1. The Continuous Mortality Investigation Bureau Chris Daykin, CMI Executive Committee

2. The CMIB History Role Structure Funding Investigations Reporting results NB “Office” = “company”

3. History actuaries produced Mortality table - 1843 –“Seventeen Offices’ Table” –assured lives –experience up to 1837 further tables during 19 th century investigation into annuitants 1900-20 continuous collection of data started in 1924 – emergence of the CMI Bureau

4. Features sponsored by the actuarial profession continuous investigations independent confidentiality is paramount production of standard mortality tables actuarial profession provides expertise

5. Standard Tables PeriodAssured LivesAnnuitantsPensioners 1924-29 (males) 1947-48 1949-52 (males) 1967-70 (males) 1975-78 (females) 1979-82 1991-94

6. Comparison of the mortality of male assured lives

7. Role of CMI Research – Mortality, IP and CI. –Methodologies –Graduation –Models Data collection Analysis & reporting –Industry experience –Contributing offices Standard Tables Projecting future experience

8. Structure Life Companies and Profession CMIB Life Companies

9. Structure of the CMIB Executive Committee Management Committee MortalityIPCI Secretariat/Bureau

10. Who serves on the Committees? life office actuaries reinsurance actuaries consultants government actuaries academics

11. Role of the Secretariat  Servicing committees  organising Meetings  drafting standard reports  printing and distribution of CMI Reports  Day to day operations  collecting data  corresponding with offices  producing results  collecting money & accounts

12. Funding Each office bears their own data contribution cost + Contributions based on premium income Change to risk-based approach?

13. Investigations Mortality –life contracts issued at standard rates –impaired lives –annuitants –individual pension arrangements –group pension arrangements

14. Investigations Income Protection –individual policies –group policies Critical Illness

15. Data Timetable Collect data as at each 31 December Wait until 30 June July  October: collect and process data Nov  Dec: final chasing & checking December: run & distribute “all office” results

16. Reporting results Own Office Results –As soon as data is clean –Data summary –A/E comparison with standard tables –Special requests

17. Confidentiality taken extremely seriously only Secretariat & office sees results office numbers can be restrictive

18. Reporting results All Office pooled results –annual –quadrennial –available to members first –interim results –available to all member offices

19. Reporting results To the Actuarial Profession –CMI Reports (CMIRs) –the profession’s magazine & internet site –conferences –sessional meetings

20. Data Collection Methodologies

21. Main methods What are we doing? –What are we measuring? –Definitions Census Policy data

22. Example of census data AgeIn force at 31/12/tDeaths in year t 20IF 20.t D 20.t 21IF 21.t D 21.t 22IF 22.t D 22.t 23IF 23.t D 23.t 24IF 24.t D 24.t 25IF 25.t D 25.t 26IF 26.t D 26.t 27IF 27.t D 27.t

23. Census - calculations Exposure = ½ (IF x,t + IF x,t+1 ) + ½ D x.t correspondence between in force and deaths Expected deaths = Exposure * q compare Actual & Expected deaths –100A/E

24. Census method approximate currently used by CMIB in mortality investigation for historical reasons offices provide schedules showing number of policies at each age in force at 1 January and deaths during year ongoing: start in force = previous year end in force care with age definitions

25. Census - drawbacks approximate, so reduced accuracy limited checking of underlying data possible limited scope for analysis of subgroups –durations –policy types cannot analyse “amounts” properly policy alterations hard to spot duplicates

26. Census - advantages less data (can be handled manually) less work to check data cheaper

27. Policy data Data on per policy basis at each 31/12/t –date of birth (avoids defn. problems) –sex –start date of policy –date of death/claim/exit –type of exit –policy type –amount of benefit –identifier

28. Policy data method IP & CI investigations use this method exposure calculated exactly for each policy by counting days calculation of expected deaths & 100A/E as with census method

29. Policy data – features advantages over census method –greater accuracy –more checking possible  better data quality –more control over data included in investigations –more detailed analyses possible should be easier for offices to supply But increased storage requirements more complex to process - hence expensive

30. Observations (1) need for detailed rules consistent interpretation across offices must check to make sure data is sensible will have delays in data collection offices “come and go” office mergers

31. Observations (2) staff who produce data are not the same as staff who use the results sometimes difficult to get offices to pay attention speedy turn around helps data quality data audits

32. Common data problems policy alterations (e.g. amounts) duplicates What is a claim? (claim date in IP) multiple claims (IP) matching data across periods consistency - over time - between offices

33. Sub-population differences

34. Questions to be investigated Do differences justify a standard table? if not, how to adjust current table? –pricing –valuation trends in sub population

35. Categories investigated Main categories age male / female policy type duration smoker / non-smoker impairment Other possible ( but only have insurance data) regional variation social variation

36. Variations by age Plot of AM92 q x by age qxqx Age x

37. Variation by sex

38. Assured lives - Variation by duration

39. Variation by smoker status

40. Variation by policy type

41. Sub-population comments must collect data! data collection follows market companies that innovate via sub-population differences are exposed getting credible data sometimes difficult takes time for investigations to get established