2008 Healthcare Conference Still using a ruler to project the future? Sameet Shah FIA, Marketing Actuary Pierre Coetzee FIA, Securitisation Transaction Manager 15 May 2008

Slide 2 Agenda Experience analysis – a brief overview Finding a plausible answer Risk selection CI related issues Changing HIV limits Impact of high lapses Quantifying impact of changes

Slide 3 Agenda Experience analysis – a brief overview Finding a plausible answer Risk selection CI related issues Changing HIV limits Impact of high lapses Quantifying impact of changes

Slide 4 Experience analysis – a brief overview “Making sense of the past” – Luc and Spivak Good quality data – Complete and correct capture of all risk factors – Be careful of different data cohorts causing heterogeneity Correct age definitions (4-8% impact) Using an appropriate table (by age, sex and smoker status) Applying appropriate IBNR factors (4% impact) Retain all factors in data, e.g. don’t lose product type split Roll data forward to allow for trends, e.g. rebase to current year

Slide 5 Agenda Experience analysis – a brief overview Finding a plausible answer Risk selection CI related issues Changing HIV limits Impact of high lapses Quantifying impact of changes

Slide 6 Finding a plausible answer Act/Exp eventsAct/Exp amounts

Slide 7 Finding a plausible answer All underwriting yearsRecent underwriting years Act/Exp eventsAct/Exp amounts

Slide 8 Agenda Experience analysis – a brief overview Finding a plausible answer Risk selection CI related issues Changing HIV limits Impact of high lapses Quantifying impact of changes

Risk selection % Accepted on Standard Rates 75% 80% 85% 90% 95% 100% Q1 Yr1Q3 Yr1Q1 Yr2Q3 Yr2Q1 Yr3Q3 Yr3Q1 Yr4 Q3 Yr4 Q1 Yr5Q3 Yr5Q1 Yr6Q3 Yr6Q1 Yr7Q3 Yr7Q1 Yr8Q3 Yr8 Higher non- medical limits Revised application form - longer! Tougher u/w criteria Regular u/w audits introduced Switch to tele u/w Brokers taking difficult cases somewhere else due to slow turnaround Turnaround times improve

Slide 10 Agenda Experience analysis – a brief overview Finding a plausible answer Risk selection CI related issues Changing HIV limits Impact of high lapses Quantifying impact of changes

Slide 11 CI related issues Impact of external factors Troponins No data adjustment could understate average experience over analysis period Care needs to be taken when measuring historical trend => could overstate future experience Cancer Screening Likely to cause “shock” in cancer incidence Dealing with shock will depend on its maturity or likelihood of occurring Calendar Year 1995 incidence rates rebased to 1 and 2 respectively Male heart attack incidence - relative to 1995 Impact of Troponins UK versus US prostate cancer incidence rates per 100, Age Incidence US 1986US 1992US 1998 UK 1986UK 1992UK 1998 Source: Hospital Episode Statistics data

Slide 12 CI related issues Product changes Adding new illnesses Starting point is HES data/internet, but number of adjustments are necessary Could often result in no cost, but need to be careful TCF issues? Experience unstable, trends unpredictable? Definitions tight as intended? Other unintended consequences? Age Age Calendar Year 1995 incidence rates rebased to 1 and 2 respectively Male multi-vessel angioplasty incidence - relative to 1995 Source: Hospital Episode Statistics data

Slide 13 Agenda Experience analysis – a brief overview Finding a plausible answer Risk selection CI related issues Changing HIV limits Impact of high lapses Quantifying impact of changes

Slide 14 Changing HIV limits Industry moves HIV medical limits for single males from £250K to up to £1M What’s the expected cost? New HIV diagnoses in the UK '97'98'99'00'01'02'03'04'05'06'07 MSM Heterosexual males Heterosexual females Source: Health Protection Agency

Slide 15 How could it be priced? QuestionPossible answer Anti-selective behaviourUnderwriting and claims management robust. Only identify lives aware they are HIV positive at issue. But no fishing allowed at claim stage! Estimated impact between 0.1% and 0.3% of portfolio claim cost Proportion of business from: – Single males with cover between £250k and £1m – Unaware they are HIV positive 10% 0.05% Assumed mortality rate for a life who is HIV positive Age specific mortality for HIV life based on Danish experience (equivalent to assuming life expectancy of 24 years for a male age 35) Assumed lapse behaviour once a life is aware that they are HIV positive Once life aware HIV positive then no lapses. Assume aware shortly after policy issue. Note that the later the diagnosis the higher the expected HIV mortality

Slide 16 Agenda Experience analysis – a brief overview Finding a plausible answer Risk selection CI related issues Changing HIV limits Impact of high lapses Quantifying impact of changes

Slide 17 Impact of high lapses Impact of higher lapses on claims experience e.g. move from 4yr to 2yr commission Assume excess lapses are ‘healthy’ lives – i.e. wouldn’t be rated at time of lapse. These lives would therefore have better claim experience that the remainder of the cohort Judgement required on how much healthier!!

Slide 18 Agenda Experience analysis – a brief overview Finding a plausible answer Risk selection CI related issues Changing HIV limits Impact of high lapses Quantifying impact of changes

Slide 19 Quantifying impact of changes Business operations constantly changing, e.g. intro of tele u/w, more leniency on non- disclosure, changes to proposal form => impacts claims experience How could claims experience be “corrected” for these changes? Might be possible to rate changes and track over time Ratings subjective at first, but over time possible to develop feel for how changes might impact claims experience enabling cost/benefit analyses Quality of proposal form U/W philosophyInternal controls Electronic acceptance Non disclosureTele u/wTotal U/W “score” xx yy Claims Philosophy Ratio of staff to claims Quality of evidence gathering Experience of staff Audit findingsMedical limitsTotal Claims “score” xx yy

Slide 20 Key take-aways Understand the data – not all fluctuations are random What’s changed or changing? – Talk to marketing, underwriting, claims, risk management, distributors, customers Adjust historic data to make it relevant for projecting the future – Correct for distortions in experience data – Bring together different data sources New realistic reporting and solvency environment – Important to justify assumptions

Slide 21 Questions