CAS Ratemaking Seminar March 2006: Data-3: The Actuary and Data Standards

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

2. 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

6. 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 organization’s activity and promotes data transparency. • Poorly-managed data can result in faulty business decisions.

7. 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.

8. 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.

10. 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

11. 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

12. 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 Data Application Infrastructure A set of guiding principles that define how we do what we do IT Architecture

14. 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

15. Business Results of Enterprise Data • Ease of doing business • Speed to market • Facilitate R&D • Customer Service • Compliance

16. Timelines

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

18. Today TechnologyFinancial3rd 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

25. How Do We Get There?

26. 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

27. 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

28. A B Producer/ agent/ Broker Carrier D C Service Provider Reinsurer 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)

29. 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.

30. 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

31. 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

32. STP Benefits • Improved Customer Relationship • Less Time Processing • Ease of Doing Business • Retention and Growth • Profitability

33. Standards

34. 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.” -- Webster’s 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.

36. 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

37. 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

38. Data Collection Organization Standards • Policy Forms and Coverages • Rate Making Standards • Data Reporting Standards • Data Quality Standards • Data Element Definitions • Code List Definitions

39. 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.

40. 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.

41. Submission Broker/Insurer Regulatory Compliance Ins/Reinsurer Claims Reinsurer Auditing Claims Management Applications Premium transactions Payment transactions Regulatory Authorities Insurance Agency Insurance Carriers Service Providers Agent/ Producer Need for Industry Collaboration

42. 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 Premium transactions Payment transactions Agent/ Producer

43. Standards and Data Management Best Practices

44. 10 Guidelines of Data Management • Data must be fit for the intended business use. • Data should be obtained from the authoritative and appropriate source.

45. 10 Guidelines of Data Management • Data should be input only once and edited, validated, and corrected at the point of entry. • 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 • Metadata must be readily available to all authorized users of the data • Industry standards must be consulted and reviewed before a new data element is created

48. 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