Moody’s Analytics Webinar

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

Independent provider of credit rating opinions

and related information for over 100 years

Models, data, software and research for

financial risk analysis and related

professional services

A unit of Moody’s Corporation, Moody’s Analytics provides risk solutions and thought leadership to the financial services industry

Leading global provider of credit rating opinions, insight and

tools for credit risk measurement and management


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


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


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:


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


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


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?


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?


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


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

» …


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%)


Case Study: Auto Loans

Source: Lexus.com


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.


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!


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.


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.


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


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



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 $


21

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]

mailto:[email protected]


moodysanalytics.com

Contact Us

Chris Henkel

Senior Director, Enterprise Risk Solutions

Moody’s Analytics

[email protected]

Tony Hughes

Managing Director, Economic Research

Moody’s Analytics

[email protected]

Anna Krayn

Senior Director, Business Development

Moody’s Analytics

[email protected]





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