CAS Ratemaking Seminar

1

• CAS Ratemaking Seminar
• March 2006
• Data-3 The Actuary and Data Standards
• Data-1 The Actuary and The Data Manager

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• The Actuary and Data Standards
• Yesterday, Today and Tomorrow
• CAS Ratemaking Seminar
• March 2006

3
Agenda

• Strategic Data Planning
• Timelines
• The Shifting Focus of Insurance Information
• How Do We Get There?
• Enterprise Data Strategies
• Standards
• Standards and Data Management Best Practices
• 10 Guidelines of Data Management
• Questions and Commentary

4
Panelists

• Art Cadorine, ACAS, ISO
• Gary Knoble, AIDM
• Pete Marotta, AIDM, ISO

5
Strategic Data Planning
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Data - A Corporate Asset

• Data, like all corporate assets, requires
  managing to ensure the maximum benefit is
  achieved by the organization.
• Well-managed, high-quality data aids good
  corporate governance by providing management with
  a cohesive and objective view of an
  organizations activity and promotes data
  transparency.
• Poorly-managed data can result in faulty business
  decisions.

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Data and Strategic Planning

• Data supports corporate decision-making
• In providing a cohesive and objective view of
  corporate activities.
• In viewing the external landscape.
• In predicting the future.
• In developing the corporate strategic plan.
• In identifying process improvements and other
  efficiencies.
• In measuring results.

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PWC Study

• Data is the currency of the new economy.
• Companies that manage their data as a strategic
  resource and invest in its quality are already
  pulling ahead in terms of reputation and
  profitability from those that fail to do so.
• Global Data Management Survey 2001,
  PriceWaterhouseCoopers

9
Enterprise Data Strategy A Definition

• A plan that establishes a long-term direction for
  effectively using data resources in support of,
  and indivisible from, an organization's goals
  and objectives.
• An Enterprise data strategy requires both
  business and technology input to
• Facilitate IT planning.
• Support the overall business plan.
• Promote and maintain clearly and consistently
  defined data across the corporation.

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Components of an Enterprise Data Strategy

• Organizational level
• Data Stewardship
• Senior level oversight of corporate data.
• From an enterprise-wide perspective.
• Data Architecture What to Run, Where to Run,
  How to Run Software and Hardware
• Ownership Customer and Data
• Data Location
• Software v. Service
• Product Definition
• Data and Process Models

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Components of an Enterprise Data Strategy

• Data level
• Data Element Management
• Data Definition and Attributes
• Code Value and Data Set Management
• Data Mapping Management
• Data Quality
• Data Standards
• Business and Efficiency Driven
• Internal and External
• Data Privacy and Security
• Compliance with Privacy Polices and Regulations
• Data from Reputable Sources
• Data Security

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Strategic Data Planning

• Strategic Data Planning is primarily a business,
  not an IT function.
• IT critical to any enterprise data strategy.

13
Enterprise Data Strategy and IT Architecture
Supports Business Strategy
A set of guiding principles that define why and
what we do
Business Strategy
Application
Data
Infrastructure
A set of guiding principles that define how we
do what we do
IT Architecture
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Results of a Successful Enterprise Data Strategy

• Provide a process and a set of tools to
  facilitate Business and IT planning and
  decision-making
• Maintain a common and consistent view of data
  that is shared company wide
• Facilitate alignment and traceability of
  significant IT investments to their respective
  business drivers

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Business Results of Enterprise Data

• Ease of doing business
• Speed to market
• Facilitate RD
• Customer Service
• Compliance

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Timelines
17
The Past

• Regulators/Business
• (underwriters, actuaries, etc.)
• Coverage Forms
• (changes in forms and coverages)
• Data Standards

18
Today

• Technology Financial
  3rd Parties
• (Internet, XML, (SOX, GLB, HIPAA, etc.)
  (Credit, DMV,
• Black Boxes, RFIDs) etc.)
• Data Standards

19
Tomorrow

• Business Needs Business, regulatory, technology,
  etc.
• (Profitability, Loss Control, Consumer
  Protection, Solvency, Privacy, Confidentiality,
  etc.)
• Data Needs
• Data Standards

20
The Shifting Focus of Insurance Information
21
Regulation

• From Annual Statement to Market Conduct Annual
  Statements to NAIC Databases
• Financial Data Repository (FDR)
• National Insurance Producer Registry (NIPR)
• Fingerprint Repository
• On-Line Fraud Reporting System (OFRS)
• Uninsured Motorist Identification Database
• From financial data used to monitor solvency to
  financial, statistical data and analytics used to
  monitor solvency
• From US driven regulations to EU and
  internationally driven regulations

22
Pricing

• From traditional underwriting and pricing - using
  traditional data sources (risk data, industry
  statistics) to predictive modeling and analytics
  - using non-traditional data sources
  (demographics, GIS, 3rd party data, non-insurance
  data, non-verifiable data sources, etc.)
• From a stable risk control and claims environment
  to a dynamic environment of new hazards - mold,
  terrorism, computer viruses, cyber terrorism,
  etc.
• From risk-specific risk management to enterprise
  risk management

23
Data

• From a data quality focus on validity, timeliness
  and accuracy to a data quality focus on
  transparency, completeness and accuracy 
• From data available on a periodic basis to data
  available real-time
• From statistical plans and edit packages to data
  dictionaries, schema and implementation guides
• From sharing data for the common good to
  protecting data for the common good

24
Technology

• From centralized highly controlled technologies
  to ASPs, the, Internet, XML, LANs, PCs, etc.
• From IT as an business enabler to IT as a
  business driver
• From mainframes to LANS and high powered PCs

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How Do We Get There?
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How do we get there?

• Enterprise Data Strategies
• Assemble the right team
• Business Needs internal and external, current
  and future
• Technology current and future
• New Products
• New Processes
• Standards
• Best Practices

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Data Users, Data Definers Data Enablers

• Business Units (Underwriters)
• Information Technology
• Finance and Accounting
• Actuaries
• Claims
• Government Affairs
• Sales and Marketing
• Research
• Data Management
• Data Element Management

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New Processes The Goal Single Entry
Real Time data entry
Download
Solution Provider/Vendor
B Carrier processes data, syncronizes with
agency data base through download
A Form/Msg from Producer (agent/broker) to
Carrier Producer either waits for download, or
does data entry to process binder, ID cards,
certs.
Re-use of data
enabler
D Data may continue along the process to be
used by Reinsurers, etc.
C Messages from Carrier to Service Providers
(CLUE, MVR)
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Straight Through Processing (STP)

• The use of common, industry standard data
  elements, throughout all interactions of all
  parties, in all insurance transactions or
  processes.
• STP allows data to flow effortlessly through the
  industry without redefinition, mappings or
  translations.

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STP Vision

• Provides a common set of definitions
• Data definitions
• Not of every transaction or message
• Allows consistent industry solutions
• Vendor provided software solutions
• Internally developed applications
• Facilitates exchange of information
• Eliminates mappings and translations
• Minimizes friction

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STP Value

• Improves data quality, utility
• better benchmarking
• Lessens data translations, eliminates return
  transactions for clarification
• Reduces friction in insurance processes
• Allows companies to differentiate on value added
• Facilitates plug and play solutions

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STP Benefits

• Improved Customer Relationship
• Less Time Processing
• Ease of Doing Business
• Retention and Growth
• Profitability

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Standards
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What are Standards?

• Definition Standard (n.) Anything recognized as
  correct by common consent, by approved custom, or
  by those most competent to decide a model a
  criterion.
• -- Websters New Universal Dictionary

35
Types of Standards

• Business Models
• Identify All the Major Processes and
  Relationships
• Common Insurance Terminology
• Coverage and Forms
• Process Standards
• Application Forms, Report of Injury or Claim,
  Licensing, etc.

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Types of Standards (Continued)

• Other
• Solvency Standards
• Financial Information Exchange Standards
• Market Conduct Information Standards
• Ratemaking Standards
• Operating Data Standards
• Data Exchange Standards
• Data Quality Standards

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ACORD Standards

• Doing Things Once Has Many Benefits
• Data names
• Data definitions
• Paper or electronic operational forms
• Machine readable formats
• Business Process Models
• Code list definitions
• Data transmission standards

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Data Collection Organization Standards

• Policy Forms and Coverages
• Rate Making Standards
• Data Reporting Standards
• Data Quality Standards
• Data Element Definitions
• Code List Definitions

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Business Process

• A business process is a collection of related
  structural activities that produce something of
  value to the organization, its stake holders or
  its customers.
• It is, for example, the process through which an
  organization realizes its services to its
  customers.

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Business Rules

• Business rules describe the operations,
  definitions and constraints that apply to an
  organization in achieving its goals. 
• For example a business rule might state that no
  credit check is to be performed on return
  customers.

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Need for Industry Collaboration
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Benefits of Industry Data Standards
Submission
Insurance Carriers
Regulatory Compliance
Broker/Insurer
Ins/Reinsurer
Claims
Reinsurer
Regulatory Authorities
STANDARDS IMPLEMENTATION
Claims Management Applications
Auditing
Service Providers
Insurance Agency
Payment transactions
Premium transactions
Agent/ Producer
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Standards and Data Management Best Practices
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10 Guidelines of Data Management

1. Data must be fit for the intended business use.
2. Data should be obtained from the authoritative
   and appropriate source.

45
10 Guidelines of Data Management

1. Data should be input only once and edited,
   validated, and corrected at the point of entry.
2. Data should be captured and stored as
   informational values, not codes.

46
10 Guidelines of Data Management

• Data should have a different steward responsible
  for defining the data, identifying and enforcing
  the business rules, reconciling the data to the
  benchmark source, assuring completeness, and
  managing data quality.
• Common data elements must have a single
  documented definition and be supported by
  documented business rules.

47
10 Guidelines of Data Management

1. Metadata must be readily available to all
   authorized users of the data
2. Industry standards must be consulted and reviewed
   before a new data element is created

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10 Guidelines of Data Management

• Data must be readily available to all appropriate
  users and protected against inappropriate access
  and use
• Data users will use agreed upon common tools and
  platforms throughout the enterprise

49
Questions and Commentary
50

• The Actuary and The Data Manager
• Custodians of Enterprise Data Assets
• CAS Ratemaking Seminar
• March 2006

51
Agenda

• Data Management Best Practices
• 10 Guidelines of Data Management
• Timelines
• The Shifting Focus of Insurance Information
• Information Quality and Assurance
• Data Quality
• Data Transparency
• ASOP 23
• Regulatory Requirements and the Role of Data
• IDMA Data Management Value Propositions
• Questions and Commentary
• Organizations That Can Help

52
Panelists

• Art Cadorine, ACAS, ISO
• Bruce Tollefson, MN WC Rating Bureau
• Christine Siekierski, WI Comp. Rating Bureau
• Pete Marotta, AIDM, ISO

53
Data Management Best Practices
54
Data Management Best Practices

• Data Stewardship establish a corporate data
  steward
• Data and Data Quality Standards foster the
  development and adoption of data and data quality
  standards
• Organizational Issues structure organization to
  promote good data management and data quality

55
Data Management Best Practices

• Operations and Processes establish processes to
  maximize data quality and utility
• Data Element Development and Specification
  design and maintain data, systems and reporting
  mechanisms in a manner that promotes good data
  management and data quality

56
10 Guidelines of Data Management
57
10 Guidelines of Data Management

1. Data must be fit for the intended business use.
2. Data should be obtained from the authoritative
   and appropriate source.

58
10 Guidelines of Data Management

1. Data should be input only once and edited,
   validated, and corrected at the point of entry.
2. Data should be captured and stored as
   informational values, not codes.

59
10 Guidelines of Data Management

• Data should have a different steward responsible
  for defining the data, identifying and enforcing
  the business rules, reconciling the data to the
  benchmark source, assuring completeness, and
  managing data quality.
• Common data elements must have a single
  documented definition and be supported by
  documented business rules.

60
10 Guidelines of Data Management

1. Metadata must be readily available to all
   authorized users of the data
2. Industry standards must be consulted and reviewed
   before a new data element is created

61
10 Guidelines of Data Management

• Data must be readily available to all appropriate
  users and protected against inappropriate access
  and use
• Data users will use agreed upon common tools and
  platforms throughout the enterprise

62
Timelines
63
The Past

• Regulators/Business
• (underwriters, actuaries, etc.)
• Coverage Forms
• (changes in forms and coverages)
• Data Standards

64
Today

• Technology Financial
  3rd Parties
• (Internet, XML, (SOX, GLB, HIPAA, etc.)
  (Credit, DMV,
• Black Boxes, RFIDs) etc.)
• Data Standards

65
Tomorrow

• Business Needs Business, regulatory, technology,
  etc.
• (Profitability, Loss Control, Consumer
  Protection, Solvency, Privacy, Confidentiality,
  etc.)
• Data Needs
• Data Standards

66
The Shifting Focus of Insurance Information
67
Regulation

• From Annual Statement to Market Conduct Annual
  Statements to NAIC Databases
• Financial Data Repository (FDR)
• National Insurance Producer Registry (NIPR)
• Fingerprint Repository
• On-Line Fraud Reporting System (OFRS)
• Uninsured Motorist Identification Database
• From financial data used to monitor solvency to
  financial, statistical data and analytics used to
  monitor solvency
• From US driven regulations to EU and
  internationally driven regulations

68
Pricing

• From traditional underwriting and pricing - using
  traditional data sources (risk data, industry
  statistics) to predictive modeling and analytics
  - using non-traditional data sources
  (demographics, GIS, 3rd party data, non-insurance
  data, non-verifiable data sources, etc.)
• From a stable risk control and claims environment
  to a dynamic environment of new hazards - mold,
  terrorism, computer viruses, cyber terrorism,
  etc.
• From risk-specific risk management to enterprise
  risk management

69
Data

• From a data quality focus on validity, timeliness
  and accuracy to a data quality focus on
  transparency, completeness and accuracy 
• From data available on a periodic basis to data
  available real-time
• From statistical plans and edit packages to data
  dictionaries, schema and implementation guides
• From sharing data for the common good to
  protecting data for the common good

70
Technology

• From centralized highly controlled technologies
  to ASPs, the, Internet, XML, LANs, PCs, etc.
• From IT as an business enabler to IT as a
  business driver
• From mainframes to LANS and high powered PCs

71

• Information Quality and Assurance

72
Data Quality

• Data Quality is defined as the process for
  ensuring that data are fit for the use intended
  by measuring and improving its
• key characteristics.

73
Managing Data Data Quality Guiding Principles

• Data is a corporate asset
• Data should be fit for the use intended
• Data should flow from underlying business
  processes
• Data quality should be managed as close to the
  source as possible
• Best Practices are ever evolving

74
Data Quality Key Characteristics

• Fit for its intended use
• Accuracy
• Validity
• Timeliness and Other Timing Criteria
• Completeness or Entirety
• Reasonability
• Absence of Redundancy
• Accessibility, Availability and Cohesiveness
• Privacy

75
Data Transparency Key Characteristics

• Data defined and documented
• Utility across time and source
• Supports internal controls.
• Clear, standardized, comparable information
• Facilitates assessment of the health of the
  systems using the data
• Promotes better controls
• Improves operational and financial performance
• Documents data elements, data element
  transformations and processes

76
ASOP 23 Data Quality

• Purpose is to give guidance in
• Selecting data
• Reviewing data for appropriateness,
  reasonableness, and comprehensiveness
• Making appropriate disclosures
• Does not recommend that actuaries audit data

77
ASAP 23 Data QualityConsiderations in
Selection of Data

• Appropriateness for intended purpose
• Reasonableness, comprehensiveness, and
  consistency
• Limitations of or modifications to data
• Cost and feasibility of alternatives
• Sampling methods

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ASOP 23 Data QualityDefinition of Data

• Numerical, census, or class information
• Not actuarial assumptions
• Not computer software
• Definition of comprehensive
• Definition of appropriate

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ASAP 23 Data QualityOther Considerations

• Imperfect Data
• Reliance on Others
• Documentation/Disclosure

80
Regulatory Requirements and the Role of Data
81
Why Regulation?

• Its all about consumer protection
• Solvency
• Ensuring that companies are financially sound and
  able to pay claims
• Market Conduct
• Point of sale and service
• Ensuring that the agent is licensed and
  appointed, the customer understands the coverage,
  claims are handled effectively (i.e. injured
  workers are paid on a timely basis)
• Rate Adequacy

82
The Impact of Standards on the US Regulatory
Landscape

• US Office of Management Budget Circular A-119
• Government agencies should recognize the
  positive contribution of standards development
  and related activities. When properly conducted,
  standards development can increase productivity
  and efficiency in Government and industry, expand
  opportunities for international trade, conserve
  resources...

83
The Impact of Standards on the US Regulatory
Landscape

• Government should utilize standards built by the
  industry and implemented within company
  operations
• Cuts expenses
• Ensures STP and quality

84
Industry, State and Federal Requirements
State
Industry
DOIs WC Commissions DMVs DOTs
Rating Bureaus Stat Agencies Residual Market Plans
Insurance Company Data Collection Data
Storage Data Sharing
Federal
SEC Treasury Homeland Security HHS
85
Regulatory Issues Data

• Reporting Requirements
• Financial
• DMV
• Workers Compensation
• Statistical
• Market Conduct
• Operations
• Electronic Applications
• UETA
• eSIGN
• Privacy (HIPAA, GLB)

86
Current Successes in Standardizing Data for
Regulatory Purposes

• Workers Compensation Insurance
• Boards and bureaus (statistical reporting)
• State WC Commissions (proof of coverage and
  monitoring claims)
• Producer licensing and appointments
• Producer to carrier information needs
• State issues such as National Producer Number
• State application compliance and filings
• Interstate Compact

87
Accountability, Quality, Transparency Regulations

• Sarbanes Oxley
• US law ensuring accuracy of financial data with
  accountability of company executives
• Solvency II
• EU proposal similar to SOX addressing financial
  reporting and public disclosure
• Reinsurance Transparency
• International Association of Insurance
  Supervisors working group to explore solvency of
  reinsurers worldwide. Differences in data
  definitions are presenting a challenge

88
SOX and the Data Manager

• The importance and visibility of Data Management
  among senior executives and regulators has
  increased.
• The importance of Data as an important corporate
  resources has increased.
• The contribution of Data Management to proper
  data and process control is more widely
  recognized.
• The demand for data quality has increased.

89
IDMA Data Management Value Propositions
90
Data Management Value

• Product Development and Revenue Generation
  Maintains data management processes and tools
  that promote speed-to-market of new products and
  services
• Enhances customer acquisition, retention, service
  and satisfaction through good quality customer
  data
• Maintains the data management processes and tools
  that support the pricing of insurance products

91
Data Management Value

• Provides an enterprise communication channel for
  new products, services, programs and technologies
  that allows all facets of the organization to
  evaluate the impact of these changes
• Specifies data needed to support new products and
  ensures that these data are assessable in a
  timely manner

92
Data Management Value

• Efficiency and Utility
• Reduces the cost of data collection, storage, and
  dispersal
• Manages data content and definition across the
  organization
• Advocates industry and enterprise data standards
  which insure consistent definitions and values
  for enterprise data elements
• Ensures accurate booking of premium and loss
  transactions
• Ensures the quality of the enterprise data
• Promotes the interoperability of data and
  databases

93
Data Management Value

• Strategic Planning
• Participates in the development of an enterprise
  data vision and strategy
• Monitors external activities and reporting on
  potential impact on enterprise
• Compliance
• Protects the privacy and confidentiality of the
  enterprise data
• Ensures compliance with data reporting laws and
  regulations,
• Represents the organization to regulators,
  workers compensation administrators, advisory
  organizations, research organizations, standards
  organizations and other industry groups

94
Questions and Commentary