1
Q
Requirements of a good model:
A
- The model being used must be valid, rigorous well documented.
- The model should reflect the risk profile of the business being modelled
- Allow for all significant features of the business being modelled
- Have appropriate input parameters and parameter values
- Be communicable and the output varifiable
- Not be overly complex or time consuming to run
- Be capable of development and refinement
- Be capable of being implemented in a range of ways
2
Q
Steps for developing a Deterministic Model:
A
- Specify the purpose of the investigation
- Collect, group and modify data
- Choose the form of the model
- Identifying its parameters and variables
- Ascribe values to the parameters using past experience and appropriate estimation techniques
- Construct a model based on the expected cashflows
- Check that the goodness of fit is acceptable. This can be done by running a past year and comparing the model with the actual results
- Attempt to fit a different model if the first choice does not fit well
- Run the model using selected values of the variables and using estimates of the values in the future
- Run the model several times to assess the sensitivity of the results to different parameter values
3
Q
Steps for developing a Stochastic Model:
A
- Specify the purpose of the investigation
- Collect, group and modify data
- Choose a suitable density function for each of the variables to be modelled stochastically
- Specify correlation between variables
- Ascribe values to the variables that are not being modelled stochastically
- Construct a model based on the expected cashflows
- Check that goodness of fit is acceptable. This can be done by running a past year and comparing the model with the actual results
- Attempt a different model if the first model does not fit well
- Run the model many times, each time using a random sample from the chosen density function(s)
- Produce a summary of the results that shows the distribution of the modelled results after many simulations have been run
4
Q
Assertions to be examined:
A
- That a liability or asset exists on a given date
- That a liability is held or an asset is owned on a given date
- That when an event is recorded, the time of the event and the associated income or expenditure are allocated to the correct accounting period
- That data is complete, i.e. there are no unrecorded liabilities, assets or events
- That the appropriate value of an asset or liability has been recorded
5
Q
Checking the assertions:
A
- Reconciliation of member numbers
- Reconciliation of benefits and premiums
- Movement data against accounts
- Validity of dates
- Consistency of contributions and benefit levels indicated by membership data with the accounts
- Consistency between the average sum assured or premium compared with previous investigation
- Consistency of asset income data and accounts
- Where assets are held by a third party, reconciliation between the beneficial owner’s and the custodian’s records
- Full deed audit for certain assets, eg property
- Consistency between shareholdings at the start and end of the period
- Random spot checks on the records
6
Q
Possible reasons for heterogeneity:
A
- Companies operate in different geographical or socio-economic sections of the market
- The policies sold by different companies are not identical
- Sales methods are not identical
- The companies will have different practices, eg underwriting, claim settlement
- The nature of the data stored by different companies will not always be the same
- The coding used for the risk factors may vary from organisation to organisation
7
Q
Additional problems with industry wide data
A
- Data may be less flexible/detailed
- Data may be more out-of-date
- Data quality may be poor
- Not all organisations contribute
8
Q
When setting assumptions it is important to:
A
- Consider the use to which the assumptions will be put
- Take particular care over the choice of the assumptions that will have the most financial significance
- Achieve consistency between the various assumptions
- Consider any legislative or regulatory constraints
- Consider the needs of the client
9
Q
When using past data, it is necessary to consider how to deal with:
A
- Abnormal fluctuations
- Changes in experience with time
- Random fluctuations
- Changes in the way in which the data was recorded
- Potential errors in the data
- Changes in the balance of any homogenous groups underlying the data
- Heterogeneity with the group to which the assumptions are to relate
10
Q
Features which make contract design riskier (viewed as an investment)
A
- Lack of historical data
- High guarantees
- Policyholder options
- Overhead costs
- Complexity of design
11
Q
Premiums or contributions should allow for:
A
- Theoretical value of benefits to be provided
- Value of the expenses that will be incurred
- Contribution to profit
- Taxation
- Commission (this may be included as an expense)
- Cost of any capital supporting the product
- Margins for contingencies
- Cost of any options and guarantees
- Provisioning basis
- Experience rating
- Investment income
- Reinsurance costs
