Chapter19-Data

Question 1

Framework

Answer 1

1 Define ‘personal data’
2 Data Protection Act
3 Sensitive personal data
4 Big data
5 Data governance
6 Risks of not having adequate data governance procedures
7 Key risks when using data
8 Algorithmic trading
9 Principle of using data
10 Sources of data (TRAINERS) *
11 Causes of poor data quality and quantity
12 Ways of ensuring good quality data from the proposal and claims form
13 Importance of proposal form at time of claim
14 Importance of retaining past policy and claim records
15 Problems with data for employee benefit schemes
16 Sources of data for a valuation of a benefit scheme
17 Reconciliation checks on data
18 Cross checks on data
19 Reasonableness checks
20 Spot checks
21 Problems with using summarised data
22 Industry-wide collection schemes in the uk
23 Reasons why industry data is not directly comparable
24 Other problems with industry data
25 Risk classification
* Uses of data

Question 2

Sources of data

• Uses of data

Answer 2

Tables, eg actuarial mortality tables

Reinsurers

Abroad (data from overseas contracts)

Industry data

National statistics

Experience investigations on the existing contract

Regulatory reports and company accounts

Similar contracts

*Uses:

Administration

Marketing

Premium rating, product pricing, determining contributions

Setting provisions (i.e. setting aside reserves to meet future benefit payments and future expenses

Experience analyses

Investment

Accounting

Risk management, including using underwriting and reinsurance

Management information

Question 3

Reconciliation checks on data (4)

Answer 3

 Reconciling the total number of members / policies and changes in membership / policies using previous data and movement data

 Reconciling the total benefit amounts and premiums and changes in them, using previous data and movement data

 Where assets are held by a third party, reconciliation between the beneficial owner’s and the custodian’s records

 Reconciling shareholdings at the start and end of the period, adjusted for sales and purchases, and bonus issues

Question 4

Cross checks on data

Answer 4

 Checking movement data against accounting data, eg benefit payments

 Checking membership data against accounting data, eg contributions

 Checking asset data against accounting data, eg investment returns

 Full deed audit, for example checking title deeds to large real property assets

Question 5

Reasonableness checks

Answer 5

 Checking the average sum assured or premium looks sensible for class of business

 Checking the average sum assured or premium against previous data

 Checking for unusual values, impossible dates or missing records

Question 6

Spot checks

Answer 6

 Random checking of individual member or policy records

 Checking individual assets or liabilities exist / are held on a given date

 Checking that the correct value of an asset or liability has been recorded

Question 7

Reasons why industry data is not directly comparable

Answer 7

Different geographical or socio-economic markets

Different policies (ie cover, terms and conditions)

Different sales methods

Different practices, eg underwriting and claims settlement processes

Different nature of data stored

Different coding of risk factors, eg definition of a smoke

Question 8

Other problems with industry data (4)

Answer 8

1. Less detailed and flexible than internal data
2. More out-of-date than internal data
3. Data quality depends on the quality of the data systems of all its contributors
4. Not all organisations contribute, and those that do may not be representative of the market

Question 9

Big data

Answer 9

1. Data sets are very large.
2. Data is brought together from different sources.
3. Data can be analysed very quickly, for example in real time.