Ch 35: Monitoring experience Flashcards
In what different ways can experience monitoring be done?
(1,5)
(2,3)
-
Directly
- want to directly analyse experience with respect to a specific item of interest, so do an analysis for this
- in this chapter we cover following direct investigations
- investigation for mortality + other contingencies
- persistency investigation
- expense investigation
- investment return investigation
-
Analysis of surplus
- investigate financial impact of any difference between actual and expected by performing analysis of surplus
- tells us
- which component is responsible for surplus arisen
- where remedial action can be taken
List reasons for monitoring experience as part of the control cycle
(6)
- Update assumptions as to future experience
- Provide management information to aid business decisions.
- Monitor trends in experience
- Monitor actual compared to expected experience and take corrective actions as needed
- Make more informed decisions about pricing and adequacy of reserves
- Develop earned asset share
DIVERGENCE
List 4 reasons a life company may need assumptions regarding future experience
- Model office work
- EV work
- profitability monitoring
- financial projections
- asset-liability modelling for setting investment strategy
- determining reinsurance requirements
- Pricing
- Valuations
- Setting discontinuance terms
List ways in which experience analysis may help provide management information and take corrective actions (5)
By helping managment to identify
* profitable
* products
* sales channels
* markets
* efficient sections of business
* successful investment strategies
- Info provided @ suff detail for man to act on. => reliable and credible
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
Data required for monitoring experience
Basic requirements of good data (3)
Splitting data (3)
Period (2)
Level of detail (2)
- Basic requirement is for data to be
- credible => Sufficient volume
- consistent => Similar form,source,grouping
- stable => Adequate to deduce trends and future experience
- Data should be split into homogenous groups
- according to relevant risk factors
- balance between homegeneity and credibility
- group hetero lives => averaging of experience
- 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
Data required for monitoring experience
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 (2)
Big data
* refers to large volumes of data, compiled from diff sources and analyzed quickly
* 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
- Supermarkets loyalty schemes
Data required for monitoring experience
List some advantages of big data (3)
Ability to analyze data to better understand risks:
* More accurate rating factors => more detailed risk classification
* eg type of food, level of exercise
* incr Ability to select preferred risks
* => pref rates for lower risks
* eg good healthy, credit,no lapse
* Monitor big data
* => drive better experience
* Early ident of change in risk => intervene / influence PH behaviour
Data required for monitoring experience
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
List the types of experience investigations an actuary might conduct (4)
- Mortality and other contingencies
- Persistency
- Expenses
- Investment return
Analysis of experience: Mortality
List the classifications by which data (both claims and exposed to risk) would be ideally sub-divided for the purpose of analysing the mortality experience
(8)
(2)
(3)
Most useful subdivision of data is by
1. type of contract
2. age
3. sex
4. duration from entry
5. smoker/non-smoker status
6. medical/non-medical status
7. source of business
8. location
Further classifications useful if sufficient data
1. eg location broken down to postal code
2. occupation (at least broad occupational group)
Term is NB for mortality:
1. Medical improvements
2. Std of living
3. New diseases
Analysis of experience: Persistency
List classifications by which data (both claims and exposed to risk) would be sub-divided for the purpose of analysing the persistency experience (10)
Which splits are used in practice (3) ?
Subdivision of persistency experience data would usually be by:
1. Type of contract
* EA usually > persistency than TA
2. Duration in force
* usually lower at start of contract
3. Distribution channel & Target market
* more suitable product sold => better persistency
* more suitable product sold => better persistency
5. Frequency & Size of premium
* + Freq => + chance to stop premiums
* Large Premiums => less affordable
7. Premium payment method
* debit order persistency > cash payment persistency
8. Term of contract
9. Age & Gender
* worse persistency for younger ages
The only first 3 are used as they are particularly important
Analysis of experience: Persistency
Give 3 other factors, external to life company, that may also influence persistency rates (3)
What impact would they have on the persistency analysis?
External factors influencing persistency
- Economic situation
- Competitive situation of product
- Perceived value of product to customer
Impact on analysis
- these factors wouldn’t be used explicitly in analysis
- but may be used to understand/explain patterns in experience
Analysis of experience: Persistency
Process
Outline how withdrawal rates can be determined for each homogenous group of lives analysed (7)
Est rates for each homogenous group
* Divide contracts issued
* into # survive in-force to next policy anniversary => persistency
* Withdrawals
* Exclude deaths and maturities from calc (if material)
* Repeat for subsequent years to obtain # survive and # withdraw
* Calc persistency
* Defined period = #surv year/ tot in-force @ beg year
* Cumulative = #surv year/ tot in-force @ outset of contract
* Examine results
* Same groups => small diff in withdrawal @ diff durations
* Large diff due to small sample size
* Regroup and recalc
* Consolidate data in form relevant to management
Other persistency analysis we may consider
* analysis of paid-up pols may be done as subsidiary part of overall investigation
* may analyse income/partial withdrawal rates, depending on product design
Analysis of experience: Expenses
What is meant by a direct expense and an overhead expense? (2)
What is meant by a fixed expense and a variable expense? (2)
Direct vs overheads
* Direct expenses
* can be directly attributed to a class of business
-
Indirect expense / overheads
- are the balance of expenses that cannot be attributed to class of business
Fixed vs Variable
* Fixed:
* Do not vary with amt of business written
* Remain constant in ST, vary in LT
* eg Staff costs, buildings
- Variable:
- Vary with amt of business written
- eg U/w costs, admin expenses