1. 3. Recovery rates
1.1. Data
1.1.1. Need large database for rates that are age, gender and duration dependent, and allows for influences of occupation and cause of claim
1.2. Grouping the data
1.2.1. Differential pricing may not be allowed, but can use for analysis. Main factors for subdividing claim recovery rate data: -Age, gender, smoker, occupation, size of benefit, class of policy, individual or group, length of time since start of claim, distribution source, territory, cause of claim. Recovery rates tend to be short, usually under 6 months, remaining cases tend to to be well in excess of 6 months. Recovery rates decrease with duration, reducing proportion of less serious cases remaining.
1.3. Adjustments
1.3.1. Medical advances aid speedier recovery - effect on claim cost may also increase however, so not clear cut. Some treatments prolong life, but not make well enough to work - increased costs. some can allow return to work, improving experience. Motivation to return to work is key factor in controlling claim continuation rates. Policy conditions are key to ensure experience is within control - Financial incentive to return to work. Rehabilitation in claims management significant impact on recovery rates. Large benefit claims tend to last longer than small amounts.
1.4. Important if benefit paid out in form of an annuity. LTCI - benefit cease if policy holder recovers or dies. Unlikely given claim conditions and age, so usually ignored IP - stop benefits if recovers, dies, or expires. Rate of recovery or rate of continuation is crucial.
2. 4. Mortality
2.1. Data
2.1.1. Should be taken from recent experience
2.2. Significance of mortality assumption
2.2.1. Non claiming vs claiming policyholders
2.2.1.1. Separate rates of mortality, healthy, fr IP, LTCI and CI and those among those in claim for IP and LTCI. Non claimants, will probably use standard mortality tables, but allow for improvmements. for IP and LTCI, mortality rates require more effort, as mortality of sick lives less predicatble.
2.2.2. Claims within survival period
2.2.2.1. Specialist consideration to surviorship requirement for stand alone CI - period of time required to stay alive following disease diagnosis - allowance usually allowed for on effect of mortality during survivorship period.
2.2.3. Policies without a death benefit
2.2.3.1. less significant. Fall in mortality rates, has little impact on CI rates. claim costs are more sensitive to mortality improvements. Where policy conditions do not have a significant death benefit, its important mortality isn't over estimated as contract will be under costed, only worth over stating if significant death benefit.
2.2.4. Significance depends on if considering: -non claiming or claiming PH -claims within survival period -Policies without a death benefit
3. 5. Lapses
3.1. Data
3.1.1. recent experience, contract, or similar not enough experience, industry wide - market data less useful for lapse as more unpredictable as a lot of influences, heterogeneity between companies- target, distn, prod design,benefits. Influence of economic environ, competition customer service. Own data depend on distn and after sales service - other source short term measure. Assess results to see if been impacted by adverse economic situation Consider if people can afford to keep policy going and if there is any cash available by surrendering - or become paid up
3.2. Grouping the data
3.2.1. calculate lapse experience early as possible, split by distribution channel if necessary. Subdivisions: Pol type, series - past pols with different wordings, sales channel, agent, pol durn, prem size, ben size, prem freq, prem method, age, gender, smoker,
3.3. Adjustments
3.3.1. Big difference vs other assumption derivation is the influence of human behavior of future experience - choice. Economic, political, commercial, awareness Ensure assumptions derived from analysis reflect expected experience of lives being rates. Who initiates sale, sales practice - clients put under less pressure to sell particular pol - size, type. info given on sale. Fin soph - perceptions. target markets
3.4. The effect of lapses
3.4.1. *Early lapse causes financial loss when premiums not suffice to cover initial strain (comm and expenses) *No SV and reserve accumulated - benefit to insurer (accumulated asset share). Profit or loss - SV - AS *May be selective - such that healthier lives more likely to lapse leaving worsening propensity to claim - if indemnity prod, larger claims more frequently. PMI lapse rates correlated to claims - lowest claiming, highest lapse, easily get cover elsewhere. *Should use modelling to estimate susceptibility of prod design to lapse
3.5. Lapse and reentry
3.5.1. Problem where competitive pressures drive premiums down. Issue for long term products - CI, LTCI, IP
3.6. Reflect future experience. Each product type, different experience. PMI renewal - prem increase, state provision, confidence in economy - factors affecting ongoing desirability of having insurance.
4. 1. Main Demographic assumptions
4.1. Data
4.1.1. Morbidity and mortality usually adjusted table rates - depending on if table available. UK, have CI and IP, but not LTCI due to data, PMI reluctant to disclose details. No of years so volume is adequate, but excessive heterogeneity due to changes over time is not introduced. Large pool vs being appropriate. Generations, very different experience - morb usually based on 3 to 4 years data. May need to turn to industry sources, reinsurers, consultancies if appropriate for parameter. Trends can be shown from sources, own data will have statistical variation.
4.2. Grouping the data
4.2.1. *Divide into homogeneous groups, subject ot adequate levels being retained within each cell. *redibility v heterogeneity, if analyse male and female together, also reflecting proportions.
4.3. Calculating the rates
4.3.1. Adjustment to standard table will be of simple form, scale and shift rate if required. -own data scares, graduation unreliable inaccurate over ages ranges -can make errors -reference to table makes easier Compare experience to std table Can use own experience or similar class to make adjustment.
4.4. Adjustments
4.4.1. If adjusted rated expected to have different experience from which data relates, may need further adjustments- changes to target market, disnt channel, underwriting. No pre-standardised data exsists for healthcare, so must gather less relevant information from other sources.
4.5. Morbidity - CI incidence rate, IP inception and recovery, LTCI transition probabilities, PMI incidence rates Mortality Lapse rate Regulatory authorities will constrain choice of parameters. Expected future experience will depend on: Target market - distn channel, underwriting controls, claims control, policy wording - covered diseases, expected change in experience since last investigation.
5. 2. Claim Incident rates
5.1. Data
5.1.1. Chose most suitable, incidence rates or have to calculate from exposed to risk.
5.2. Grouping the data
5.2.1. Break rates down into discrete risk cells, can combine after due analysis. Risk cell - collection of lives - reduce risk of antiselection by offering appropriate terms. -May need to combine so get useful results due to volume, combining relatively similar morbidity experience, but not exactly the same. Trial and error followed by comparison of rates can help determine most appropriate combinations. Splits: age, gender, smoker, occupation, size of benefit selected, class of product - rider, stand alone, Individual or group, distribution source, region - for group risks, duration in force, duration since current benefit, underwriting status - standard or special terms. Some factors may be prohibited, but still allow for internal company analysis. PMI ruled out gender - although claims differential can be very significant. Smoker not prominant in PMI, generally by age. Short term factors for incidence: age, gender, smoker occupation, class of product, benefit level, individual or group, distn source, region. for premiums: age, occupation, class, benefit level, individual, distn source, region.
5.3. Calculating the rates
5.3.1. If incidence is deemed combo of propensities, incidence rate is is derived from combo of probabilities - CI and PMI in particular CI Rates derived separately for each main class - cancer, heart attack, stroke, total permanent disability, combined to produce global incidence rate. Might not be suitable for tiered benefits or where multiple claims are possible. PMI IP might use past claims experience as a risk factor.
5.4. Adjustments
5.4.1. Social and economic influences
5.4.1.1. Govt promises in welfare
5.4.1.1.1. More generous state in relation to long term disability support and acute medical provision, more difficult to retain business
5.4.1.2. Pessimism in the economy and the levels of unemployment
5.4.1.2.1. Take advantage of group PMI when employed, higher incidence. Is incentive to go back to work IP - especially self employed if clients reduce. Unemployment and lapse and renewal
5.4.1.3. high inflation
5.4.1.3.1. Price of PMI, reduce renewal rates, PH choosing lower benefits, selective lapsing, especially if IL premiums. Own future experience vs historical: -target market - could be driven unintentionally - fiscal reasons -Distn channels -Territory targeted -impact of medical science - rate of diagnosis of CI -state health care -selective withdrawals underwriting practice -claims control -Policy wording -Economy
5.4.1.4. Propensity to claim and continue paying premiums depend on several social and economic factors: -State welfare provision (PMI alternative) -Economic wellbeing of country -Inflatio generally
5.4.2. Make rates appropriate to products and time frames for which they will be sold. improving and worsening - reflect in premiums Trends most relevant if derived from insurance market.