1

On24 Tech Tips

• Make sure your speakers are on• Hit F5 any time your console freezes• For a LIVE event you should be hearing music now• Use the “Ask a Question” feature to report issues• Webcast starts at the top of the hour

Presented by:

Charyn FaenzaVice President, Manager, Corporate Business Intelligence Systems, First National Bank

Tara Heusé SkinnerManager, Risk Research and Quantitative Solutions SAS Institute

May 19, 2015

GARP Webcast

Stress Testing ModelingFinding the ‘Fault Lines’ in Risk Management through Dynamic Stress Test Modeling

2

Charyn Faenza, Vice President, Manager, Corporate Business Intelligence Systems, First National Bank.

Ms. Faenza is Vice President and Manager of Corporate Business Intelligence Systems for First National Bank, the largest subsidiary of F.N.B. Corporation (NYSE: FNB). An accountant by training, she is passionate about not only understanding the technology, but the underlying business utility of the systems her team supports. In her role she is responsible for the architecture and development of F.N.B.’s corporate profitability, stress testing, and analytics platforms and oversees the data collection and governance functions to ensure high data quality, proper data storage and transfer, risk management and data compliance.

Throughout her tenure at F.N.B. her experience in data integration and governance has been leveraged in several cross functional projects where she has been engaged as a strategic consultant regarding the design of systems and processes in the Finance, Treasury and Credit areas of the Bank.

Ms. Faenza earned her bachelor’s degree in Accounting from Youngstown State University where she is currently serves on the Business Advisory Board of the Youngstown State University Lariccia School of Accounting and Finance.

Charyn Faenza, First National Bank

3

Tara Heusé Skinner, Manager, Risk Research and Quantitative Solutions, SAS Institute

Ms. Skinner is the Risk Solutions Manager for the Risk Research and Quantitative Solutions Division at SAS Institute, Inc. As a risk consultant, Ms. Skinner provides consultative guidance in SAS’ risk management solutions to North and South American banks, credit unions, and insurance companies, U.S. and Canadian Federal governmental agencies, and non-financial services companies.

An experienced regional bank Chief Risk Officer, Ms. Skinner created enterprise risk management divisions for two separate Top 50 U.S. Banks. She has spoken in various conferences globally on the fundamentals of enterprise risk management and its implementation challenges. Ms. Skinner co-authored a book for the American Bankers Association (ABA), The Bank Executive’s Guide to Enterprise Risk Management.

Her financial industry career spans over twenty years. Ms. Skinner, who holds an MBA from Louisiana State University, is a distinguished graduate of the ABA’s Stonier Graduate School of Banking; her Stonier Strategic Leadership thesis is published in the Harvard Business School library. She served on Stonier’s faculty and is a former member of its board of advisors. Ms. Skinner also served as Professeur of Risk Management at the Institut Supérieur Européen de Gestion for the MBA Program of the International School of Management, Paris, where she is also a Ph.D. candidate. Her doctoral thesis: Risk Culture and Its Influence on Firm Value and Financial Performance.

Tara Heusé Skinner, SAS Institute

4

DFAST(Dodd-Frank Act Stress Tests)

CCAR(Comprehensive Capital Analysis and Review)

“DFAST 10-50” $10 Billion to $50 Billion in

Assets One DFAST per year

“DFAST 50” $50+ Billion in Assets Two DFASTs per year

Fed uses a standardized set of capital action assumptions

For 31 (33) US Banks ($50+ Billion), the Fed: Uses a bank’s planned capital

actions Assesses whether a bank would be

capable of meeting supervisory expectations for minimum capital ratios

While the supervisory DFAST and CCAR quantitative assessment incorporate the same projections of pre-tax net income, the primary difference is the capital action assumptions that are combined with these projections to estimate a Large BHC’s post-stress capital levels and ratios..

Stress Testing Regimes in the U.S.

5

Model Implementation and Integration

The Needs for Today’s Stress Testing Regimes:• Improved Model Deployment• Model Inventory Management System• Results Exploration• Model Structures for Easy Utilization

6

Modular View of Stress Testing

Data Extraction,

Transformation and

Validation

Model Methodology, Specification

and Execution

Macro Scenario

Specification and

Enrichment

Aggregation and

Computation

7

Data Extraction, Transformation and Validation

Source Data Data Management Actions

GL Data

Transactions/ Positions

Collateral

External Market Data

Parameters

Data Integration

Data Quality

Reconciliation

Segmentation

Mapping

Supplement bank’s data with consortium and

proxy data

Data Extraction, Transformation and Validation

Model Methodology, Specification and

Execution

Macro Scenario Specification and

Enrichment

Aggregation and Computation

Pre-populate reports with preliminary data

Document

MODEL INPUT

8

Assets and Liabilities Tested Models

Model Methodology, Specification and Execution

Data Extraction, Transformation and

Validation

Model Methodology, Specification and

Execution

Macro Scenario Specification and

Enrichment

Aggregation and Computation

Credit Portfolios

Other (non-credit) Assets and Liabilities

CREC&I

Mortgage

ConsumerHELOC

REO

EquipmentBuildings

Swaps

Goodwill

Securities

Document

PD

LGD

EAD

ESTIMATION PROCESS

PPNR

Net Interest Income

(NII)

Noninterest Income

Noninterest Expense

Balance Sheet

Credit

Losses Provisions (ALLL) Reserves

9

SegmentationScenarios

Macro Scenario Specification and Enrichment

*All scenarios include 28 variables (16 domestic variables and 12 international variables)

Macro Scenarios*

BaselineAverage projections from

surveys of economic forecasters

AdverseModerate US recession (real GDP 1%; rate of unemployment = 9¼%)

Severely AdverseSevere US recession with unemployment peaking

at 11¼%

“Localized” Scenarios Industry

Geography

FICO Score Bands

Data Extraction, Transformation and

Validation

Model Methodology, Specification and

Execution

Macro Scenario Specification and

EnrichmentAggregation and

Computation

Business Segments

(FR Y-14A)

Retail/Small Business

Commercial Lending

Investment Banking

Merchant Banking/

Private Equity

Sales and Trading

Investment Management

Investment Services

Credit Cards

Mortgages/ Home Equity

Deposits

Advisory

Equity Cap Markets

Debt Cap Markets

Net Investmt Mark-to-MarketManagement

Fees

Treasury Services

Insurance Services

Retirement/ Corp Benefits

Document

10

Aggregation and Computation

Document

CAPITAL POSITION

Capital Ratios RWA Capital

Actual and Projected Capital Ratios – Adverse Scenario

Actual Stressed Capital Ratios CCAR 2015-16 Minimum30Sep2014 31Dec2016 Minimum

Tier 1 Common Capital Ratio 13.2% 13.2% 12.5% 5.0%

Common Equity Tier 1 Risk-based Capital Ratio 13.2% 11.8% 11.5% 4.5%

Tier 1 Risk-based Capital Ratio 13.2% 11.8% 11.5% 6.0%

Total Risk-based Capital Ratio 14.9% 13.9% 13.6% 8.0%

Tier 1 Leverage Ratio 11.4% 10.7% 10.1% 4.0%

Actual and Projected RWAs – Adverse Scenario

(in US$Billions)Actual 31Dec2016

30Sep2014 General Approach Standardized Approach – Basel III

Tier 1 Common Capital Ratio $60.9 $55.9 $59.6

Illustration

Data Extraction, Transformation and

Validation

Macro Scenario Specification and

Enrichment

Model Methodology, Specification and

Execution

Aggregation and Computation

11

About First National Bank

F.N.B. Corporation, established in 1864, is a publically traded company on the NYSE (FNB) headquartered in Pittsburgh, Pennsylvania with holdings in four

states including Pennsylvania, Ohio, Maryland and West Virginia.

12

Stress Testing at First National Bank

• Lessons Learned:• Good Modeling Requires Good Data • Data Warehousing• Resources – Technical and Human• Time Management

• Issues:• Data • Data Availability

• Tackling the Challenges

13

Lesson Learned: Good Modeling Requires Good Data

• Availability and quality of data is an issue• Not enough historical data• Incomplete / unstructured data• Decentralization of data

• Reconciling data becomes very a very long and manual process under these conditions

Potential reconciliation between credit and accounting

Consider all of the exposures of a bank across multiple business

units!

14

Lesson Learned: Data Warehousing & Resources

• Data Warehousing• Can help alleviate reconciliation by creating a “single source of

truth”• Time consuming, expensive, and vulnerable to scope creep• Implementation in phases to maximize real-time ROI is crucial

• Technical Resources (Hardware to support data and analysis initiatives)• Disc Space• Memory• CPU

• Human resources• Database administrators• System administrators• Programmers• Financial analysts & modelers

15

Lessons Learned: Time Management

• Use time in the “off-season” wisely• Perform post mortem analysis as soon as possible after each

submission to establish a game plan for the next season• Off-season activities include:

• Training• System Installations• System Enhancements• Project Planning• Resource Planning

16

Lesson Learned/Issues Uncovered: Data Availability

• Proxy data is acceptable but only a temporary solution• The Federal Reserve indicates:

• Banks must strive for better data:

• Developing high-quality internal data is a crucial project for improving a company’s stress testing estimation practices.

• Agencies encourage companies to take ownership of stress tests rather than relying on vendors

• Companies should have in place effective model risk management practices, including validation, for all models used in stress tests.

“Companies are expected to have appropriate management information systems and data processes that enable them to collect, sort, aggregate, and update data and other

information efficiently and reliably within business lines and across the company for use in…stress tests.”

17

Tackling the Challenges

• Create a cross-functional steering committee to bring business units together

• Use gap analysis to evaluate the enterprises readiness in key areas

• Data• Modeling• Human resources• Governance• Process

18

Tackling the Challenges

• Stress Testing requires results to be used in decision making• Maximize returns by using data and business intelligence

generated in the Stress Testing process beyond the evaluation of capital

• Potential benefits:• Consistent enterprise data management processes• Improved modeling to support operational forecasting, ALLL,

ALM, etc.• Better data to support portfolio and customer analysis

19

Tackling the Challenges

Governance• Regulators to continue to take a strong look at the governance process• Focus on both data management and the qualitative analysis

Stronger model validation• Banks should strive to place more formal processes around creation, management and validation• Regulators will review qualitative discussions to address how the model was challenged

• limitations, weaknesses and uncertainties in the model • the sensitivity of the model to these issues• what mitigating actions were taken

Emphasis on controls• Banks must be prepared to demonstrate that the models used by the business units are:

• utilizing consistent assumptions • maintaining balance consistency with a complete reconciliation of variances• translating model output is correctly into financial results

Enhanced qualitative analysis• Banks must improve results analysis and communication to management

20

Best Practices – Stress Testing

• Good Design• Current status 5-year objectives• Compliance Capital Management Business Strategy

• Governance• Sponsorship• Budget

• Solid Infrastructure• Integrate existing models• Capacity to expand/adapt to future regulations

For more information:

• White Paper: CCAR: An Appraisal of Current Practices

• Stress Testing Model Implementation and Integration With SAS