The Continuous Mortality Investigation Bureau

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 19th 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

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. Life Companies and Profession CMIB Life Companies Structure

9. Executive Committee Management Committee Mortality IP CI Secretariat/Bureau Structure of the CMIB

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

23. Census - calculations • Exposure = ½ (IFx,t + IFx,t+1) + ½ Dx.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 qx by age qx 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