Data/Technology 3 - Data Management Panel Discussion CAS Ratemaking Seminar March 2005

1. Data/Technology 3 - Data ManagementPanel Discussion CAS Ratemaking Seminar March 2005

2. Panelists • Jason L. Russ, Consulting Actuary, Milliman, Inc. • Michael L. Toothman, Consultant Actuarial & Risk Consulting Services • Peter Marotta, Principal, ISO • Gary Knoble, Vice President, The Hartford

3. Data Management and the Actuary An ABCD Perspective Michael L. Toothman

4. The Role of the ABCD • Consider complaints • Counsel Actuaries • Recommend disciplinary action • Respond to requests for guidance • Mediate issues

5. The Role of the ABCD Consider Complaints • Conduct Investigations • Hold Hearings

6. The Role of the ABCD Counsel Actuaries • A primary role for the ABCD • Possible result at several points in the ABCD process • In lieu of an investigation • After the investigation • After a hearing

7. The Role of the ABCD Recommend Disciplinary Action • Only the participating organizations have authority to discipline their members • Occurs in well under 5% of ABCD case

8. The Role of the ABCD Respond to Requests for Guidance • Over 50% of ABCD cases • Key function of the ABCD

9. Comply with Code of Conduct Comply with Qualification Standards (both general and specific) Comply with Standards of Practice The Actuary’s Responsibility

10. Precept 1: An Actuary shall act honestly, with integrity and competence, and in a manner to fulfill the profession’s responsibility to the public and to uphold the reputation of the actuarial profession. The Actuary’s Responsibility

11. Real Life Issues • ABCD classifies cases as • Practice • Conduct • Majority of cases are conduct issues • Very few cases have involved data quality

12. Code of Professional Conduct The Code of Professional Conduct identifies the professional and ethical standards required of actuaries who belong to the Academy. The SOA, ASPA, the CAS, and the CCA have adopted identical codes.

13. ABCD Case Resolution ABCD cases considered during 2003:

14. ABCD Case Resolution ABCD cases considered during 2003: CASES CLOSED Action by Individual ABCD members Replied to requests for guidance 30 Mediated 1 Disposition by Chairperson and Vice Chairpersons Dismissed 4 (Referred to Investigators in 2003—4) Disposition by Whole ABCDafter investigation Dismissed 3 Dismissed with guidance 2 Counseled 2 Counseled after hearing 2 Recommended suspension 1 Total 45 CASES IN PROGRESS (as of 12/31/03) Pending investigation 7 Pending hearing 1 Pending receipt of more information 6 Request for Guidance pending 2 Total 16

15. ABCD Case Resolution Since its inception in 1992, the ABCD completed its cases as follows:

16. INSURANCE DATA MANAGEMENT ASSOCIATION (IDMA) Peter Marotta

17. Who Are We? • IDMA is a non-profit professional association advancing data management through education

18. Timeline • Established March 14, 1984 • First annual meeting December 10, 1985 • April 1990 – first graduates received professional designations • January 2005: • CIDMs: 119 • AIDMs: 121

19. Mission and Purpose • Promote professionalism in the Data Management discipline, principally through education • Create and maintain a curriculum for developing data management professionals, test professional proficiency, and provide professional certification

20. Mission and Purpose • Provide a forum for the discussion of insurance data management issues • The focus of such discussions is on the satisfaction of insurance data needs in a manner that takes advantage of current technology and is efficient and consistent with data quality

21. Membership • Statistical Agents • Regulators • Third Party Administrators • Consultants • Property & Casualty Insurers • Life Insurers • Trade Associations • Technology Vendors • Associations • Societies

22. Functions Represented • Accounting/finance • Data administration • Actuarial • Operations/administration • Claims • Statistical • Data processing • Data quality • Underwriting • Product development

23. Products and Papers • Data Management Value Propositions • Monthly data management bulletin (EDMIS) • Data Quality Certification Model • White Paper on Data Quality • Recommended Steps for Legislators and Regulators to Follow in Issuing Data Requests • White Paper on Recommended Standards for Injury Coding • Inventory of Carrier Reports • Co-sponsor, with the Casualty Actuarial Society (CAS), an academic paper competition

24. Curriculum • The AIDM designation requires passage of four IDMA examinations • The CIDM designation also requires the passage of coursework from one of four organizations - CPCU, LOMA, SOFE or CAS

25. IDMA Courses: Insurance Data Collection and Reporting (IDMA I) The course addresses the core of most data managers' responsibilities, the collection and reporting of statistical and financial insurance data.

26. Insurance Data Quality (IDMA 2) The course update focuses on very specific topics concerning data quality and how to maintain quality. The syllabus includes texts from two leaders in the field – Thomas C. Redman and Larry P. English, and materials from the CAS and IDMA.

27. Systems Development and Project Management (IDMA 3) The course presents and analyzes in detail the many aspects of successful project management: staffing, implementation, leadership and other roles, “Project Authority”, time management, scheduling techniques, dealing with problems cultural and otherwise, and effective strategic planning.

28. Data Management, Administration, and Warehousing (IDMA 4) This course explores data flexibility and shareability concepts which are aimed at increasing the availability and usefulness of Data, as well as, an introduction to basic concepts and principal tools for maximizing the usability and value of data. The Bill Inmon concept of the Corporate Information Factory is explored. In this 2003 update, new focus and attention are given to data standards.

29. Data Management for Insurance Professionals This overview course is highly recommended for a broad audience including new hires, IT personnel who want to deepen their knowledge of the business side of data management, anyone who manages data in the industry, and anyone who needs to use or communicate data – from actuaries to underwriters. It is clear, well organized, well written, illuminating, and structured for easy study. Student proficiency is tested via a 100-question, multiple-choice exam, and the successful student will earn a diploma.

30. Data Management Value Proposition(see Appendix for details) • Reduces cost of collecting, storing and dispersing data • Improves data quality, establishes standards • Provides quality controls • Protects privacy and confidentiality

31. Contact Information • Headquarters: 545 Washington Boulevard, 22-16 Jersey City, NY 07310-1686 • Website: www.idma.org • Executive Director: Richard Penberthy • Email: rpenberthy@idma.org • Phone: 201-469-3069 • Fax: 201-748-1690

32. INSURANCE DATA MANAGEMENT ASSOCIATION (IDMA)

33. IDMA: Data Management Value Proposition Value: Data Quality • Good data management improves data: • Validity—Are data represented by acceptable values? • Accuracy—Does the data describe the true underlying situation? • Reasonability—Does the data make sense? How does it compare with similar data from a prior period? • Completeness—Do you have all the data you need? • Timeliness—Are the data current? • Allowing the data user to have more confidence in, and a better understanding of, the data being used.

34. IDMA: Data Management Value Proposition Value: Better Decisions • Better decisions result from better data. • Better priced risks—rates, increased limits, etc.—means improved bottom line, greater customer satisfaction, improved customer retention, increase in number of customers • Improved ability to explain, defend (and testify as necessary) decisions with better data behind the decision, documented controlled data management processes in place helps to prove the value of data being used • Improved data integrity, data utility • As data is and can be sliced ever more finely, attention to quality, privacy and confidentiality is critical. Data management skills can ensure that.

35. IDMA: Data Management Value Proposition Value: Better Decisions (continued) • The user’s time is freed up for more focus on core professional responsibilities, decisions and analysis when data quality is assured under the guidance of the data manager. • Putting data management under the responsibility of a data management professional allows both disciplines to do what they do best and are best trained to do. • In many cases, skilled data managers can assume handle functions such as responding to special calls. • Predictive modeling is improved when better data are available, allowing for better existing products and better new product development.

36. Data Management and the Actuary The Value of the Data Manager to the Company Actuary Gary Knoble

37. IDMA Data Management Value Proposition • Value to Actuaries • Better Decisions • Data Quality • Internal Data Coordination • Compliance

38. Internal Data Coordination • Reduces cost and time of data collection, storage, and dispersal • Promotes interoperability of data and databases – data integration • Manages data content and definitions • Advocates data standards • Ensures quality and communication between sources

39. Enterprise Data Initiative • Mission: • To provide direction and oversight to the Actuarial and business communities concerning data, data management (including quality), data analytics, including sourcing, manufacturing, and delivery. • To insure data integrity and availability in actuarial work products and business requirements.

40. Today Lack of enterprise vision Lack of communication between divisions Independent resourcing for initiatives Tomorrow Actuarial vision to influence enterprise vision Communication across divisions Shared resources Vision

41. Independent budgets Data planning in business units without Actuarial representation Data sources built for individual needs Redundant data Budget coordination Actuarial presence in all business data planning Data sources built from common plan Minimize redundancy Vision (cont.)

42. Redundant sources Lack of standards Lack of meta data Lack of business rules Lack of knowledge transfer Authoritative source Standards Meta data repository Documented rules Knowledge transfer through documentation and rules Vision (cont.)

43. Disparate processes for managing data Uncoordinated vendor relationships Inconsistent technologies Different tools in silos Lack of reconciliation Core processes Standard vendor management Consistent technologies Coordinated tools Authoritative reconciled sources Vision (cont.)

44. An Approach • Framework for Governance • Rules/Operational Policies • Change data process • Technology infrastructure • Measure results