CHP 30

Question 1

1.1. Main uses of data

Answer 1

1. Admin
2. Accounting
   Statutory returns
3. Investment
4. Financial control, management information
5. Risk management
6. Setting provisions
7. Experience stats
8. Experience analysis
9. Premium rating, product costing, determining contributions
10. Marketing
    All of these functions should be controlled through one single, integrated data system.
    This is not always possible in practice, for small organizations it is easier to ensure the data for different applications are consistent. This is because it is likely that the same small group of people will carry out the applications.

Question 2

1.2. Sources of data

Answer 2

Publicly available data
For some purposes, data will be required on a “big picture” basis – use public data e.g. published accounts, statutory returns.
Internal data
Product providers require data on individual risks they provide cover for. Quantity and quality of this is closely related. If the quantity is too low, data groupings will either be non-homogenous or lack credibility.
If plenty of data is available but the quality is poor, the result will not be reliable.

Question 3

Publicly available data

Answer 3

For some purposes, data will be required on a “big picture” basis – use public data e.g. published accounts, statutory returns.

Question 4

Internal data

Answer 4

Product providers require data on individual risks they provide cover for. Quantity and quality of this is closely related. If the quantity is too low, data groupings will either be non-homogenous or lack credibility.
If plenty of data is available but the quality is poor, the result will not be reliable.

Question 5

2. Data quality

Answer 5

Problems of data quality and quantity could be as a result of:
• Poor management control of data recording or its verification processes
• Poor design of the data systems
The may be as a result of past management – data may take a long time to collect. After implementing a process for maintaining extensive records, it may take years to have enough to analyse.
The availability, quality and quantity of data will vary between organizations, within organizations and between different classes of business.

Question 6

2.1. Ensuring good quality data – the proposal form

Answer 6

When placing a value on liabilities for health care, life and general insurance, the prime source of info is the details on the proposal form. Thus the importance of relevant and reliable info for the system.
Questions must be well designed and unambiguous, this is to get the full and correct information so the underwriting department can process properly.
In particular the result of any medical or occupational underwriting will need to be added. For general insurance the composition of the final premium from various rating factors will be important.

Question 7

2.2. The use of proposal form information when assessing claims

Answer 7

The proposal form and any subsequent changes will need to be held for a number of purposes, including cross checking against claim information at the time of any claim.
Holding basic policy information should enable the automatic checking of validity of a claim and the updating of policy info (e.g. termination of cover in the event of a total loss under a general insurance policy or death under a life insurance policy).
The data requirement will depend on the type of benefit provided.

Question 8

2.3. Ensuring good quality data – the claim form

Answer 8

Thus should be designed with the aim of producing information that can be both analysed accurately and transferred easily to the computer system.

Question 9

2.4. Data to be captured

Answer 9

As well as data relating to current risks covered, it is important to retain the history of past policy and claim records.

Question 10

3. Data issues for employee benefit schemes

Answer 10

Many instances, the actuary does not have control over the data available – e.g. valuing benefits under an employee benefit scheme where the sponsor provides the data. Here it will be important to validate the type of data.

Question 11

3.2. Data requirements

Answer 11

Data will be required to place a value on the benefit entitlements of individuals.
Data will be required in respect of:
• Individuals who have entitlement to receive a benefit in future
• Individuals who are currently receiving benefits
The data will need to be detailed enough to provide all info that will be financially important to the level of timing and level of future benefits.

Question 12

3.3. Verifying current data

Answer 12

Any data used when previously valuing benefits will enable reconciliations to be performed that help validate the current data.

Question 13

3.4. Use of accounting data

Answer 13

A balance sheet and income and expenditure statement may exist where reserves are built up for benefits.
This will provide info about the total value of assets held and also perhaps info about recent benefit outgo and premium/contribution income.
This info can be used to verify other data or when considering assumptions used.
Audited accounts will enable greater reliance to be placed on the figures when verifying data.

Question 14

3.5. Asset data

Answer 14

To place a value on future benefits, a full listing of existing assets is required.
These individual holdings should be checked to make sure they are permitted, or are subject to valuation restrictions imposed by regulation or legislation.

Question 15

4. Checks on data

4. 1. Assertions to be examined

Answer 15

• A liability or asset exist on a given date
• A liability is held or an asset is owned on a given date
• When an event is recorded, the time of the event and the associated income or expenditure are allocated to the correct accounting period
• Data is complete, i.e. there are no unrecorded liabilities, assets or events.
• The appropriate value of an asset or liability has been recorded.

Question 16

4. Checks on data

4. 2. Checking assertions

Answer 16

A decision on how to check the assertions will be taken and the level of detail that will be appropriate.
Possible checks include:
• Recon of the total number of members / policies and changes in membership / policies, using previous data and movement data.
• Recon of total benefit amounts and premiums and changes in them, using previous and movement data.
• Movement data should be checked against any appropriate accounting data, especially with regard to benefit payments
• Check for any unusual values, for example, impossible dates of birth or retirement ages or start dates.
• Consistency between salary related contributions and in-payment benefit levels indicated by membership data and the corresponding figures in the accounts.
• Consistency between the average sum assured or premium for each class of business should be sensible, and consistent with the figure from the previous investigation.
• Consistency between investment income implied by the asset data and the corresponding totals in the accounts
• Where assets are held by a third party, recon between the beneficial owner’s and the custodian’s records
• Full deed audit for certain assets, e.g. checking the title deeds to large real property assets.
• Consistency between shareholdings at the start and end of the period adjusted for sales and purchases, and also bonus issues, etc.
• Random spot checks on data for individual members / policies or assets.

Question 17

5.1. When data is not available

Answer 17

The main circumstance where ideal data is not available is where:
• Data have not been captured at a detailed enough level. E.g. benefit scheme may not record if an employee is manual or clerical – this could have a significant effect if there is a shift in demographic.
• There may not be enough data to give a credible result. E.g. new product or target market or small business.

Question 18

5.2. The use of summarized data

Answer 18

When valuing benefits, it may be appropriate to use summarized data in some instances.
It should be made clear that the reliability of the values is reduced and full validation is impossible.
Added to this, summarized data may miss significant differences between the nature of benefits that have been grouped together – not enough data to split into homogenous groups.
Summarized data is only suitable if such inaccuracy is recognized by the users of the results.

Question 19

6.1. What are industry-wide data collection schemes

Answer 19

Organizations sometimes collect data from all their members for general use. This data cannot be used in place of policy data, but could be used to determine a pricing basis.
E.g. Continuous Mortality Investigation Bureau of the Faculty and institute of actuaries.
The volume of data that can be collected across industry will greatly improve the statistical significance of the resulting analysis.

Question 20

6.2. Potential benefits from using industry-wide data collection schemes

Answer 20

Can compare own experience with industry experience with regards to both the overall level and the pattern of the experience by categories into which data is classified.
Any significant differences point to a need for explanation.
Since an insurer is likely to try and expand by attracting business from its competitors, it may be important to have an indication of the ways in which the characteristics of the business it seeks may differ from the current business it has.

Question 21

6.3. Possible reasons for heterogeneity

Answer 21

Data from different companies may not be precisely comparable because:
• Companies operate in different geographical and / or socio-economic sections of the market
• Policies sold by different companies are not identical
• Sales methods are not identical
• Companies will have different practices e.g. underwriting, claim settlement
• Nature of the data stored by different companies will not always be the same.
• The coding used for risk factors may vary between organizations

Question 22

6.4. Other problems with industry-wide data

Answer 22

• Data will usually be less detailed, or less flexible than internal data
• External data are often much more out-of-date than internal data
• Data quality will depend on the quality of the data systems of all its contributors
• Not all organizations contribute

Question 23

7.1. What is the aim of risk classification

Answer 23

The main aim of risk classification is to create homogenous groups. The reduction of heterogeneity within a group of risks makes the experience in each group more stable and characteristic to that group. This enables the data to be used more appropriately for projection purposes.
This is important when monitoring claims experience, mortality experience etc.
Any heterogeneity in the data could distort the results and could cause setting provisions at an incorrect level. Also could mean that premiums / contributions are incorrect.
The trade-off between homogenous data and enough data in each group to be credible must be considered. Combine groups that are too small to make analysis credible, e.g. at extreme ages for deaths.
Do sensitivity testing to check the effect on the results if data is grouped differently.