Hughes
4The Value of Granular Risk Rating Models for CECL
In June 2016, the FASB issued an Accounting Standards Update (ASU), commonly known as “CECL”
Who does it apply to?
» The new accounting standard applies to all banks, savings associations, credit unions, and financial institution holding
companies, regardless of asset size
» Entities holding financial assets and net investment in leases that are not accounted for at fair value through net income
» Includes: Loans, debt securities, trade receivables, net investments in leases, off-balance-sheet credit exposures,
reinsurance receivables, etc.
» The standard requires organizations to immediately record the full amount of credit losses that are expected in their financial
assets held at amortized cost
Topic 326: Financial Instruments – Credit Losses:
Measurement of Credit Losses on Financial Instruments
When does it go into effect?
» FY 2019 (after 12/15/19) for public business entities that are SEC filers, including interim periods within those fiscal years
» FY 2020 (after 12/15/20) for all other public business entities, including interim periods within those fiscal years
» FY 2021 (after 12/15/21) for all other entities, and interim periods within fiscal years beginning after December 15, 2021
» All entities may early adopt beginning after December 15, 2018, including interim periods within those fiscal years
5The Value of Granular Risk Rating Models for CECL
Bank loans are among the largest asset class to be affected by the new accounting standard update
C&I21%
CRE22%
SFR21%Credit Card
8%
Auto5%
Consumer -Other
8% Other15%
Loan Portfolio Composition including all
FDIC Insured institutions (total ~$9 trillion)
Source: FDIC:
6The Value of Granular Risk Rating Models for CECL
Collective (“Pool”) Evaluation
» Required for financial assets when similar
risk characteristic(s) exists
Individual Evaluation
» Required when a financial asset does not
share risk characteristics with its other
financial assets
The ASU requires entities to apply one of two approaches to evaluate expected credit losses
» Internal or external credit score
» Risk ratings or classification
» Financial asset type
» Collateral type
» Size
Examples of Shared Risk Characteristics
» Effective interest rate
» Term
» Geographical location
» Industry of the borrower
» Vintage
7The Value of Granular Risk Rating Models for CECL
The concept is really quite simple
Example of a CRE Portfolio
Historical Loss Rate 2.00%
Impact of Decline in RE Values 0.50%
Impact of Increase in Unemployment Rate 0.25%
Expected Loss Rate 2.75%
Portfolio Value (Amortized Cost Basis) $10,000,000
Allowance for Expected Credit Losses $275,000
8The Value of Granular Risk Rating Models for CECL
In the context of credit risk modeling, collective evaluation could be at varying levels of granularity
C&I Loan Portfolio
Region (Geography)
Sector
Size (Total Assets)
Credit Risk Models / Scorecards
Should we segment here?
…perhaps here?
…or here?
9The Value of Granular Risk Rating Models for CECL
The right answer…?
Is our primary objective accuracy in the ECL estimate?
…It depends
If a less granular model is more accurate, how can we use it for
portfolio management decisions?
Do we have the required data to run a more granular model?
How homogenous is the portfolio?
10The Value of Granular Risk Rating Models for CECL
Treating all C&I loans the same, for example, would ignore obvious differences in the level of risk across sectors
Percentage of loans with bank-assigned adverse regulatory ratings, by industry
Source: Moody’s Analytics
11The Value of Granular Risk Rating Models for CECL
Model Coefficients
(Sensitivity to
Macroeconomic Variables)
Credit Risk (e.g., PD)
Sector
Within C&I loan portfolios, Sector and Credit Risk are two factors with different sensitivities to economic variables
0.25% (Loan 1)
1.00% (Loan 2)
2.00% (Loan 3)
5.00% (Loan 4)
10.00% (Loan 5)
Sector: Trade (All Loans)
C&I Loan Example
Macroeconomic Variables:» Unemployment
» Equity Prices
» Interest Rates
» Credit Spreads
» …
12The Value of Granular Risk Rating Models for CECL
Granular models also allow you to capture the different sensitivities to current conditions and forecasts
0.05% 0.08% 0.10%0.20% 0.25% 0.26%0.40% 0.44% 0.40%
1.00%0.93%
0.76%
2.00%
1.64%
1.20%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
Q0 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8
ECL Forecast (S0: Baseline Scenario)
Loan 1 Loan 2 Loan 3 Loan 4 Loan 5
Sept. ‘16
Exp
ecte
d C
red
it L
oss (
PD
x L
GD
) Sept. ‘17 Sept. ‘18Loan Q4/Q1 Q8/Q1
Loan 1 1.54 2.06
Loan 2 1.27 1.32
Loan 3 1.09 1.01
Loan 4 0.93 0.76
Loan 5 0.82 0.60
Change In ECL
Case Study Assumptions: » C&I loan portfolio (five loan sample)
» Moody’s Analytics Baseline Scenario (S0)
» PD/LGD approach; LGD constant at 20%
» 1 year remaining to maturity
(10% x 20%)
13The Value of Granular Risk Rating Models for CECL
Case Study: Auto Loans
Source: Lexus.com
14The Value of Granular Risk Rating Models for CECL
Forecasting Used Car Prices
» Applications for loans (LGD) and leases (residual risk).
» Potential applications to CECL and loss provisioning.
» Portfolios are often heterogenous – Camrys, Hummers and Bugatis – so
granular forecasts are desired.
» Forecasts have other uses, for which granularity is also a desired
attribute.
15The Value of Granular Risk Rating Models for CECL
Form of the (Assumed) Champion Model
The model is actually made up of several sub-components, each of which
serves a different key function.
Model layers are all developed at the Make, Model, Year, Trim level or
below:
» A VIN level model captures relativity between different vehicles under
different underlying economic conditions.
– This model rank orders the cars. Tells us which car will be worth more (given
economic conditions) in relative terms
– Cars can “leap-frog” over other cars under different economic scenarios.
– For example, under baseline a 2009 BMW 328 with 78k miles may be worth
slightly more than a 2011 Kia Rio with 16k on the clock.
– These vehicles may then “flip” positions in a recession!
16The Value of Granular Risk Rating Models for CECL
Form of the Model (Cont.)
» A quantile model shows and allows us to project how the distribution of
vehicles changes over time.
– This model allows us to map individual relativities into the aggregate distribution
– We can consider forecasts for different vehicles with different levels of
condition.
– For two identical cars with the same trim and mileage, the 10th percentile
projection pertains to a poorly maintained car, the 90th percentile to a very well
maintained car.
» A fleet level, forecasting model that captures the effect of the economy
on the entire segment in question.
– This model “anchors” the more detailed projections, allowing the effect of the
economy to be correctly rendered.
– Importantly, this model allows prediction of future model years.
17The Value of Granular Risk Rating Models for CECL
Form of the (Assumed) Challenger Model
» This model just uses the VIN level model (Step 1) without the application
of the calibration methods used in the (assumed) champion.
18The Value of Granular Risk Rating Models for CECL
Autocycle 2.0 Backtesting Results
Model N R-Square ME MAE RMSE
In Sample Challenger 30,069,965 0.88 -0.004 0.0615 0.083
Champion 30,146,120 0.90 -0.001 0.055 0.075
Out of
Sample Challenger 6,417,497 0.86* -0.006 0.0608 0.0836
Champion 6,417,497 0.84* 0.034 0.066 0.089
Sources: NADA, Moody’s Analytics
19The Value of Granular Risk Rating Models for CECL
Autocycle 2.0 Backtesting Results
Model N R-Square ME MAE RMSE
In Sample Champion 30,069,965 0.88 -0.004 0.0615 0.083
Challenger 30,146,120 0.90 -0.001 0.055 0.075
Out of
Sample Champion 6,417,497 0.86* -0.006 0.0608 0.0836
Challenger 6,417,497 0.84* 0.034 0.066 0.089
Sources: NADA, Moody’s Analytics
20The Value of Granular Risk Rating Models for CECL
Lexus Loses Value During Deep Recession
10
14
18
22
26
30
2016 2017 2018 2019
Baseline S4
Lexus RX-350, forecasted sale price, ths $
Sources: NADA, Moody’s Analytics
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Moody’s CECL Councils: Collaborating with the Industry
Activities
» Discuss key implementation challenges
» Share best practices regarding implementation
timelines, governance structure, and modeling
methodologies
» Deep dives into current provision calculation practices
and gaps relative to CECL requirements
Three Groups to “Right” Size CECL Implementation
Community Banks Regional Banks Large Banks
High-Level Timelines
Q1 2017 TBD
Form
CouncilsMeeting #1 Other Meetings
Current point
Benefits for Participants
» Network with leading impairment accounting
practitioners
» Define specific impairment calculation methods for
different asset classes and different-sized institutions
» Help shape design of your and our loss estimation tools
If you would like to participate, please email us at [email protected]
23The Value of Granular Risk Rating Models for CECL
moodysanalytics.com
Contact Us
Chris Henkel
Senior Director, Enterprise Risk Solutions
Moody’s Analytics
Tony Hughes
Managing Director, Economic Research
Moody’s Analytics
Anna Krayn
Senior Director, Business Development
Moody’s Analytics
24The Value of Granular Risk Rating Models for CECL
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