GE Frankona Re g Valuing Healthcare - Introduction to Pricing Ash Desai

e GE Frankona Re Objectives of this session Understand the pricing models used Understand the data sources used in pricing Examine the challenges involved in using these sources Understand the key concepts involved in examining experience data Understand impact of future trends on pricing

e GE Frankona Re Critical Illness Insurance payable on the diagnosis of a specified “critical” condition –eg: Cancer, Heart Attack Lump Sum benefit / instalments Two main types of cover –Stand Alone –Accelerated An example healthcare product

e GE Frankona Re Benefit paid on critical illness only – no payment on death Payment subject to a minimum survival period –e.g. 28 days or 14 days Stand Alone Critical Illness

e GE Frankona Re Benefit paid on the first of: – a critical illness – death Benefit could be partially accelerated Accelerated Critical Illness

e GE Frankona Re Objectives of this session Understand the pricing models used Understand the data sources used in pricing Examine the challenges involved in using these sources Understand the key concepts involved in examining experience data Understand impact of future trends on pricing

e GE Frankona Re Pricing Models Multi State Modelling Dead Sick Healthy

e GE Frankona Re Multi State Modelling - Theory I x = No. of incidences of CI for lives aged x (dh) x = No. deaths among healthy population from a cause other than CI (or in survival period for Standalone) (dc) x = No. deaths among those suffering CI due to CI (do) x = No. deaths among those suffering CI from a cause other than CI Total S/A claims= I x * t p x (incidence adjusted for survival of survival period, t) Total Acc claims = I x + (dh) x - (1)

e GE Frankona Re Multi State Modelling - Practical Approach Theory looks simple - but no reliable data to calculate separate items, especially (dh) x In practice we need to : – re-express the formula using k x –where k x is the proportion of deaths due to CI –assume mortality of CI sufferers from causes other than CI is the same as mortality of healthy lives And we end up with : –i x = (1 - k x ) * q x where –i x is CI incidence rate per mille –k x is proportion of deths due to CI –q x is mortality rate per mille

e GE Frankona Re Multi State Modelling - Practical Approach (dc) x = k x * d x we know d x = (dh) x + (dc) x + (do) x - (2) re-express (2) as –(dh) x + (do) x = (1 - k x ) * d x - (3) assume (do) x /(ls) x = (dh) x /(l x - (ls) x ) –where (ls) x = no. lives aged x suffering a CI –where l x = no lives aged x use (2) and (3) to eliminate (do) x to get –(dh) x * l x /(l x - (ls) x ) = (1 - k x ) * d x - (4) divide (1) by healthy population at outset (l x - (ls) x ) –I x /(l x - (ls) x ) + (dh) x / (l x - (ls) x ) - (5) Replace (4) into (5) to get i x = (1 - k x ) * q x

e GE Frankona Re Objectives of this session Understand the pricing models used Understand the data sources used in pricing Examine the challenges involved in using these sources Understand the key concepts involved in examining experience data Understand impact of future trends on pricing

e GE Frankona Re Pricing data requirements i x = (1 - k x ) * q x Incidence Data Proportion of deaths due to CI Mortality following CI –probability of surviving a CI to help estimate reduction in incidence due to survival period requirement

e GE Frankona Re Population data - Incidence Data –Morbidity Statistics from General Practice –Hospital Episode Statistics –ONS Cancer registrations –US publications Population data - Proportion of deaths due to CI –ONS Mortality by cause –CMI Statistics for Assured Lives –WHO Population Data - Mortality following CI –ONS Cancer Survival Trends Experience –Own or Intercompany –Reinsurer’s Sources of pricing data

e GE Frankona Re Objectives of this session Understand the pricing models used Understand the data sources used in pricing Examine the challenges involved in using these sources Understand the key concepts involved in examining experience data Understand impact of future trends on pricing

e GE Frankona Re Data source –HES - admissions for treatment in NHS hospitals in England (by age & sex) Challenges –relies on admission process being completed - a problem for immediate deaths –private treatment excluded –those not receiving any treatment also excluded –population data  insured life data –underwriting selection effects vs. ultimate experience –aggregate - e.g. smoker status, socio economic groups, occupation, geographical location etc –HES definition  product definition Population Data - Incidence Data

e GE Frankona Re Data source –Morbidity statistics from General Practice - data on why people consult GPs Challenges –open to interpretation by doctor or practice nurse –Main Advantage : splits data by ‘type’ of consultation (ie. first, new or ongoing ) and therefore helpful for removing re-admissions from HES data –population data  insured life data Data source –ONS Cancer registrations - records number of people who were diagnosed for the first time in any year Challenge –only available for cancer Population Data - Incidence Data

e GE Frankona Re Data source –US publications or Irish data Further challenges –variations in experience –differences in lifestyle, diet, education and environment –attitudes to healthcare –differences in medical opinion Benefits –established product overseas –scarce domestic data Population Data - Incidence Data

e GE Frankona Re IC94 v CIBT93 Both tables: –Male and Female –Aggregate –No adjustment for selection But IC94…. –Adjusted for Insured Lives –Adjusted for Ireland –No allowance for TPD

e GE Frankona Re Comparison of UK Data (CIBT93 ) with Irish Data (IC94)

e GE Frankona Re Adjustments required to Incidence data –differences in definition of illness for insurance - eg. single vessel angioplasty and stroke –for immediate deaths –for multiple illnesses - eg.heart attacks and bypass surgery Adjustments for Assured Lives –ratio between population and assured life mortality –varying by age, sex and disease (if data allows) –Selection effects –Apply non smoker/smoker discount/loading –Interpolation/Graduation Population Data - Incidence Data

e GE Frankona Re Data source –own experience Challenges –credible data? –higher variability likely –sparse data sets –misleading interpretation –inadequate systems - inaccurate and incomplete data Benefits –insured experience –most relevant Experience Data

e GE Frankona Re Data source –reinsurer’s or industry wide(e.g CIBT93) Challenges –relevant? –different business mixes –differing underwriting and claims philosophies –differing target markets Benefits –insured experience –credible data set –less variability year to year Experience Data

e GE Frankona Re Objectives of this session Understand the pricing models used Understand the data sources used in pricing Examine the challenges involved in using these sources Understand the key concepts involved in examining experience data Understand impact of future trends on pricing

e GE Frankona Re Example –CI Healthcare Study Group Base Table Date requirements –exposure –claims Key analyses –experience against standard tables –smoker/non smoker analysis –selection effects –variation by offices/distribution channel –cause of claims Experience Data - Key concepts

e GE Frankona Re Exposure –Data at each year-end, split by –Sex –Smoker status –Duration (0/1/2+) –Cover Type (Stand Alone /Accelerated) –5 year age bands or individual ages –Policies and amounts Experience Data - Data requirements

e GE Frankona Re Details for each claim: Sex Smoker status Cover type (Stand Alone/Accelerated) Date of birth Policy commencement date Critical Illness sum assured Claim amount paid Date of diagnosis Date claim paid Cause of claim Experience Data - Data requirements

e GE Frankona Re Against Standard Tables (% of CIBT93) – Accelerated CI, Male, aggregate, policies Experience Data - Key Analyses

e GE Frankona Re Smoker / Non smoker differential Experience Data - Key Analyses

e GE Frankona Re Selection effects –Accelerated CI, Male, Non-smokers, policies, CIBT93 Experience Data - Key Analyses

e GE Frankona Re Variation by offices/distribution channel Experience Data - Key Analyses Distribution ChannelActual/Expected % Bancassurer37% DSF51% IFA34%

e GE Frankona Re Cause of Claim –Accelerated CI Experience Data - Key Analyses

e GE Frankona Re Cause of Claim –Accelerated CI, Males, Aggregate Experience Data - Key Analyses

e GE Frankona Re Cause of Claim –Accelerated CI, Females, Aggregate Experience Data - Key Analyses

e GE Frankona Re Objectives of this session Understand the pricing models used Understand the data sources used in pricing Examine the challenges involved in using these sources Understand the key concepts involved in examining experience data Understand impact of future trends on pricing

e GE Frankona Re Different sources = different challenges However irrespective of data source, one common problem is that …. …..“historical experience is not always an accurate indicator of future experience” Sources of pricing data

e GE Frankona Re Medical advances –reduces incidence by treating at an earlier stage (Cancer) –increases surgical procedures (Angioplasty) Allow for future trends –Prostate Cancer Risk Management - considerable issue when pricing a guaranteed product Especially where the future is uncertain….

e GE Frankona Re Medical Advances

e GE Frankona Re Cancer Registrations Source: ONS

e GE Frankona Re Trends Heart Attack per 100,000 population Trend - to 93/94 ( -1.2%pa) to 94/95 (- 3.7% pa) Source: HES

e GE Frankona Re Trends

e GE Frankona Re

e GE Frankona Re Pricing- are we sitting on a time bomb? Potential impact of prostate cancer

e GE Frankona Re Prostate Cancer What is it? –cancer in the male prostate gland What’s the prostate gland –it’s a cluster of glands surrounding the urethra near the bladder - exact function unclear

e GE Frankona Re Prevalence of latent prostate cancer - % of population

e GE Frankona Re Impact on Pricing 0% 20% 40% 60% 80% 100% 120% 140% 160% 180% % Loading Age Loading to male core 6 rate 100% find rate 50% find rate 25% find rate

e GE Frankona Re

e GE Frankona Re Objectives of this session Understand the pricing models used Understand the data sources used in pricing Examine the challenges involved in using these sources Understand the key concepts involved in examining experience data Understand impact of future trends on pricing

e GE Frankona Re Valuing Healthcare - an Introduction to Pricing Discussion and Questions