Welcome to Today’s Web Seminar!

September 17, 2013

12:00 PM ET Sponsored By: Hosted By:

MODERATOR:

Michael Sisk is a New York-based journalist who has

covered business and the financial markets for 15

years, including stints as the investor editor at Red

Herring, editor-at-large at American Banker, and

contributing editor at Bank Technology News.

His articles have appeared in numerous publications,

including American Banker, Barron's, Crain's New

York Business, Inc., Institutional Investor, strategy +

business and Worth. Michael has co-written and

edited three books; the most recent was Merge

Ahead: Mastering the Five Enduring Trends of Artful

M&A (McGraw-Hill 2009).

PRESENTER: Thomas Day Senior Director, Risk Solutions

Moody's Analytics Thomas works to solve difficult stress-testing, capital planning, and risk

management problems across complex portfolios and product sets for

financial organizations worldwide. Day’s primary areas of focus include

CCAR/DFA stress testing, pre-provision net revenue (PPNR) calculations,

systems, and methodologies, advanced liquidity risk quantification and

reporting, capital planning, performance and balance sheet management.

As a former Board member and Vice-Chairman of the membership driven

Professional Risk Managers’ International Association (PRMIA), Day is a

recognized industry expert with over twenty-two years of increasingly

senior roles with multifaceted experience in financial risk management,

corporate governance, business development and leadership.

Stress Testing Webinar Series: Enterprise-wide Stress Testing

September 17, 2013

Presented by: Thomas Day, Senior Director - Moody’s Analytics

5

Agenda

1. About stress testing

2. Best practices for expected loss (EL) and pre-provision net revenue (PPNR) forecasting

3. Integrating stress testing into existing business architecture

4. Techniques to make it worthwhile

5. Next webinar: Macroeconomic Conditional Loss Forecasting – October 29, 2013

6. Question and answers

6

About Stress Testing 1

7

Starting Point Assumptions

» Loss estimation (i.e., asset models) is the first most important element of the

stress test:

– Estimates of losses, revenues and expenses must all be “synchronized” with the same economic

and market conditions. Estimates must be driven by a variable selection process that is consistent

with the FRB scenarios, but may be more or less broad and these variables may be different from

one asset-model to another, as well as for PPNR.

» Integration of loss estimates into PPNR modeling has been weak; however,

this integration is required in order to get a proper quarterly ALLL and “net

income” number.

» Stress-testing requires unprecedented coordination between heretofore

“siloed” risk and financial planning processes.

» Data, data-management, and risk and finance integration are key elements of

success or failure.

8

Stress-Testing and Capital Planning

» August 19, 2013, the FRS issued a report entitled, “Capital Planning at Large Bank

Holding Companies: Supervisory Expectations and Range of Current Practice.”

» While the requirements for smaller banks, those between $10 and $50 billion, are less

onerous (see FR Vol 78, No 150, 8/5/2013) for the initial submission (i.e., March 2014),

the underlying principles are important for all firms.

» One key lesson learned is that firms:

“…failed to adequately identify the potential exposures and risks stemming from

their firm-wide activities” and that one of the key weaknesses was the inability of

firms to simulate risks exposures, across the enterprise, in a comprehensive and

integrated fashion.”

» If one looks at the specification of the stress-test and Capital Plan Rule with an objective

eye, it seems plain that a primary goal of the exercise is to spur a significant

improvement in the internal infrastructure, planning, risk, and forecasting capabilities of

financial organizations.

» Conclusion: A significant amount of work on data, analytics, and integrated risk,

finance, and management reporting is required in order to create a repeatable,

sustainable, and transparent stress-testing and capital planning process. What does

that work-flow entail?

9

Stylized Workflow (Steps) for DFAST/CCAR Exercise » Beyond meeting the “use-test”, the biggest challenge of the DFAST/CCAR exercise is

the ability to integrate, automate, and validate the entirety of the business process.

Data Pull as of Sept-30

Fill-in “Missing” Data with Proxy Data (inc. Tags)

Populate Required Fields for FRY-

14M/Q

Document Workflow, Version, and Audit the Data

Receive Scenarios Expand and

“Regionalize” Scenarios

Ensure Market Data is Consistent with

the Scenario Tailor Scenarios

Calculate Conditional ELs

Across All Assets

Determine Business Strategy in Each Scenario

Create Proper Assumption Input

for Integrated PPNR

Calculate Expected NII/NIM and

Balance Sheet for Each Scenario

Calculated Expected NIR and

NIE in Each Scenario

Determine Charge-Off and ALLL in Each Scenario

Assess and Apply Other Losses,

Including Ops Risk

Calculate Appropriate Pro-

Forma Regulatory Capital

Populate Required Regulatory

Reporting Forms

Reconcile Reports to FRY-9C and

Other Reporting

Assess and Validate Results

Apply Measures to Capital Plan

Data

Scenario

Design

Analytics

Reporting

10

Case Study: CCAR Integration Framework

Market Context Scenario Context

ALM System COA Behavioral

Assumptions

• Prepayment model(s)

and tables

• Valuation method(s)

• Amortization type(s)

• Other factors

• Basecase scenario

• Alternative scenarios

(for reference and

recon only)

Results tables

– Runoff

(Basecase)

Current position

Basecase

Runoff

Moody’s Analytics

RiskFoundationTM

Datamart

FP&A Step (client defined)

FP&A Moody’s Analytics

RiskFoundationTM

Datamart

New Business Plan

• Rolling 9-quarter • Credit dimension • Non-interest income and

expense • RWA allocation

Scenario Planning

Basecase Plan – 9Q forecast

RVM NIR/NIE

Scenarios

Credit

Regulatory Reporting

Pro-Forma

Balance Sheet

PPNR Calculator Recon

Pro-Forma

Income

Statement

FP&A Step

(client defined)

Only volume, rate,

and maturity

New Business Plan

Results tables

– New bus.

(Basecase)

New Business

Basecase Runoff

1. Volume

2. Price

3. Maturity

11

Best Practices for EL and PPNR Forecasting 2

12

Requirements of an Effective Process

» Expected loss (EL) estimates must be integrated into the “forecast”. Questions that

arise:

– Should we utilize a “top-down” or a “bottom-up” approach? Does it matter by asset-class?

– Regardless of method, how do loss estimates ingrate into existing processes?

– Who “owns” the loss calculations?

» Pre-provision net revenue (PPNR) requires the integration of credit “and” business

planning into the pro-forma forecast. Question that arise:

– How do we estimate “conditional” new business volumes under stress? What is the correct

“volume” estimate? What is the “correct” credit conditioned “price” rate?

– How do we estimate the “credit quality” and “EL” of new business production under stress? How

does this relate to capital planning and pro-forma RWA calculations?

– How do we hit the right NPA levels and how do we create the right “drag” on earnings from

increased NPA as well as increased charge-off and reserves?

13

Expected Loss: Question #1 – Top Down or Bottom-up?

» Consider the following:

– Primary v. Challenger models Need both! They should be “integrated.”

– Wholesale (i.e., idiosyncratic and heterogeneous) v. Retail (i.e., homogeneous)

» Challenges are addressed by:

– Using your own data, but supplementing the data where needed (with documented explanation)

– Focus on how the models will be used for business purposes, not simply the stressed metric

» FRB Principle 2 for Designing and Implementing a Stress Testing Framework Expects

Banks to Use Multiple Approaches to Stress Testing:

An effective stress testing framework employs multiple conceptually sound stress testing activities and approaches

All measures of risk, including stress tests, have an element of uncertainty due to assumptions, limitations, and other factors associated with using

past performance measures and forward-looking estimates. Banking organizations should, therefore, use multiple stress testing activities and

approaches …, and ensure that each is conceptually sound. Stress tests usually vary in design and complexity, including the number of factors

employed and the degree of stress applied. A banking organization should ensure that the complexity of any given test does not undermine its

integrity, usefulness, or clarity. In some cases, relatively simple tests can be very useful and informative.

Furthermore, almost all stress tests, including well-developed quantitative tests supported by high-quality data, employ a certain amount of expert

or business judgment, and the role and impact of such judgment should be clearly documented.

Interagency Guidance on Stress Testing for Banking Organizations

with Total Consolidated Assets of More Than $10Bn

SR Letter 12-7, May 14, 2012

“Companies may choose loss estimation processes from a range of

available methods, techniques, and levels of granularity.”

14

Expected Loss: Question #2 – How to Integrate?

» Consider the following:

– We need to forecast the balance sheet and income statement, but ALM systems are often

insufficient

– How do we “initialize” the ALM computation so we avoid MRAs?

» Solution:

– If an ALM system is being used, it must be properly “seeded” with consistent inputs from the credit,

finance, and risk groups, including defining the proper input factors for market conditions.

– Develop transition matrices by asset class – by quarter (bottom up).

– Matrices should be derived from the bank’s champion models that are used for loss estimation

and reporting.

– Define and agree on “conditional” new business credit spreads and volume estimates.

– Business strategy must include credit, regulatory capital, and proper reporting data tags.

– Connection between credit quality and prepayment is critical, but often missed.

– Must consider legal entity, cost centers, and other non-traditional dimensions!

While some banks partially integrated loss projections into net interest income projections, some

“…BHCs were unable to demonstrate coherence between NII projections and loss projections,

generally because one or both modeling approaches did not fully capture the behavioral

characteristics of the loan portfolio.”

Source: FRB’s August 19.2013 paper entitled, “Capital Planning at Large Bank Holding Companies:

Supervisory Expectations and Range of Current Practice”

15

Expected Loss: Question #3 – Who Owns the Loss Calcs?

» Should conditional expected loss calculations be owned by a central function, or more

integrated with front-office risk origination systems and processes?

» In order to pass the “use-test”, the loss estimates must be consistent with the manner in

which risk is originated and priced, actual and projected. Therefore, loss models used to

estimate risk and determine deal structure should be consistent with loss models for

CCAR/DFAST.

» Conclusion: Credit loss models must be integrated with front-office systems, and the

line-of-business managers should have a stake in validating/approving the conditional

loss estimates from models that are deployed.

» Line managers should determine stress-loss measures that may be important in deal

pricing, deal structuring, and “return-on” measures. Should support RAPM.

“Loan pricing should be consistent with both scenario conditions and competitive and

strategic factors, including projected changes to the size of the portfolio. Origination

assumptions should be the same for projecting loan balances, related loan fees,

origination costs, and loan losses.”

Source: FRB’s August 19.2013 paper entitled, “Capital Planning at Large Bank Holding Companies:

Supervisory Expectations and Range of Current Practice”

16

PPNR: Question #1 – Estimating New Business Volume?

Review:

» Most new business volume forecasts are:

– Not conditioned for credit and associated EL contribution(s).

– Non-conditional on the macro-economic scenario.

– Defined by SME input only.

– Usually tied to the budget and planning process and are, thus, aspirational.

Thesis:

» FP&A need more accurate methods to estimate conditional new business volumes.

Solution:

» Quantitatively estimated approaches for new business volumes and credit spreads that

are “agreed” between the planners, the LOBs, and the model output.

» Sensitivity analysis around the range of estimates to determine the impact on capital.

17

PPNR: Question #2 – New Business Under Stress?

Review:

» The manner in which a firm’s “origination strategy” will change is heavily influenced by

the expected economic conditions.

» For stress-testing, many banks assume business mix either 1) stays the same or 2)

changes in ways not necessarily properly tied to scenario design and evolution.

» What level of NB estimation is needed?

Thesis:

» Since we must conduct pro-forma RWA calculations, each asset class must possess a

new business credit distribution over time, and will generate EL. For example, new C&I

must show rating grade origination by industry, by geography, by quarter in order to

produce an accurate RWA calc.

» These assumptions must “seed” any PPNR calculation; new business EL estimates must

be reviewed and confirmed with credit, risk and finance staff (and ALLL impacts).

Solution:

» This level of new business planning is not a normal element of existing finance/FP&A

processes. Thus, a certain level or BPM re-engineering is normally needed, as well as

the technology to support this re-engineering.

18

PPNR: Question #3 – NPA and ALLL Influence on NI Calculation?

Review:

» As loans “transition” to non-accrual, they create a “drag” on net interest income.

» The FRB has identified the integration of PPNR and credit as a key weakness.

» The current sound practice is to use “conditional” transition matrices, by asset class.

» Charge-off forecasting should be driven by a similar process, and calibrated to existing

charge-off history/experience.

Thesis:

» As credit transition from performing to non-performing rating grades under various

scenarios, the impact on earnings should be direct and transparent.

Solution:

» Integrating conditional loss models with the PPNR calculation engine is required. For

forecasting NPA levels, a key linkage (input assumption) are conditional transition

matrices.

» Charge-offs are relatively easy once the ALLL modeling method is chosen and linked to

the EL estimation methodology, and calibrated to loss history.

19

Integrating Stress Testing Into Existing Business Architecture 3

20

Initial Steps

» Start a stress-testing and capital planning “office.”

» Emphasis on project planning and program management early in the process.

» Ensure that someone in the “office” has responsibility for determining current and “future

state” architecture.

» Understand that the evolution of the future state will require integration across numerous

“legacy siloed” risk and finance systems. Need Board and Senior Management

champions.

» Understand that the process is multi-step, not single step.

» Be proactive - don’t simply wait for an MRA and regulatory pressure (the writing is on the

wall.)

Many “…financial companies simply failed to adequately indentify the potential

exposures and risks stemming from their firm-wide activities..." due in part to "...a failure

of information technology and MIS, the often fractured nature of which made it difficult

for some companies to identify and aggregate exposures across the firm."

Source: FRB’s August 19.2013 paper entitled, “Capital Planning at Large Bank Holding Companies:

Supervisory Expectations and Range of Current Practice”

21

Credit Loss Models For All Asset Classes

» Commercial

Mortgages

» Income Producing

» Construction

» Fixed & Floating Rate

Commercial Real

Estate Commercial &

Industrial

» Public Companies

» Private Companies

Treasury & Asset

Management

» Non-Agency & Agency

RMBS

» ABS (credit cards, autos,

student loans, etc)

» CMBS & CLOs`

Retail Banking

» Residential Mortgages,

1st and 2nd Liens

» Auto Loans & Leases

» Credit Cards

» Equipment Leasing

Baseline and

Custom

Scenario 3:

Deeper Second Recession

Scenario 2:

Mild Second Recession

Scenario 4:

Depression Scenario

Scenario 1:

Quicker Recovery

CCAR:

Adverse

CCAR:

Baseline

Probability of Default | Loss Given Default | Exposure at Default Charge Offs

CCAR:

Severely Adverse

All Methodologies: Top-Down, Hybrid, and Bottom-up

22

Three Phases to Developing a Comprehensive DFAST/CCAR Platform

» Three-tier (and “N” tier) architecture is fundamental to good systems design.

» Modular, comprehensive platforms creates a “future proof” design that embraces internal

and 3rd party technologies.

Analytic Layer:

For DFAST/CCAR purposes, best practice is to begin with the analytical layer and supporting

models while working towards automation of data and reporting.

1

Data Layer:

For DFAST/CCAR purposes, and to target required data reporting, many banks must launch a

technology project. The goal is to target a single data platform to support risk, finance, credit, and

regulatory reporting and capital planning needs.

Reporting Layer:

The DFAST/CCAR reports are complex, and must be reconciled to FRY-9C, FFIEC 031/041,

Basel FFIEC 101, and other internal management reports. Automating this process must leverage

work performed from the Analytic Layer and the Data Layer.

2

3

23

End-to-End Software Solution Modular, Flexible and Comprehensive – Allowing for Straight Through Risk Processing

Spreading System Core Systems

(e.g. GL, Loan Accounting)

Datamart

DA

TA

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AT

AM

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Management

Reporting / Key

Performance

Indicators

Risk & Performance

Management

MA

NA

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Regulatory

Reporting

Risk Management

and ALM Systems

Outputs

Credit Models

(Wholesale & Retail)

Budgeting &

Planning Systems

Outputs BA

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» Our solution design accommodates comprehensive regulatory

reporting, internal risk and LOB reporting, plus dimension /

hierarchy management:

– Executive and board-level reporting

– Instantiation of the organization’s Risk Appetite Framework(s)

– Existing and expected liquidity risk reporting

– Drill-through and scenario dependent PPNR, balance sheet, new

business volume

– Comprehensive wholesale and retail credit portfolio reporting

» Moody’s is able to work within existing analytical layer to

coordinate, enhance and improve risk transparency

» By linking results from point solutions to the reporting layer

(RiskFoundation), Moody’s can empower the bank by

providing key linkage between input data and output results.

» RiskFoundation Datamart as an integrated risk and finance

data layer, is the foundation for stress testing

» RiskFoundation can be fully integrated with various data

sources, including enterprise data warehouses and core

banking systems

» This platform layer is used for Dodd-Frank-mandated reporting

(e.g. CCAR stress testing), Basel II and III

24

Techniques to Make it Worthwhile 4

25

A few recent examples:

Lower Cost of Credit Delivery

» As capital and liquidity costs increase, the need to run a bank more efficiently becomes paramount.

One way to achieve this is to automate and integrate disconnected, bulky, and older technologies

into a common framework that allows for “straight-through-risk-processing” across the accrual book.

Capital Arbitrage

» The regulatory capital required for a bank may be far more than the economic capital required of an

entity that the bank can lend to. Lending to this “new entity” may attract lower regulatory capital than

“direct” lending to an obligor. Understanding these nuances requires integrated calculations.

Return on Capital After Stress (ROCAS)

» Not all businesses or relationships are created equal. Some industries and businesses are more or

less correlated with business cycles, and existing portfolio dynamics. Thus, the need to include more

advanced analytics, and to emphasize the continued importance of economic capital, the need to

consider correlations, portfolio shape, and granularity remain important considerations.

Enhance data model to include “latitude, longitude and elevation” of collateral

» The recent FRB guidance placed an emphasis on tailoring scenarios to the firm’s business model, mix

of assets and liabilities, geographic footprint, portfolio characteristics, and revenue drivers. Tailoring

included linking to things like natural disaster, particular counterparty default(s), and regional

events/issues. This speaks to some of the “vision” that some banks have vis-à-vis the data model

and the desire to expand it for more customized, idiosyncratic scenario analysis

26

» Poor DFAST/CCAR data, analytics, and processes may lead to:

– An inability to pay dividends

– Prohibition from buying back stock (treasury stock repurchase programs)

– Leverage limitations

– Capital and liquidity surcharges

– Prohibition on growth – organic and M&A

– Fines

– Informal and formal actions (e.g., WA, MOU, C&D, Capital Directive)

» Failure can originate from poor processes, weak governance, or analytical, infrastructure

and reporting shortcomings.

» Most common causes of failure (to date) are related to data and infrastructure

weaknesses.

Poor DFAST/CCAR Data, Analytics or Processes Can Cause a Failed Stress Test – With Severe Consequences

“I was being asked to attest to this. It is worse than SOX 404. I

hired [CRO] to have him sign it. I’m not signing this thing.”

27

Next Webinar 5

28

Moody’s Analytics Stress Testing Webinar Series

Macroeconomic Conditional Loss Forecasting

October 29, 2013 at 12:00pm EDT

Join Thomas Day and other Moody’s Analytics experts for a webinar covering:

» The primary challenges confronting banks when forecasting macroeconomic conditional

losses.

» Best practices for forecasting macroeconomic conditional losses.

» Tools and techniques used by Moody’s Analytics to address the challenges and/or close

any gaps between best practices and current challenges.

Register at: http://www.cvent.com/d/h4qj0l/4W

29

Questions? 6

3

0 moodysanalytics.com

Thomas Day

Senior Director

Direct: 404.617.8718

Thomas.Day@moodys.com

7 World Trade Center at

250 Greenwich Street

New York, NY 10007

www.moodys.com

31

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moodys.com

Thomas Day

Senior Director

Direct: 404.617.8718

Thomas.Day@moodys.com

7 World Trade Center at

250 Greenwich Street

New York, NY 10007

www.moodys.com

34

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Q&A Session

Questions???

For More Information Contact:

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Senior Director, Risk Solutions

Moody's Analytics 7 World Trade Center at 250 Greenwich Street New York, NY 10007 Thomas.Day@moodys.com Direct: 404.617.8718