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Ratemaking Seminar 2005 PowerPoint Presentation
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Ratemaking Seminar 2005

Ratemaking Seminar 2005

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Ratemaking Seminar 2005

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  1. Ratemaking Seminar2005

  2. The Convergence of Technology, Data Standards & Analytical ToolsThe Need for Better InformationArthur R. Cadorine - ISO

  3. Insurance Industry Standards • Standards for policy and claim transactions are being developed • ACORD • IAIABC • IDMA • These standards will change the industry

  4. Impact of Standards • If everyone speaks the same language, communication is possible • Information quality and timeliness improves

  5. Data StandardsWho Needs’Em and Why? • Trading partners such as insureds, insurers, TPAs, vendors, and brokers • Various sources use different definitions • Need data that is clean and consistent • Reduce duplication and cost • Numerous indirect benefits • Some obstacles remain

  6. Data StandardsDon’t They Exist Already? • Financial services and some retailers use data standards • Some insurance standards developed for specific applications • Standards are not identical

  7. Data StandardsCurrent Working Groups • IDMA TPA Data Standards Work Group • ACORD • ANSI • RIMS • ISO • WC Insurance Organizations (WCIO)

  8. Data StandardsCurrent Tools • PDRP - GL database for public entities • IDMA Claims Data Exchange Standard • IDMA Policy Data Element Dictionary • IDMA TPA Data Standards White Paper •

  9. Value of Knowing Sooner • Delays in claims reporting cost money • Real-time fraud detection could save $$ • Early claim-trend detection means corrective premium action

  10. Integrating EDI Reporting • Straight-through processingbecomes possible • Data quality improves • Information can be aggregated • ASP model has many advantages

  11. Integration of Data • ASP can have policy and claim databases • Systems can talk to one another • One source/multiple outputs

  12. Analytical Tools • Predictive models • Web access • User-friendly report writers • User-friendly analysis software

  13. 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

  14. 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

  15. ASOP #23: Data QualityDefinition of Data • Numerical, census, or class information • Not actuarial assumptions • Not computer software • Definition of comprehensive • Definition of appropriate

  16. ASAP #23: Data QualityOther Considerations • Imperfect Data • Reliance on Others • Documentation/Disclosure

  17. IDMAData Management for InsuranceProfessionals Chapters at a Glance I. The History of Insurance Data Management II. Role of Insurance Data Manager III. Key Data Elements of Insurance IV. Insurance Company’s Use of Data V. The External Insurance Environment VI. Data Quality VII. Data Repositories VIII. Future Data Management Issues

  18. C.A.S.RATEMAKING SEMINARNew Orleans 2005 INT-1 Introduction to Insurance Data Management 101 Nathan Root CNA

  19. A groupof people within insurance organizations whose primary day-to-day function is to provide business managers with the information they need to accomplish the goals and objectives of the organization. Data Managers, Who-What-Where • Core data managers are involved in: • Internal data coordination • External data reporting • Information systems development • Data administration

  20. What is a Data Manager? • A Data Manager: • Provides data • To internal customers • To external customers • Is concerned that the data provided • Is accurate & consistently derived & defined • Is readily available and timely • Is comparable/reconcilable • Secure

  21. Who are the customers? • Internal customers include: • ACTUARIES • Underwriters • Accountants • Claims • Marketing and Distribution Network • Management

  22. Who are the customers? • External customers include: • Statistical organization • Rate making organization • Insurance research organizations • Investors • State & Federal regulators

  23. The data manger’s job? • To interpret requests for data • Determine how to obtain data • Determine where to obtain data • Control the cost of development and maintenance • Provide data to customers in appropriate format

  24. Data Management’s Task The data manager’s task is to assure that the same data in different systems can be reconciled, that the data is consistent, and that derived data is defined and calculated consistently.

  25. Partnerships Data users are, and should be, involved in a partnership with insurance data managers. -A partner in = defining systems = building systems = testing systems = and final acceptance of a system.

  26. Knowledge and Other Users Just as Data Managers need and must have knowledge of the customer’s they serve, so must other insurance professional understand the Data Manager’s function. EACH TIME AN INDIVIDUAL WANTS INFORMATION, DATA MANAGEMENT SKILLS COME INTO PLAY. It must be determined where the data is, how it is identified, how it is defined AND HOW IT CAN BE VARIFIED.

  27. What do you need to know about Data Management • Data definitions and how they differ. • Coding conventions. • Data redundancy. • Level of data available/needed. • Where did the data come from and how is it maintained. • Schedule for updating. • Reasonability and reliability of the data.

  28. Who are Insurance Data Managers? • Managers of data which can be anyone • Professional insurance data managers • Actuaries • Underwriters and Agents • Claims personnel and SIU’s • Marketing personnel and Researchers • Accountants and Economists

  29. Who Owns the Data? Data use - implies data ownership - which mandates control. An individual companies data is one of its most valuable assets, if not its most valuable asset. With control comes definite responsibilities. - You also become responsible for your data’s - VALIDITY - ACCURACY - REASONABILITY - COMPLETENESS

  30. Confidentiality and Privacy All users and managers of data MUST be constantly aware of the issues surrounding Confidentiality and Privacy. Confidential data is very different from data that is controlled by privacy laws. -Confidential data: given with the understanding that the information will be treated as confidential • Privacy of data: usually governed by law, either State or Federal or both. • GRAMM-LEACH-BLILEY(GLB) • HEALTH INSURANCE PORTABILITY and ACCOUNTABILITY ACT(HIPAA)

  31. DATA for the REGULATOR • Solvency Data Accounting Actuarial • Ratemaking Data Rate Filings Special Calls

  32. The Data Management Environment Micro-Computers have changed the data management environment. Literally every user of a micro-computer has had to become a data manager. The Insurance Data Management Association(IDMA) provides education and a forum for knowledge in this field

  33. Insurance Data Management Association(IDMA) IDMA has partnered with the CAS and is prepared to share its knowledge of data with CAS members. The IDMA’s “Data Management for Insurance Professionals” is available today. It is designed as a primer and is intended for both the professional and those yet to become professional within the insurance industry. For further information on this course contact the IDMA at 1-201-469-3069.

  34. IDMAData Management for InsuranceProfessionals Chapters at a Glance I. The History of Insurance Data Management II. Role of Insurance Data Manager III. Key Data Elements of Insurance IV. Insurance Company’s Use of Data V. The External Insurance Environment VI. Data Quality VII. Data Repositories VIII. Future Data Management Issues

  35. Casualty Actuarial SocietyRatemaking SeminarNew Orleans 2005 INT-1 Introductory Data Management 101 Al Hapke Meadowbrook Insurance Group

  36. Your Role in Data Management • Demanding User • Lack of defined needs • Lack of knowledge about information technology • Lack of business knowledge in the IT staff

  37. Therefore, you must communicate your goals effectively and clearly. Objective of Data Management: To store and organize data in a way that allows the analyst to answer questions about the business. These questions should help direct and guide the management of the business.

  38. Processing Systems are not adequate to satisfy the analytical needs of the company. They’re designed to do work, not answer questions.

  39. Steps That Help Communication: • Formulate many specific questions • Brainstorm yourself • Talk to your customers/clients/boss • Read actuarial papers • Review competitive rate filings • Write them down • Design your spreadsheet or model to answer the question • Determine what you need to populate the spreadsheet

  40. Example of Questions: What do we expect to pay for claims in this class vs. other classes? 1. Age/experience of driver 2. WC class code 3. Property construction 4. State/territory/location 5. Other characteristics such as credit rating, new/renewal, etc.

  41. Issues with this Question: • Volume in each class or characteristic • How much history? • Can premium be appropriately matched with losses? • Can earned exposures be captured? • Can class definitions be multidimensional?

  42. Other Questions: • What is our exposure to maximum loss? • Answer can be found in limit/location studies • How much development can we expect on reported losses? • Considerations: • Report date • Historical claim distributions

  43. Other Questions (continued) • Has our underlying book of business changed? • What types of losses are we seeing? • This is only meaningful if we have been profiling our mix of claims so that we can see what’s different. • e.g • Size of loss at consistent points in time • Minor coverage detail • Cause of loss

  44. Other Questions (continued) • Who, how much, and what is your producer selling? • What are the characteristics of the business being brought in the front door? • Or…. Leaving through the back door?

  45. Issues to Consider in Your Management Information System • Ease of Access - level of independence from programmers • Flexibility - new classes, new characteristics, new products • Quality of data