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19 - Data filtered Flashcards

1
Q

What is Personal data

A

Personal data is information which would allow an individual to be identified, either on its own or when combined with other information

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2
Q

Examples of personal data

A 
Name
Address
Personal email address
Occupation
DOB 
Health status
Race or ethnicity
Criminal record
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3
Q

What is Sensitive personal dat?

A

Sensitive personal data is information which is more private to the individual
Its disclosure to others without consent could cause the individual a high level of distress or damage

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4
Q

Examples of sensitive personal data

A 
o Racial or ethnic origin
o Political opinions
o Religious or other similar beliefs
o Membership of trade unions
o Physical or mental health condition
o Sexual life
o Convictions, proceeding and criminal acts
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5
Q

Under what conditions might Sensitive personal data be processed

A
  1. The data subject has given explicit consent
  2. It is required by law for employment purposes
  3. It is needed in order to protect the vital interests of the individual or another person
  4. It is needed in connection with the administration of justice or legal proceedings
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6
Q

What is the main concern of using / transferring data across international borders?

A

The legislation around data handling may be more stringent in one of the two countries and organisations need to take extra care to not breach local standards.

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7
Q

Aside from criminal action and fines, what is another damaging effect of data breaches occurring within a company’s data bases?

A

Damage to reputation and the ability to retain and attract clients.

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8
Q

What is data governance?

A

Data governance is the overall management of the: availability, usability, integrity and security of data

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9
Q

Give the aspects that a data governance policy should aim to cover. (5)

A
  1. The specific roles and responsibilities of individuals in the organisation with regards to data.
  2. How an organisation will capture, analyse and process data.
  3. Issues with respect to data security and privacy
  4. The controls that will be put in place to ensure that the required data standards are applied
  5. How the adequacy of the controls will be monitored on an ongoing basis with respect to data usability, accessibility, integrity and security.
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10
Q

Give the data governance risks (4).

Failure to have adequate data governance policy can lead to?

A
  1. Legal and regulatory non-compliance
  2. Inability to rely on data for decision making
  3. Reputational issues
  4. Incurring additional costs
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11
Q

Give a data concern around mergers and acquisitions. (3)

A
  1. Should data be combined into one system
  2. Which company’s system to use
  3. Data aggregation issues.
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12
Q

Give the main risks associated with data. (6)

A
  1. The data are inaccurate or incomplete
  2. The data are not credible due to being insufficient volume, particularly for the estimation of extreme outcomes.
  3. The data are not sufficiently relevant to the intended purpose
  4. Past data may not reflect what will happen in the future.
  5. Chosen data groups may not be optimal
  6. The data are not available in an appropriate form for the intended purpose.
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13
Q

Why may past data not be an accurate reflection of future experience.

A
  1. Past abnormal events
  2. Significant random fluctuations
  3. Future trends not being reflected sufficiently in past data
  4. Changes in the way in which past data was recorded
  5. Changes in the balance of any homogeneous groups underlying the data
  6. Heterogeneity with the group to which the assumptions are to relate
  7. The past data may not be sufficiently up to date
  8. Other changes
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14
Q

What is big data?

A

Big data comprises very large data sets, often brought together from different sources, and which can be analysed very quickly

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15
Q

State the data protection principle which may be difficult to meet when using big data

A

Personal data should be adequate, relevant and not excessive for the purposes concerned.

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16
Q

How can companies avoid big data being excessive and personal for the given purpose?

A

Anonymisation can be used to ensure that the data is not considered to be personal data.

17
Q

List the main uses that actuaries make of data.

A
  1. Premium rating, product pricing and determining contributions
  2. Setting provisions
  3. Experience analysis
  4. Risk management - underwriting and reinsurance
  5. Investing
  6. Accounting
  7. Management information
  8. Marketing
  9. Administration
18
Q

List the key data required for active members when valuing a pension scheme

A
  1. Membership ID / number
  2. Date of birth
  3. Date of joining employer
  4. Date of joining the scheme
  5. Date / age of retirement
  6. Current salary
  7. Salary scale / growth assumptions
  8. Category of membership
  9. Dependents - marital status
  10. Age of dependants
  11. Data from previous valuations for reconciliations
19
Q

Outline the design features of a good proposal form.

A
  1. Collects data at an appropriate level - including data that are not currently used but may be used in the future
  2. Be clear and unambiguous - to capture the correct information
  3. Have inputs that are quantitative as far as possible
20
Q

Give a design feature of the claims form in order to store good quality data.

A

Should be clear and unambiguous and link to the proposal form - to cross check information

21
Q

Give features of data inputting processes that can ensure that good quality data is stored by a company. (5)

A
  1. Inputs should be in the same order as the proposal form
  2. Staff that are inputting data should be trained
  3. Financial incentives for accurate inputting
  4. Data systems should have data validation checks - blank entries and sensible entry values
  5. Send policyholders copies of the key information in order to check all values are captured correctly
22
Q

Give the data system features that can help ensure that good quality data is stored by an insurance company.

A
  1. The system should be capable of storing information so that historical data is available for future pricing exercises
  2. System should be robust yet flexible
  3. System should be secure - restricting access of people who can manipulate data
  4. Regular checks of data movements and changes
  5. Single integrated systems can make data handling easier
23
Q

List reasons why claims data might not be directly comparable between different general insurance companies.

A
  1. Organisations operating in different geographical or socio-economic sections of the market
  2. Different policies being sold - policy conditions or perils
  3. Products being sold by different sales methods or to different target markets
  4. Differing underwriting standards at the initial claims stage
  5. Different companies assessing risk differently - different rating factors
  6. Data being stored or recorded differently or relating to different time periods
24
Q

Describe the issues with using industry data.

A
  1. Supplied data may be inaccurate or incomplete
  2. May be out of date
  3. May not be relevant to the intended purpose
  4. Data may not be available in intended format
  5. Chosen data groups within the industry-wide data may not be optimal
  6. The coding used for the factors by which the data is split may vary between pension schemes
  7. May have different definitions of benefits
  8. May not be detailed enough data
  9. Data may be less flexible than is required
  10. Data may not be credible at extreme conditions
  11. Data may not reflect what will happen in the future
25
Q

Describe how industry data may not be relevant for a benefit scheme.

A
  1. Not originally collected for the intended purpose
  2. May not be accurately comparable
  3. Employer operates in a different geographical region
  4. Has a different socio-economic mix of members than the industry average
  5. The nature of the benefits is very different to industry averages
  6. The extent of voluntary vs compulsory membership differs from the industry average
26
Q

List the reasons that past data may not reflect future experience.

A
  1. Past abnormal events
  2. Significant random fluctuations
  3. Future trends not reflected in historical data
  4. Changes in the way in which past data was recorded
  5. Changes in the balance of homogeneous groups underlying the data
  6. Other changes such as medical, social or economic.
27
Q

What is algorithmic decision making?

A

Algorithmic decision making refers to investment trading decisions that are automated so that they take place without human interventions

28
Q

Give the advantages of algorithmic decision making.

A
  1. Faster
  2. More efficient
  3. Possibly lower dealing costs on trades
  4. It is possible to implement complex trading strategies
29
Q

Give the disadvantages of algorithmic decision making.

A
  1. Could be errors in the algorithm or the data or parameters - especially when a large number of trades could be implemented in a short period
  2. Algorithm may not operate in adverse conditions
  3. Trading may be suspended before algorithmic trading can be completed in turbulent markets
  4. Could have adverse impacts on the financial systems
30
Q

Give the main sources of data for an insurance company.

A
  1. Publicly available data
  2. Internal data
  3. Industry-wide data collection
  4. Reinsurance data
31
Q

Give the main issue with using internal data.

A

There may not be sufficient data for launching or pricing a new product or target markets.

32
Q

Give some advantages of using industry-wide data

A
  1. Can compare experience with that of the industry as a whole
  2. Can provide an indication of the ways in which competitors attract policyholders through differences between the company and the industry-wide data.
33
Q

How can individual assets data be checked:

A
  1. Check that a liability or asset exists on a given date
  2. Check that a liability is held or an asset is owned at a given date
  3. Check that when an event is recorded, the time of the event and the associated income or expenditure are allocated to the correct accounting period
  4. Check that data are complete – no unrecorded liabilities or assets
  5. Check the appropriate value of an asset or liability has been recorded
34
Q

Describe the main data checks that actuaries can use.

A

RECONCILIATIONS

  1. Reconciliations of member / policy numbers
  2. Reconciliations of benefits and premiums
  3. Reconciliation of beneficial owner and custodian records where assets are owned by a third party

CONSISTENCY CHECKS

  1. Consistency of asset income data and accounts
  2. Consistency of salary related contribution and benefit levels with the accounts
  3. Consistency between average sum assured and premium for each class, and when compared with previous investigations
  4. Consistency between start and end period shareholdings adjusting for sales etc
  5. Consistency between actuary and custodian’s records
  6. Consistency between contributions and active members
  7. Consistency between pension benefits and number of pensioners

SPOT CHECKS

  1. Records picked at random for spot checks.
  2. Look for very high or very low values/ unusual values
  3. Full deed audit for certain assets, eg property
  4. Validity of dates
35
Q

Give the main circumstances under which there may be a lack of ideal data.

A
  1. Insufficient volume to provide credible results

2. Data captured at insufficient details

36
Q

Describe the main issue with using summarised data.

A

The records cannot be validated which makes error detection impossible

37
Q

Outline the issues with provisions that are too large

A
  1. The funding level will appear to be lower than it actually is
  2. Capital may not be used efficiently
38
Q

Outline the issues with provisions that are too small

A
  1. Over time it will become apparent that additional money is required
  2. Worst case is insolvency

3, Profits will be recognised earlier and the payment of tax will be accelerated4. Inappropriate business decisions can be made.

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