Chapter 30 Monitoring & feedback into the control cycle Flashcards
What role does monitoring experience play for a health insurance company? (4)
- forms part of the actuarial control cycle
- management of risk will depend on adequate knowledge of the business in force and its deviation from expectation in the
- pricing, and
- subsequent reserving calculations
List reasons for monitoring experience as part of the control cycle
(7)
- Update assumptions for future experience
- Provide management information to aid business decisions.
- Monitor changed trends in experience and take corrective action
- Monitor actual compared to expected experience and take corrective actions as needed
- Make more informed decisions about pricing and adequacy of reserves
- For underwriting can: check effectiveness of procedures, identify anti-selection
- For reinsurance can: assess need for reinsurance, calculate profit share entitled
List some reasons a health insurance company may need assumptions regarding future experience (6)
- Model office work
- EV work
- profitability monitoring
- financial projections
- determining reinsurance requirements
- Pricing
- Valuations
List ways in which experience analysis may help provide management information and take corrective actions (5)
By helping management to identify
- profitable
- products
- sales channels
- markets
- effecient sections of business
- successful investment strategies
Types of experience monitoring: intro
Briefly describe 2 ways in which experience monitoring can be done for a particular factor of interest (2)
Experience monitoring can be done either:
- directly, or
- analysis of surplus (AoS)
Types of experience monitoring: additional
Briefly describe 2 ways in which experience monitoring can be done for a particular factor of interest:
- directly (4)
- via analysis of surplus
Experience monitoring can be done either:
Directly
- explicit investigation of the factor(s) of interest
- want to directly analyse morbidity experience, so do an analysis for this explicitly
- for the factor of interest (eg morbidity) results from a direct experience investigation will tell us how much actual morbidity differs from expected morbidity
- may also be called an ‘actual vs expected’ investigation
Analysis of surplus
- can investigate impact of any difference between actual and expected by considering the financial impact thereof, by performing an analysis of surplus
- AvE investigation would tell us “actual <> expected”
- …but analysis of surplus would importantly tell us
- financial impact of difference of AvE ie which component is responsible for surplus arisen
- where we really need to take remedial action vs where we can be less worried of differences of AvE
Types of experience analysis: direct investigations
List the types of experience investigations an actuary might conduct (7)
- New business
- Renewal rates
- Mortality, sickness, and other contingencies
- Claim amounts
- Persistency/withdrawal rates
- Expenses
- Investment return
What are the general steps involved in an experience analysis? (broadly speaking) (4)
- Decide what type of investigation needs to be done
- direct
- analysis of surplus
- Gather required data
- Conduct analysis
- Use results
List the key points to consider regading the data required for monitoring experience (4)
- consider basic requirements the data must fulfill
- being able to split the data into homogenous groups
- consider the period over which data is collected, as this is very important
- consider the level of detail required and what this depends on
Discuss the data required for monitoring experience, considering:
- basic requirements of good data (3)
- splitting data (2)
- period (2)
- level of detail (2)
- Basic requirement is for data to be
- of sufficient volume
- consistent
- adequate to deduce trends and future experience
- Data should be split into homogenous groups
- according to relevant risk factors
- balance between homegeneity and credibility
- Period over which data is collected is very important
- sufficiently long time period for enough data volume
- …but too long time period, might not give info about recent experience
- Level of detail depends on
- volume of data available
- ideally want split at least for different contract classes
What do we mean by ‘big data’ and how have technical developments changed the insurance landscape in this regard?(2)
Give an example of big data (1)
Big data
- big data essentiallly refers to large volumes of data
- technical developments => insurers can handle/analyse large volumes of data more easily
Big data example
- banks with insurance subsidiaries selling insurance mostly to own customers (‘bancassurers’) amass large volumes of additional data on the insurance customers eg personal spending habits and travel locations
List some advantages of big data (6)
Big data advantages
- allow better understanding + analysis of risks…
- …hence better predict future behaviour
- develop more sophisticated + detailed risk classification…
- …allowing for greater ability to select preferred risks
- drive better experience through monitoring
- earlier identify changes in individual risks
- being able to intervene/influence PH behaviour
- other data sources
List some disadvantages of big data (5)
Big data disadvantages
- reputational damage
- privacy concerns
- data protection failures
- regulation changes
- regulator preventing certain data being used
- fines for misuse of data
- data issues
- collected data may be inaccurate, incomplete, or irrelevant
- modelling risk: complex models=> choice of wrong model
- expenses: collecting/analysing data vs benefits
Analysis of new business
What is compared when performing an experience investigation which analyses new business? (1)
What is the purpose of new business analyses? (4)
When analysing new business the health insurer will…
- …compare/monitor sales against targets
Purpose
- Check strains caused by volume of new business sold against capital set aside for this purpose
- Check mix of business in each significant homogenous cohort against pricing assumption
- Check staffing levels (numbers, competence) against those required by business written
- Check commissions paid against those assumed
Analysis of renewals
How might a health insurer review its experience of renewals? (2)
From what viewpoints will analysis of renewals be useful for? (3)
Under what circumstances are renewals quite important? (2)
Renewal experience should be reviewed for policies where renewal is an option eg PMI
- lapse rates will be compared with those assumed/used in assumptions
- analysis can be done by region, policy type, distribution channel
Such analyses will be important from the viewpoints of
- sales management,
- commission clawback, and
- marketing impact
The impact of lapses/renewals is quite important in terms of
- …profitability…
- …where pricing bases have amortised initial costs over a number of years of renewal.
Mortality, sickness and claims incidence investigation: groupings
List the classifications/groupings by which data (both claims and exposed to risk) could be ideally sub-divided for the purpose of analysing the mortality, sickness, or claims incidence experience (10)
Most useful classifications would be (where relevant):
- type of contract
- benefit conditions (eg deferred period)
- age
- sex/gender
- duration from policy inception
- smoker/non-smoker status
- underwriting status
- source of business
- occupation
- location
Mortality, sickness and claims incidence investigation: PMI and CI groupings
List additional factors by which data could be grouped/classified for the purpose of an experience investigation done on mortality, morbidity, claim incidence rates, and other contingent events
- for PMI contracts specifically (4)
- for CI contracts (2)
For PMI specific contracts, we can also consider
- cover option
- type of benefit
- amount of excess or co-payment level
- NDC level
For CI contracts, it would also be desirable to break the investigation down according to
- the various illnesses (cause of claim) involved
Mortality, sickness and claims incidence investigation: general process
Briefly list the process of analysing experience related to mortality, morbidity, and claims incidence rates for healthcare insurance (10)
What considerations could be made when adjusting the claims data? (6)
Collect necessary actuals data - should be quality data
- accurate, volumous, consistent, useful to deduce trends
Group data according to groupings necessary
- retaining enough size per group
Create summary statistics
- analyse exposure and analyse claims
Adjust data where necessary
- base claims data likely to be incomplete, or unrepresentative due to delays in reporting and or processing
Consider the following when adjusting data
- …heterogeneity,
- unsettled, unreported, re-opened claims
- large/exceptional claims
- changes in types of claim
- changes in development patterns
- seasonality pattern of claims
Mortality, sickness and claims incidence investigation: general process, summary stats
What is the most important principle to follow when analysing claims and exposure? (4)
Most important principle is that
- claims and exposure must correspond
- claims included in analysis must be related exactly to claims in the groupings that could have arisen, in terms of
- periods,
- policies