enterprise risk management: new paradigms for measuring and reporting enterprise risk

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Enterprise Risk Management: New Paradigms for Measuring and

Reporting Enterprise Risk

Presented by Charles McKinney

PRMIA Montreal Chapter

December 2, 2009

Today’s discussion

• Dawning of a new era

• Evolution of business intelligence

• Interesting topics for risk managers

• Plotting and executing strategy

Disclaimer: The information and views contained in this document are solely those of the presenter. Although attempts to provide reliable, useful information were made, no guarantee can be made that the information is accurate, current or suitable for any particular purpose.

“Killer apps” in financial services

• Killer apps are technologies at the center of value creation

• Structured finance enabled risk transfer:-Asset-backed securities-Mortgage-backed securities and CMOs-Collateralized debt obligations-Credit derivatives

• If securitization embodied “killer apps,” is a new era of competing on analytics (managing risk) on the horizon?

Dawning of a New Era Evolution of Business Intelligence Topics of Immediate Interest Plotting and Executing Strategy

Coming back from the brink

Market capitalization and total shareholder returns suggest financial institutions may be rebounding from the lows of 2008.

$0$1$2$3$4$5$6$7$8$9

2004 2005 2006 2007 2008 2009-Q3 -60%

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Total banking market capitalization1

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Total shareholder returns2

Sources: Thompson Reuters Datastream; Boston Consulting Group

Notes1. Total banking market capitalization refers to market capitalization of banking sectors worldwide. Figures are rounded.2. Total shareholder return consists of capital gains and free-cash-flow yields. TSR calculated after conversion to U.S.

dollars.


Non-agency MBS market has dried up

Mortgage and asset securities issuance (end of period)

Source: Inside MBS and ABS

The market for non-agency mortgage-backed securities contracted severely in 2008, as the financial crisis unfolded. It continues to be dry.

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$1,000 $1,250 $1,500 $1,750 $2,000 $2,250 $2,500 $2,750

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Ginnie Mae Freddie Mac Fannie Mae Non-Agency


U.S. house price depreciation may be bottomingFHFA (OFHEO) House Price Index History for the United States (Purchase-Only Index)

Percent Change in the FHFA (OFHEO) House Price Index (Purchase-Only Index)

Source: Federal Housing Finance Agency. Note: The FHFA (OFHEO) House Price Index is a weighted, repeat-sales index. It is based onrepeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae orFreddie Mac since January 1975.

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% change over previous quarter (seasonally adjusted) % change over previous four quarters (seasonally adjusted)

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U.S. home sales decline may be bottoming

U.S. New and Existing Home Sales

Source: National Association of Realtors. Latest data are August 31, 2009..


U.S. housing affordability appears to be improving

NAR U.S. Quarterly Housing Affordability Index

According to data from the National Association of Realtors, housing affordability is at an all-time high in the U.S.

Source: National Association of Realtors, Freddie Mac.


U.S. foreclosures are at a record level

Foreclosures as a Percent of Total Loans in the U.S.

Source: Mortgage Bankers Association


Further interest rate resets are expected

Source: Agora Financial, Credit Suisse (U.S. Mortgage Strategy), Loan Performance, FH/FN/GN.


Headlines suggest broadening data requirements

• Lingering uncertainty about the U.S. mortgage market (as well as other economic forces) highlight the need for quality information

• Organizational and regulatory data requirements are bound to grow to encompass external and internal factors affecting enterprise risk

Housing Feedback Loop (External Considerations)

Source: International Monetary Fund

• Enterprise risk data:‒ Credit risk ‒ Financial risk‒ Market risk‒ Operational risk (external

losses, too)• Financial data• Transaction data• Products and pricing• Customers and markets• Regulatory compliance• Legal considerations

Internal Considerations


Re-regulation implies new data requirements

• Regulatory reporting requirements of financial institutions are on the rise

• Certain themes are prominent among the various U.S. proposals and draft legislation to re-regulate financial services:-Expanding regulatory oversight to all financial services institutions-Extending the scope of regulatory oversight for systemically important firms and

to ensure financial markets can absorb system-wide stress-Strengthening consumer protection and mitigating predatory practices-Providing governmental actors with new power and better tools to monitor

financial institutions, take corrective action and resolve important failures-Raise international regulatory standards and improve international cooperation

• These themes imply new requirements to collect, use and share information in the public sector


Standardizing inter-organizational data

• Industry standards for inter-organizationally shared data are common:-CUSIP numbering-Swift messaging standards-Financial products markup language-Extensible business reporting language

• Interest in standardizing other types of shared data is building (e.g., universal mortgage identification and data standards in the U.S.)

• There is momentum to establish a U.S. National Institute of Finance that would set standards, gather data and provide analytical capabilities to:-Assist with monitoring systemic risk-Perform independent risk assessments of individual financial entities-Provide advice to regulatory agencies and the U.S. Congress


A new era driven by data and analytics

• Coming out of the financial crisis, data and analytics are more important than ever:-Recent events highlight a need for evidence-based management (including counterviews where risk could materialize)

-Having greater access to data and making better use of analytics are important tools for managing systemic risk

• Financial institutions have the IT foundation to elevate the quality of their data and analytics


Framing business-analytical gaps

• The financial crisis occurred despite significant models, tools and systems to manage risk

• Financial institutions did not compete on analytics where enterprise was adequately measured, evaluated, monitored—and integrated into corporate governance, strategy and management


Definition of business intelligence

• Deriving and refreshing knowledge to manage the firm:-Transforming raw data into meaningful, useful information-Equipping managers to monitor and control the enterprise-Enabling managers to make fact-based decisions-Challenging views with new information

• Business intelligence describes management information systems that support planning, decision making and performance management

• Business intelligence is evolving to emphasize business analytics:-There is a focus on investigating past performance to gain insights that drive

business planning, resource allocation and risk management-The technologies, skills, applications and practices for business analytics

integrate and mine and variety of data to gain new insights-Business analytics implies new management paradigms, which exploit data, facts

and insights for competitive advantage (e.g., Tom Davenport’s research)


Business intelligence is a growing software market

• The business intelligence market is forecast to more than double in size

• Business analytics and other emerging BI solutions will fuel that growth

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Core Business Intelligence Business Analytics Solutions Other Emerging BI Capabilities

Source: Adapted from Forrester


Evolving to “smart business intelligence”

Business intelligence capabilities are following an evolutionary path. Reporting functionality was the starting place. With a focus on business analytics, “smart business intelligence” is a desired future state

Source: Forrester


Reporting encompasses traditional applications• Traditional “business intelligence” solutions support report production and ad hoc

queries (i.e., decision support)

• Reporting solutions consolidate data elements, often into a data mart that is accessed by commercial software tools

Source: Wherescape


Analytical tools equip users to answer questions

• Business intelligence tools enable users to:-Answer questions through the analysis of data-Present the answers in a visually compelling format-Routinely reproduce the analysis with updated data

• A “dimensional model” organizes data that is analyzed

• Analysis is limited to the data elements accessible by the business intelligence solution, unless custom queries are also used

• These data must reside in a structured format


The business intelligence landscape is complex

• After years of implementations and upgrades, many institutions do not have a unified business intelligence architecture. Instead, there is a set of IT solutions

• Business intelligence software has become more complex to assimilate heterogeneous data sources and accommodate diverse methods of data analysis and information delivery

Source: Forrester


Unifying data sources for business analytics

• Today many institutions are unifying their data sources, so business analytics that are based on a wide array of external and internal data types can be achieved

• These efforts are closely aligned with initiatives to rationalize data architecture and improve information quality (e.g., master data management)

• Some institutions are planning to bring unstructured content into the BI environment

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A robust information model, data dictionary and business requirements drive unification


Competing on analytics is the future

• Strategies and execution based on analytics are common today:-Automated underwriting in the mortgage industry-Credit scoring in the United States and around the world-Custom promotions, pricing and loyalty programs-National defense and homeland security

• Business analytics are used in enterprise risk management:-Risk dashboards-Modeling and forecasting-Testing approaches-Data mining

• Future competitive strategies will exploit business analytics in an integrative, comprehensive fashion


IT paradigms for business intelligence are changing• A new paradigm of “in database analytics” embeds business intelligence in processes (moving

away from departmental business intelligence applications)

• This paradigm tightly couples database and analytics processes by moving business intelligence functions into the enterprise data warehouse

• Any new IT paradigm requires cost/benefit analysis and evaluation by business users

Traditional Setup for Predictive Analytics “In Database Analytics” Paradigm

Source: Forrester


User-built applications can be effective

• User-built applications are often appropriate, such as when:- Researching and prototyping new model applications- “Time to usability” is critical, and the SDLC is too slow- Flexibility is required (e.g., multiple model versions)

• Capacity planning and change management are important


Information quality is an unmeasured challenge

• Financial institutions are beginning to measure information quality at an enterprise level and in an inter-organizational context to drive quality improvement

• Information quality appears to be a lever for reducing risk, especially where transaction processing and models use nonstandard reference data


Measurement of Information Quality

Counterparty Data Quality

Data Quality at Delivery

Accuracy of The Data

Data Volatility

Internal Data Quality

Intrinsic Data Quality

Internal Data Consistency External Data

Consistency

Impact of Information Quality

Recourse for Information Quality

Unstructured data is an untapped gold mine

• Unstructured content represents as much as 80% of an institution’s data

• Tools to convert unstructured content into a structure for analysis is an emerging area of interest (e.g., web analytics, forms processing, loan file reviews)

Turning Unstructured Information into Business Intelligence

Transportation autom ob ile m ake H onda Ford Porsche Saturn type SU V sedan sports sta tion w agon a irplane m ake Boeing ........ ..

AnalyticsEngineUnstructured Content

(Scanned Loan Files)

BusinessMetadata

Structured Data

Business IntelligenceSource: William Inman (adapted)

Borrower Income Wages & Salary Business Income Interest Received Etc. Social Security Nbr Borrower SSN Co-borrower SSNProperty Street Address Zip Code Appraised ValueLoan Points and Fees


Assessing business intelligence for risk

• Periodically assessing business-analytical capabilities, in the context of strategies to measure, monitor and report risk, is a good idea.

• Things to consider:-Current capabilities and any shortcomings-Availability and reliability of BI input factors-Flexibility and scalability of the IT platform-Architecture of future BI capabilities for risk-Requirements for unifying enterprise data-Leveraging past investment (e.g., Basel II)-Time and cost to address current gaps

• The current environment offers many opportunities to improve the business intelligence capabilities for enterprise risk management


enterprise risk management: new paradigms for measuring and reporting enterprise risk

Documents

new paradigms

house price depreciation

absthe market

sales index

strategy3nonagency mbs

enterprise risk management

risk transfer

risk managersplotting