capital management and stress test

Capital Management and Stress Test

Capital management framework

3

CCAR Capital management framework

Capital buffers – Stress Test Requirements

Material Risk

Material Impact in Capital

Measures

CCAR stress test Economic CapitalRegulatory Capital B1

Buffer Post Stress

1. Credit Risk- Commercial- Consumer- Investment Issuer- Counterparty

HIGH

2. Market Risk - IRR - Trading - Other mkt. risks

MODERATE

3. Operational and Compliance Risk HIGH

4. Business Risk MODERATE PPNR partially

captured

5. Model Risk LOW Model Buffers

6. Liquidity Risk MODERATE Captured in

idiosyncratic scenario

Liquidity risk does not require economic capital

buffer

The capital planning process needs to identify all the material risks at the Company and show how they are captured in the measures used to assess capital adequacy.

4

CCAR process & stress test overview

The CCAR process is intended to ensure banks hold sufficient capital to continue operating under multiple stressed scenarios inclusive of any proposed capital action. CCAR warrants comprehensive capital-based stress testing .

5

Scenarios

Stress Testing / Modeling and Data Templates

(FRY 14-A, Q and Ms)

Capital Plan Document

Baseline Scenario

Internal Stress

Scenario(s)

Supervisory Stress

Scenario

Loss Estimation• Credit Risk (by portfolio) • Trading Risk

9-Quarters Pro Forma Income

Statement• Loss Estimates.• Revenue impact.• Cost Impact

9-Quarters Pro Forma Balance

Sheet• Retained

Earnings• OCI• DTA Goodwill

Base and Stress Capital

Ratios

Capital Adequacy Assessment

• AFS/HTM• Operational Risk

Integrated Stress Testing• Comprehensive systemic and

idiosyncratic scenarios• Enterprise-wide consolidated

stress testing • Dynamic over the course of the

next nine quarters• Linked to capital adequacy by

expecting Tier 1 common post-stress limits (e.g., 5% Tier 1 common)

• Incorporates financial forecast of revenue and expenses

• CCAR data templates and result disclosures

Single Shock, Risk-Specific Stress Requirements• Trading Market Risk• Credit and Counterparty• Liquidity/ Funding • Operational

Business strategyThe Strategic Plan is a key input to the capital management process. The bank needs to hold sufficient capital and liquidity to support the plan under multiple economic and bank scenarios.

To annual planning and budgeting process

Defines strategic objectives and plans

Recommends Risk Appetite

Develops and disseminates corporate strategic plan

Develops LOB and sub-LOB strategic plans and scenarios

Strategic Plan Process

Approves Risk Appetite

Reviews and Approves

Reviews

Reviews

LOB Leadership, Finance & Risk

Corporate Strategy

Executive Risk Committee

CEO/CFO

Board of Directors

Strategic Plan

State of the world

Baseline or better

Poor economic conditions

Severe economic conditions

Min capital (and

liquidity)

Target capital (and

liquidity)

Capital Planning Process

To capital management process

5

Building Blocks for Stress Testing System

6

Tier 1 Ratios

Master Scenarios

Macro Economic ProjectionsGDP growthUnemploymentUSD/ EURInterest Rates....

Portfolio Specifics

Portfolio GrowthAsset QualityMigration...

Translation Models

P&LDeposit MarginsLending MarginsInvestment FeesCredit LossesOperational Expenses

Required CapitalOperationalMarketCredit

Available Capital

Strategic Plans

Risk Weighted Assets

PD = f(PD0 ,GDP,...) EAD = f(EAD0 ,PGF,...)

RWA = f(PD,LGD,EAD)

EL = f(PD0 ,LGD, EAD)

Scenario generation

Scenario Generation Activities Scenario Generation Output

1. Detailed Systemic Scenario Description

- Define systemic scenario and macroeconomic factors- Calibrate macroeconomic factors- Adjust scenario factors for regional considerations

2. Idiosyncratic Risk Considerations

- Leverage risks identified in Risk Assessment- Determine high-level impact for each idiosyncratic event- Define likelihood for each idiosyncratic event

3. Scenario Assessment, Review and Finalization

- Review systemic scenario(s) and company-specific events- Assign/discuss likelihood for each scenario option- Select scenario(s) and review impact

Scenario “N” (if applicable)

BHC Baseline (Budget)

BHC Stress

Scenario Description

Macro Factors

Key areas of Impact

Multiple scenarios should be developed which are relevant to the Company’s risk profile and incorporate simultaneous firm-specific and market-wide macroeconomic events.

7

Scenario development — Provisions for Loan and Lease Losses Loan losses are projected using product-specific models utilizing historical and expected relationships between credit performance and relevant macroeconomic variables.

9

Mortgage Loan Products

Domestic Mortgages

Commercial & Industrial and Commercial Real Estate

Credit Cards Other Consumer

Other Loans

Loan Types Includes first and junior liens; closed-end and revolving

Includes Commercial & Industrial loans to obligors globally and domestic Commercial Real Estate loans

Includes bank and charge cards both domestically and internationally

Includes personal loans, student loans, auto loans, and other consumer loans

Includes international real estate loans and a variety of non-retail loans

Key Modeling Inputs

Home Price Index (HPI)

Interest rates Unemployment

rate

Obligor and facility risk characteristics

Country (local GDP) Sensitivity to global

trade flows

Vintage Credit score Country Unemployment

rate

Vintage Credit score Country Unemployment

rate

Local GDP HPI Interest rates Unemployment

rate

Business Activities

Domestic residential mortgage portfolios (RESI), the Private Bank, and Bank Holdings in RESI

Corporate and commercial loan, commercial real estate, commercial industrial loans, exposures in Securities & Banking (S&B), Transaction Services, and Bank Holdings

Consumer and corporate credit card lending globally

Domestic credit cards in Bank’s Branded and Retail Services segments

Domestic and global operations

International residential real estate

International commercial real estate and other loans in S&B, Transaction Services, Bank Holdings

FR Y-14 A schedules will require to develop capabilities around loss forecasting—Retail portfolios

Historical Charge-offs DelinquencyEconometric Component

Based Models PD/LGD-Based Models

Model Characteristics

• Simple average of historical net charge-offs or Markov-based loss rates

• Might include lag to address dampening from growth or trend functions to capture recent experience

• Delinquency-based vintage• Ratio-based roll-rate• Unit based to control for size

and/or separate severity

• Entry rate reflecting FICO refresh, behavior score and/or other characteristics such as MTM LTV

• Regression or transition probabilities subsequent to entry

• Separate LGD reflecting current market conditions

• Basel PD/LGD/EAD expected loss approach

• Segmentation based on LOB/product type, rating, FICO Score and/or collateral

Considerations

• Significant lag• No explicit link to root cause

other than in segmentation

• Somewhat lagging since still based on delinquency

• No explicit consideration of certain characteristics such as appreciation/ equity except in segmentation

• Seasoning adjustments and segmentation can increase complexity

• Segmentation inherent and at the loan level

• Calibration in periods of change

• Transparency/flexibility

• PD/LGD typical for commercial but not always well linked to business levers and loss forecasting for consumer

• GAAP consistency (economic vs. accounting)

• Transparency and model stability

LeadingLaggingModels currently utilized by banks vary in level of sophistication, but regulatory expectations are moving towards increasing levels of segmentation and transparency.

10

LeadingLagging

FR Y-14 A schedules will require to develop capabilities around loss forecasting—wholesale portfolios

Top Down Loss Model Simple Delinquency / Rating Based Portfolio Models

More Sophisticated Flow or Transition Models

Loan Level Default and Severity Models

Model Characteristics Model Characteristics Model Characteristics Model Characteristics

• Simple regression of charge-off rate (gross or net) to macroeconomic variables

• Might include lag functions• May be augmented by, or

benchmarked to, call report peer data

• Rating based PD banding• Macroeconomic regression of

PD cycle adjustments by rating band

• Separate severity assumption or model(s)

• May be augmented with vendor data

• Dynamic full risk rating transition matrix

• Various options for regressing / estimating rates as a function of macroeconomic inputs

• Separate severity assumption or model(s)

• Can be augmented with vendor data

• Predict default probability and/or loss severity as a function of loan level characteristic data and macroeconomic inputs

• Often leverages vendor models calibrated to pooled data sets

Considerations Considerations Considerations Considerations

• Longer time series available• Useful benchmark model• Limited segmentation• Implicit but not explicit

reflection of the portfolio condition at the forecast start date (e.g., delinquency pipeline)

• Lags many CCAR banks• Within range of broader

current practices of CCAR banks

• Relatively robust and transparent

• Only marginally more data intensive than top down loss model

• Separates frequency and severity

• Implicit, not explicit transition modeling (requires additional transformation to quarterly defaults)

• Limited vendor-based data specific to CRE

• Explicit modeling of defaults and timing of loss

• More consistent with leading industry approach for commercial

• Much more data intensive (loan level vs. summary level)

• More complex modeling concepts

• May sacrifice some transparency / flexibility

• Limited vendor-based data specific to CRE

• Incorporates loan specific drivers

• Increased segmentation inherent in models

• Much more data intensive – very few companies with sufficient internal data to develop / validate

• Much more complex modeling concepts

• Vendor models gaining more traction; initial and on-going licensing costs, however and some lack of transparency

LeadingLagging

Vendor Solutions available for Credit loss forecasting

LeadingLagging

The following is a representative, but not necessarily comprehensive list of vendor solutions and data sets that can be used to augment the credit modeling

12

Economic Data Portfolio & Loan Level Credit Data Pooled Default and Recovery Data

Representative Vendors / Data Model Characteristics Model Characteristics

• Moody’s Analytics, CreditCycle & Economy.Com (Moody’s specific scenarios and Fed scenarios)

• FHLB regional economic reports (historical)• FRB/FFIEC– Macro Economic Data and Consumer

Macro Performance Data• Credit Bureaus – Consumer Credit Data• NAR – Residential Mortgage Macro Performance Data• S&P Case-Shiller Home Price Indices• S&P Credit Models & Capital Stress Test services• CoreLogic • Argus Information Services & Predictive Analytics• Oracle OFSAA• Axiom

• SNL Call Report Data (e.g., balances, 30-89, 90+, charge-off, recovery data for major product segments)

• RMBS, CMBS Securitization data• Delinquency and flow rates, defaults,

write-offs and charge-offs, recoveries and net-losses, prepayments

• Scoring metrics• Loan to value, debt-to-income ratios,

credit limits and usage• Application volume, marketing

activity, collection treatments• ADCO & Intex

• Moody’s DRD (corporate default and recovery data)

• Moody’s CRD (private firm financial and EDF data)

• Moody’s LGD data (recovery database)

Considerations Considerations Considerations

• Moody’s develops full “economies” under both proprietary scenarios (S1-S5) and Fed scenarios

• Useful for utilizing regional inputs or deriving derivative indices from the limited set of variables forecast by the Fed

• Useful for augmenting internal data when developing top down loss models, or serving as a benchmark to more sophisticated models (i.e., sanity check)

• Useful for augmenting internal rating and recovery data in developing proprietary (internal) rating index or transition matrix based methods

Scenario development — Trading and Counterparty Losses Trading and counterparty losses represent losses on Bank’s trading portfolios, CVA, and other mark-to-market assets, inclusive of default losses.

.

8

Integrated Risk and Capital decision reporting

12

Key Risk Indicators Current Prior Trendvs. Tolerance /

TargetScore Benchmarking

Credit

Mortgages

HELOC

Auto Loans

Credit Cards

Other Consumer

C&I

CRE

Market

Opera-tional

Risk BASELINE STRESSED

S 1 S 2 S 3 S 4 S 5 Benchmarking Key Risk IndicatorsBaseline Stressed

Current Prior Trendvs.

Tolerance/TargetScore Benchmarking S1 S2 S3 S4 S5 Benchmarking

Capital reporting should include baseline and stressed views of KRIs.

PPNR forecasting modeling framework

Business Plan - PPNR Forecast

Business Plan - PPNR Forecast

Business Plan - PPNR Forecast Macroeconomic Scenarios

• Baseline• Fed Adverse• Fed Severely Adverse• BHC Scenarios (Systemic and

Idiosyncratic)

PPNR Model

Estimated Regression Coefficients

PPNR Drivers

Net Interest Income

Projected Balances

Projected Yields

Non-interest Income

Non-interest Expenses

+

-

Compensation

Op Risk Events

Put‐back Losses

OREO Expenses

Changes in MSR

Income

Other Expenses

x

Fee & Commissions

Securitization & Gain on

Sale…

Servicing Revenue

Segments

Baseline Scenario

Scenario 1

Scenario 2

Balances Originations

# of Accounts

# of Loans

Assets

• Residential Mortgages• HELOCs• C&I Loans• Small Business• CRE Loans• Credit Cards• Other Consumer (Auto,

Student, etc.)• Other Loans & Leases• Interest-bearing Securities• Trading Assets• Deposits with Other Banks

Liabilities

• Customer Deposits• Fed Funds, Repos, Other

Short-term Borrowing• Trading Liabilities• TruPS• Long-term Debt• Other

# of Deposits …

Modeling Components

• Alternative Variables

• Simple vs. Multiple

• Transformations

• Autoregression/Lags

• Data Disaggregation

• Data Augmentation

• Residual Analysis

• Sensitivity Analysis

13

Modeling aspect Fed ModelIndustry Practice

(less complex)Industry Practice(more complex)

Model Type Multiple autoregressive

models

Single regressions Lagged variables Moving averages used where

regressions had insufficient fit

Multiple regressions 2-3 drivers for each regression Dummy variables to adjust for

seasonality Moving averages used where

regressions had insufficient fit

Considerations for Granularity

BHC business model Ability to accurately model

small components of revenue

Data availability (included available balance and volume forecasts)

Market environment, competitive landscape, and resources available to lines of business

Granularity of Components

17 components of PPNR:o Interest income (5)o Interest expense (3)o Non-interest non-trading

income (5)o Non-interest expense (3)o Trading revenue (1)

~10 regressions with sufficient fit Non-interest income and expense

line items projected

+100 regressions with sufficient fit Components of balances modeled in

addition to line items

Macroeconomic Variables

Interest Rates GDP Equity Markets and

Volatility

Direct forecasts based on internally provided balances and volumes

GDP growth Interest rates Equity markets Commodities

Model Ownership N/A

Input from line of business on economic drivers

Line of business has no review of output after model creation

Line of business CFOs own economic drivers and model results

Line of business determines whether identified correlations are non-spurious

Documentation and Validation

N/A Documentation and validation consistent with SR 11-7 model risk management

framework

There are a range of industry practices related to PPNR modeling capabilities across US institutions. PPNR modeling is one of the most challenging modeling areas and has been the source of Matters Requiring Attention (“MRAs”) for many banks in the 2012 and 2013 CCAR cycles.

PPNR industry modeling practices

11

Loss forecasting — Operational risk modeling

2.1. Regression Analysis of Frequency and Severity using internal loss data

• Regression techniques

• Lagging

2.2. Add external data when necessary

2.3. Add scenario analysis to calculated idiosyncratic add-on

Economic Indicators Dataset

LOB / Risk Type

Retail Bankin

g

Commercial

Banking

Clearing

Retail Brokerag

e

Private Bankin

g

Business Disruption / IT

Clients / Business Practice

Damage to Physical Assets

HR and Workplace Safety

Execution and Process

External Fraud

Internal Fraud

# Economic Factors 4Q’11 1Q’12 2Q’12 3Q’12 4Q’12 1Q’12 2Q’13 3Q’13 4Q’13

1 Real GDP change (% YoY)

2 Unemployment rate

3 Inflation (%)

4 Personal savings rate

5 House price index

6 Consumer debt to income ratio

7 Personal bankruptcy filing

8 Business bankruptcy filing

9 Prime interest rate

10 3-Month Libor

11 10-Year Treasury Note

12 Vehicle Sales (millions)

13 S&P 500 Index (end of period)

14 National Consumer loan growth

15 National C&I loan growth

AMA Inputs

BU 1

BU 2

BU 3...

LT 1 LT 2 LT 3 …

Internal Loss

Database

External Loss

Database

Scenarios Loss

Database

AMA Calculation Engine

Frequency distribution

Severity distribution

Aggregate Loss Distribution

Scenario Design

Loss Forecasting

Capital Impact & Validation• Systemic

• Idiosyncratic• Supervisory

• EL • RWA

• EC• RC

• Risk type / LOB cell

• Lagging

Stress Testing Output

Scenario 1 2010 Amount in $MilBHC Baseline Q4 2010 Q1 2011 Q2 2011 Q3 2011 Q4 2011 Q1 2012 Q2 2012 Q3 2012 Q4 2012

Projected Operational Risk Losses

Scenario 2 2010 Amount in $MilBHC Stress Q4 2010 Q1 2011 Q2 2011 Q3 2011 Q4 2011 Q1 2012 Q2 2012 Q3 2012 Q4 2012


Scenario 3 2010 Amount in $MilSupervisory Stress Q4 2010 Q1 2011 Q2 2011 Q3 2011 Q4 2011 Q1 2012 Q2 2012 Q3 2012 Q4 2012


2011 Amount in $Mil 2012Amount in $Mil

2011 Amount in $Mil 2012Amount in $Mil

2011 Amount in $Mil 2012 Amount in $Mil

• Systemic• Idiosyncratic• Supervisory

LOB / Risk Type

Retail Banking

Commercial Banking

Clearing Retail Brokerage

Private Banking

Business Disruption / IT Clients/ Business PracticeDamage to Physical AssetsHR and Workplace SafetyExecution and ProcessExternal FraudInternal Fraud

• LOB• Risk Type

Stress Testing Process

16

Pro-forma capital ratios

B/S and P&L

Forecast

Loss / PPNR

Forecast

Scenario and

Financial Data

The completion of FR Y-14 reports represents a significant challenge for Mizuho given the breadth and depth of areas covered by the schedules

FR Y-14M*

• Credit card data collection schedule (domestic)

• First lien closed-end 1-4 family residential loan schedule

• Home equity loan and home equity line of credit schedule

• Address matching loan level data collection

FR Y-14Q

• Securities risk• Retail risk*• PPNR• Wholesale risk• Trading risk• Basel III/Dodd-Frank• Regulatory capital

instruments• Fair value option/Held

for sale• Mortgage servicing

rights• Operational risk • Supplemental schedules

FR Y-14A

• Summary schedules for each scenario

– Income statements, balance sheet, and equity /Capital statements; Retail, Wholesale, Loans, Securities; Trading; Counterparty Credit Risk; Operational risk; and PPNR

• Macro scenario schedule

• Basel III and Dodd Frank schedule

• Regulatory capital instruments

• Counterparty credit risk

15

*Not applicable to Mizuho’s US operations as these schedules are focused on retail exposure information

• Large number of data providers

• Numerous data sources

• Increased data granularity

• Aggregation of data across main platforms

• Complex accountability framework

• Change management challenge due to constantly changing requirements

• Diverse skill set required

• Y-14A

• Y-14 Semi-Annual

• Y-14Q

Key Challenges

The completion of FR Y-14 and other material regulatory filings is supported by over 3,000 data attributes across 16 categories

16

Below is the approximate count of total data attributes, by different product type, used to build a data platform with the capabilities to address external regulatory reports and internal management reports/analytics

# Type of Product Approximate # of data attributes

10 Repo 175+

11 Equities 50+

12 Forex 20+

Mitigants

13 Guarantees 175+

14 Credit derivatives 75+

15 Collaterals 50+

Financial Data

16 GL data 25+

# Type of Product Approximate # of data attributes

1 Loan contracts 450+

2 Investments 350+

3 Overdraft accounts 325+

4 Options 300+

5 Swaps 300+

6 Futures 250+

7 Money market contracts 200+

8 Bills 200+

9 Letters of Credit 175+

Stress test aggregation solution functionality overview

The stress test platform requires a consolidated baseline forecast with aggregated incremental stress impacts, determined at each business unit/portfolio and at the aggregate enterprise level, to produce a set of pro forma stressed financials.

Baseline Income Statement

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)

Revenue:1 Fee revenue

Net interest revenue:2 Interest revenue3 Interest expense4 Net interest revenue - - - - - - - - 5 Total revenue - - - - - - - -

6 Operating expenses

7 Pre-provision net revenue - - - - - - - -

Losses:

8 Loan lossesInvestment portfolio (OTTI):

9 AFS10 HTM11 Total OTTI - - - - - - - - 12 Trading losses13 Counterparty losses14 Operational/fiduciary15 Off-balance sheet16 Total losses - - - - - - - -

17 Taxes

18 Extraordinary items, net of tax

19 Net income - - - - - - - -

2011 2012

Baseline Balance Sheet

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)

Assets:1 Cash and due from banks2 Interest-bearing deposits with banks

3 Securities purchased under resale agreements4 Trading account assets5 AFS investment securities6 HTM investment securities7 Loans and leases8 Goodwill and other intangible assets9 Other assets

10 Total assets - - - - - - - -

Liabilities:

11 Deposits12 Securities sold under repurchase agreements13 Federal funds purchased14 Short-term borrowings15 Accrued expenses and other liabilities16 Long-term debt17 Total liabilities - - - - - - - -

Shareholders' Equity:18 Preferred stock + surplus

19 Common stock + surplus20 Retained earnings21 Accumulated other comprehensive income22 Treasury stock23 Total shareholders' equity - - - - - - - -

24 Total liabilities and shareholders' equity - - - - - - - -

Off-balance sheet:25 Commitments and contingencies

2011 2012

Baseline

Summary Income Statement - Scenario 1

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)

Revenue:1 Fee revenue - - - - - - - -

Net interest revenue:2 Interest revenue - - - - - - - - 3 Interest expense - - - - - - - - 4 Net interest revenue - - - - - - - - 5 Total revenue - - - - - - - -

6 Operating expenses - - - - - - - -


Losses:

8 Loan losses - - - - - - - - Investment portfolio (OTTI):

9 AFS - - - - - - - - 10 HTM - - - - - - - - 11 Total OTTI - - - - - - - - 12 Trading losses - - - - - - - - 13 Counterparty losses - - - - - - - - 14 Operational/fiduciary - - - - - - - - 15 Off-balance sheet - - - - - - - - 16 Total losses - - - - - - - -

17 Taxes - - - - - - - -

18 Extraordinary items, net of tax - - - - - - - -

19 Net income - - - - - - - -

2011 2012

Summary Balance Sheet - Scenario 1

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)

Assets:1 Cash and due from banks - - - - - - - - 2 Interest-bearing deposits with banks - - - - - - - -

3 Securities purchased under resale agreements - - - - - - - - 4 Trading account assets - - - - - - - - 5 AFS investment securities - - - - - - - - 6 HTM investment securities - - - - - - - - 7 Loans and leases - - - - - - - - 8 Goodwill and other intangible assets - - - - - - - - 9 Other assets - - - - - - - -

10 Total assets - - - - - - - -

Liabilities:

11 Deposits - - - - - - - - 12 Securities sold under repurchase agreements - - - - - - - - 13 Federal funds purchased - - - - - - - - 14 Short-term borrowings - - - - - - - - 15 Accrued expenses and other liabilities - - - - - - - - 16 Long-term debt - - - - - - - - 17 Total liabilities - - - - - - - -

Shareholders' Equity:18 Preferred stock + surplus - - - - - - - -

19 Common stock + surplus - - - - - - - - 20 Retained earnings - - - - - - - - 21 Accumulated other comprehensive income - - - - - - - - 22 Treasury stock - - - - - - - - 23 Total shareholders' equity - - - - - - - -


Off-balance sheet:25 Commitments and contingencies - - - - - - - -

2011 2012

Stressed Pro Forma

BU 1 Stress Losses - Scenario 1

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)





Losses:8 Loan losses

Investment portfolio (OTTI):9 AFS10 HTM11 Total OTTI - - - - - - - - 12 Trading losses13 Counterparty losses14 Operational/fiduciary15 Off-balance sheet16 Total losses - - - - - - - -

17 Taxes


19 Net income - - - - - - - -

2011 2012Source Reports

BU 1 Stress Losses - Scenario 1Source Reports

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)

Assets:1 Cash and due from banks2 Interest-bearing deposits with banks3 Securities purchased under resale agreements4 Trading account assets5 AFS investment securities6 HTM investment securities7 Loans and leases8 Goodwill and other intangible assets9 Other assets

10 Total assets - - - - - - - -

Liabilities:11 Deposits12 Securities sold under repurchase agreements13 Federal funds purchased14 Short-term borrowings15 Accrued expenses and other liabilities16 Long-term debt17 Total liabilities - - - - - - - -

Shareholders' Equity:18 Preferred stock + surplus19 Common stock + surplus20 Retained earnings21 Accumulated other comprehensive income22 Treasury stock23 Total shareholders' equity - - - - - - - -



2011 2012

Corporate


Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)







17 Taxes


19 Net income - - - - - - - -



Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)


10 Total assets - - - - - - - -





2011 2012

Business Unit 4


Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)







17 Taxes


19 Net income - - - - - - - -



Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)


10 Total assets - - - - - - - -





2011 2012

Business Unit 3


Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)







17 Taxes


19 Net income - - - - - - - -



Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)


10 Total assets - - - - - - - -





2011 2012

Business Unit 2


Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)







17 Taxes


19 Net income - - - - - - - -



Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

($MM)


10 Total assets - - - - - - - -





2011 2012

Business Unit 1

Consolidated Baseline Income Statement and

Balance Sheet Forecast

Incremental Stress Impact Estimates Pro Forma Stressed

Income Statement and Balance Sheet

2016

Current Actions Taken at CCAR Institutions

CCAR institutions continue to enhance capital management process and related validation processes

18

At RiskInefficient Unstable Stress

Testing Process

FunctionalIssue Awareness with Manual Processes and

Control

SustainableReliable,

Controlled Function

AspirationalHigh Performing Enterprise-

wide Business enabler

Strategy

• Siloed Stress Testing Approach• Ad-hoc planning for

consolidated stress testing exercises

• Coordinated business and corporate stress testing approach

• Integrated business and corporate approach

• Comprehensive coverage and alignment of stress testing efforts

• IT enabled strategy focused on creating value for business and corporate function

• Integrated framework fully aligned to Basel, ICAAP, contingency, recovery and resolution planning

Governance

• Structures remain inconsistent and are based on “who can do it” rather than “who should do it”

• Skills and capabilities requirements loosely defined

• Consistent structures exist with clear functional boundaries between risk, finance and LOB

• Some functions are centralized

• Skills and capability requirements are well defined and pursued

• Stress testing committee and working group

• Centralized and organized stress testing unit to increase accountability and drive expertise

• Stress testing fully aligned to strategic planning and performance evaluation

• Highly skilled resources focused on analysis vs. result production

• Leverage shared services to deliver routine, high volume transaction processing when necessary

Process

• Scenario Analysis, loss forecasting, aggregation and reporting processes are informally documented, not standard and disconnected

• Issues are partially known and managed reactively

• Standard policies and procedures are well documented and maintained

• Ad-hoc efforts to standardize and automate procedures

• Activities are performed manually and consume excessive resources

• Outsource of systemic scenario generation

• Processes are highly standardized and consistent across LOBs

• Linkage between LOB and corporate stress testing processes

• Key activities and controls are performed on a timely basis based on controlled cycle time and effective preventive controls that reduce errors

• End-to-end process approach to standardization & simplification

• Integrated loss and RWA forecasting

• Continuous process improvement and ongoing formalized documentation

Technology

• Multiple databases with no common structure or reliable interfaces

• Heavy reliance on ad-hoc reporting to provide information

• Significant data manipulation to support stress testing needs

• Streamlined inventory of risk and finance applications participate in stress testing process

• Data validation controls in place to ensure completeness and reconciled information to GL/disclosures

• Automation of balance sheet aggregation and reporting steps in stress testing process

• Model and data quality governance and controls in place

• Financial and risk applications (scenarios, loss forecasting, balance sheet aggregation and fully integrated into a common stress testing platform

• Ability to expand functionality and link other areas (RWA, ICAAP, liquidity risk, ALM, etc.)

• Flexible functionality (e.g., what if and sensitivity analysis)

Stabilization Sustainability

The identification, discovery and preparation of data to complete the FR Y-14 reports should leverage both top-down and bottom-up approaches

18

There are two methodologies to identify, discover and get data ready for the FR Y-14 reporting, top down approach and bottom up approach as shown in the diagram below.

Data gaps

Data sourcing solutions

Strategic enhancements

Tactical workarounds end-user computing

Understand the internal/ external

reporting requirements

Component evaluation & issue resolutions

FRY-14Data Elements

•Balance sheet,•Income statement • Risk reports,•Financial data

FRY-14Data Model

Data schema (grouping)

Group FRY-14 DataRequirements

Identify source system for the data elements

Data dictionary

Top Down

Bottom Up

Defining Reporting Requirements – Reporting & Data

Anticipating Reporting Requirements – Work Streams

Mapping Sourcing

Analysis

capital management and stress test

Documents