Chapter 11: Data Flashcards Preview

Actuarial F103 - General Insurance > Chapter 11: Data > Flashcards

Flashcards in Chapter 11: Data Deck (49)
Loading flashcards...
1
Q

Actuaries need data for 3 main purposes

A
  • premium rating
  • reserving
  • determining the level of capital to hold
2
Q

2 Main reasons lack of data is more of a problem in general insurance than other areas

A
  • actuaries are relative newcomers to general insurance, so there have been fewer years to establish appropriate data collection for actuarial applications.
  • range and scope of the data needed is greater, in particular given the rapidly changing and competitive nature of general insurance and the complex statistical models that are used to set accurate premium rates.
3
Q

2 Main categories of data sources

A
  • Internal data

- External data from industry sources

4
Q

5 Possible reasons for heterogeneity in industry-wide data

A
  • companies operate in different GEOGRAPHICAL or socio-economic sections of the market
  • policies sold by different companies are not identical
  • companies will have DIFFERENT PRACTICES, eg underwriting, claim settlement and outstanding claim reserving policies.
  • NATURE of the data stored by different companies will not always be the same.
  • CODING used for the risk factors may vary.
5
Q

Problems with using industry-wide data (5)

A
  • heterogeneity
  • much less detailed and less flexible than those available internally.
  • often much more out of date than internal data.
  • quality will depend on the quality of the data systems of all its contributors.
  • Not all companies contribute, unless it is compulsory to do so. Thus the industry data may not be a true reflection of the industry’s experience as a whole.
6
Q

Why might external data be much more out of date than internal data?

A

Because the data takes quite a while to

  • COLLECT,
  • COLLATE
  • and then DISTRIBUTE to the insurers.
7
Q

12 Main uses of policy and claims data by a general insurer

A
  • reserving (including unexpired risk assessment)
  • premium rating and product costing
  • administration
  • financial control and management information
  • experience statistics
  • analysing performance
  • marketing
  • informing investment strategy
  • risk management
  • capital modelling
  • preparing accounts
  • preparing statutory returns
8
Q

Factors that influence the quality and quantity of data (6)

A

between organisations:
– SIZE & AGE of the company
– the current DATA SYSTEM in use, including the use of legacy systems, the integrity of the data system(s)
– the management and STAFF responsible for collecting and maintaining data
– the nature of the organisation, eg direct insurer vs reinsurer.

within organisations:
– depending on the distribution method of the business
– between the different classes of business.

9
Q

Impact of size and age of company on data quantity

A

Large companies will have much MORE DATA available than smaller ones.

They are likely to make more use of their own data, rather than rely largely on industry-wide data.

10
Q

Impact of size and age of company on data quality

A

Large company may have BETTER DATA SYSTEMS in place than a small one.

However, a large well-established company’s computer data system may be outdated and difficult to amend,
while a new small company on the other hand may have a modern system that can be readily adapted to change and allowing better quality data to be recorded.

11
Q

3 Main distribution channels for insurers

A
  • DIRECTLY to customers
  • through BROKERS (intermediaries)
  • through AGENTS (eg banks or building societies that sell a certain insurer’s buildings and contents insurance)
12
Q

3 Reasons for variation by different classes

A

principally due to the different nature of risk, which leads to the following:

  • big variations in claim frequency between classes affects the quantity of claims data
  • the length of the tail of some classes means that it takes considerable time to collect the necessary claims data
  • subjectivity used in underwriting influences the ability to capture risk details.
13
Q

5 stages required in the establishment of a good information system to ensure that good quality data is captured and stored

A
  • consideration of the users’ requirements
  • careful design of appropriate proposal and claim forms
  • ensuring that features of premiums and claims can be recorded
  • consideration of policy and claim information to be collected
  • adequate training of staff
14
Q

4 features of premiums that should be recorded

A
  • amounts
  • timings
  • adjustments to premiums, such as premium discounts and commission paid
  • cross-selling information.
15
Q

6 Features of claim information

A
  • type / cause of claim (peril)
  • the description of the claim event
  • claims paid to date
  • the estimated outstanding claim amount
  • claims handling expenses
  • reinsurance recoveries.
16
Q

attainment of majority

A

where a payment is made once the claimant reaches a CERTAIN AGE that is pre-specified by the courts.

17
Q

case estimate

A

When a claim is notified to the insurer, if the full amount of the claim is not paid immediately then it is common practise to estimate the amount that will still be paid on that claim - referred to as the case estimate.

18
Q

5 Different types of claims payments will include

A
  • indemnity payments made to policyholders
  • compensation payments made to third parties
  • payments to claimants’ solicitors
  • payments to loss adjusters
  • payments of interest
19
Q

4 Reasons for some claims having to be reopened

A
  • it may be purely due to the closure definition used by the insurer (for example, where claims are closed when it is deemed unlikely for there to be future payments relating to the claim)
  • a FURTHER LIABILITY for payment came to light
  • RECOVERY AGAINST a 3RD PARTY was made
  • an ERROR was made in closing the claim originally.
20
Q

9 data requirements for each POLICY record

A
  • unique policy identifier (policy number)
  • person number / code to link to policyholder information (name, ID, sex, etc.)
  • risk definition and details of cover.
  • policyholders’ risk factors should be recorded.
  • status of present record (in-force / expired / cancelled).
  • control dates (policy inception date, cancellation date, date of endorsement etc.)
  • relevant amounts and currencies (exposure/sum insured, premium, excess, etc.)
  • payment dates where applicable
  • administrative details
21
Q

What might the risk definition include (2)

A
  • class and subclass of business

- details of cover (sum insured, excess, etc)

22
Q

9 data requirements for each CLAIM record

A

CLAIM DETAILS

  • unique claim identifier (claim number)
  • details of claim.
  • control dates (dates of incident, date of reporting)

POLICY

  • status of present record.
  • rating factor details.
  • policy number / code to link to policy information

PAYMENT

  • dates and amounts of payments (including claim payments and recoveries from reinsurance and salvage)
  • payment type
  • dates and estimates of amounts outstanding, including movement data as estimates are changed. Records may include estimates of dates of settlement
  • currency of both payments and outstanding claim amounts
23
Q

What might the details of a claim include?

A
  • TYPE of claim (eg in motor, bodily injury or property damage),
  • a claim CAUSE code (type of peril, eg in household, storm damage or burst pipes)
  • an EVENT DESCRIPTION
24
Q

3 Possible payment types (on a claim record)

A
  • indemnity cost,
  • lawyers’ fees
  • adjustors’ fees
25
Q

3 Possible values for the status of a claim record

A
  • open
  • closed
  • reopened
26
Q

In holding the ENTIRE history of policy and claim records, It may be necessary to strike a balance between (4)

A
  • the CAPACITY of the system
  • data STORAGE COST
  • the AMOUNT of data stored
  • the level of DETAIL at which they are stored.
27
Q

6 Examples of potential sources of data errors on claims

A
  • Wrong claim number
  • Wrong claim date
  • Wrong claim type
  • Wrong policy number
  • Wrong risk details
  • Wrong payment dates
28
Q

Consequences of a Wrong claim number

A

Details of the claim could be allocated to the wrong record, and hence to the wrong claim risk group.

This could result in charging incorrect premium rates for the affected risk groups, incorrect capital requirement for a class, etc.

29
Q

Consequences of a Wrong policy number

A

If the claim record picks up its risk details from the wrong policy record, these are likely to be wrong.

30
Q

Consequences of a Wrong claim date

A

This could cause the claim details to be allocated to the wrong year, distorting both the apparent numbers of claims for those years and their development.
A wrong claim date could also mean that the claim will relate to the wrong risk details (if these have changed).

31
Q

Consequences of Wrong payment dates

A

Some claims are settled by several payments, made on different dates. If these are not each identified separately, then development patterns may be distorted.

32
Q

6 Possible sources of data distortion

A
  • changes in claim handling procedures
  • case estimates
  • processing delays
  • large claims
  • return premiums
  • claims inflation.
33
Q

Risk classification

A

the process of
… grouping data according to certain factors
… in order to obtain homogeneous experience within each group with respect to the factor being analysed.

34
Q

Purpose of risk classification (and reducing heterogeneity)

A

By reducing heterogeneity within the data for a group of risks,
… the experience in each group is more stable
… the risks within each group have similar characteristics,
… so that we can use the data appropriately for projection purposes.

35
Q

The effect of inadequate data

A

If the reserves calculated from the data are incorrect, this will distort
… the reported results
… and tax payments.

If the premium rates calculated from the data are incorrect, this could lead to
… unprofitable rates
… or uncompetitive rates,
… or anti-selection.

36
Q

How might data capturing errors be avoided? (5)

A
  • check digits
  • data field integrity checks
  • mandatory fields
  • error reports
  • training of staff
37
Q

Why do industry-wide data collection schemes exist?

A
  • Insurers use the data to CONFIRM OR REFUTE SUSPICIONS from their own data.
  • To be aware of what’s going on in the market place
38
Q

What does CRESTA stand for?

A

Catastrophe Risk Evaluating and Standardising Target Accumulations

39
Q

What is the global CRESTA zone data used for?

A

To help assess risks relating to natural hazards, particularly earthquakes, storms and floods.
Areas are classified into zones according to the likelihood of catastrophes occurring in those zones.

40
Q

What are the main uses of data for:

Senior management

A

Making business decisions

41
Q

What are the main uses of data for:

Accounting department

A
  • Collecting premiums;
  • paying intermediaries,
  • paying claimants,
  • preparing summaries
42
Q

What are the main uses of data for:

Underwriting department

A
  • Premium rating,
  • identifying improvements,
  • evidence of selection,
  • portfolio monitoring
43
Q

What are the main uses of data for:

Claims department

A

Processing and settling claims

44
Q

What are the main uses of data for:

Marketing department

A

Assessing marketing performance and identifying opportunities

45
Q

What are the main uses of data for:

Investment department

A

Monitoring investment performance and opportunities

46
Q

What are the main uses of data for:

Actuarial department

A
  • Premium rating
  • Reserving
  • Assessing solvency
  • Capital requirements
  • Investment strategy
  • Reinsurance strategy
  • Management informations
47
Q

What are the main uses of data for:

Computing department

A

Writing and implementing the IT system

48
Q

What are the main uses of data for:

Reinsurance department

A

Monitoring reinsurance performance and adequacy

49
Q

Check digits

A

Policy numbers are often designed so that the last digit is a check digit.
It is defined by a mathematical formula based on the other digits so that the wrong entering of a policy number is likely to result in the rejection of a transaction being processed rather than it being processed to the wrong policy.