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Actuarial Risk Management (A311) > 19 - Data COPY > Flashcards

19 - Data COPY Flashcards

1
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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2
Q

List the eight conditions of the POPI Act in South Africa.

A
  1. Accountability
  2. Processing limitation
  3. Purpose specification
  4. Further processing
  5. Information quality
  6. Openness
  7. Security safeguards
  8. Data subject participation
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3
Q

Describe the POPI Act condition of “Accountability”.

A

The party responsible for processing the data is also responsible for compliance with POPI.

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4
Q

Describe the POPI Act condition of “Processing Limitation”.

A

Information must be processed in a fair, lawful and relevant manner, after consent is given by the data subject.

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5
Q

Describe the POPI Act condition of “Purpose Specification”.

A

Personal information must be collected for a specific purpose.

Record keeping to be destroyed when personal data is no longer relevant or authorised to be held.

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6
Q

Describe the POPI Act condition of further “Further Processing”

A

Further processing must be compatible with the initial collection prupose.

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7
Q

Describe the POPI Act condition of “Information Quality”

A

Data completeness, accuracy and updates to be ensured by holder of the data.

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8
Q

Describe the POPI Act condition of “Openness”

A

Documentation to be maintained on all processing operations and maintaining transparency on data use.

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9
Q

Describe the POPI Act condition of “Security safe-guards”

A

Integrity and confidentiality of personal data must be secured and all processing done only by authorised operators.

Notification to be done on security compromises.

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10
Q

Describe the POPI Act condition of “Data subject participation”.

A

The data subject may request confirmation of personal data held and request correction or deletion of any inaccurate, misleading or outdated information held.

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11
Q

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

A
  1. Damage to reputation

2. The ability to retain and attract clients.

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12
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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13
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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14
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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15
Q

Give the main risks associated with data.

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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16
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
17
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.

18
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.

19
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
20
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
21
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
22
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

23
Q

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

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
24
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
25
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
26
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
27
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
28
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.
29
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
30
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
31
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
32
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.

33
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.
34
Q

Describe the main data checks that actuaries can use.

A
  1. Reconciliations of member / policy numbers
  2. Reconciliations of benefits and premiums
  3. Movement data against accounts
  4. Validity of dates
  5. Consistency of contributions and benefit levels
  6. Consistencies between average sum assured and premium for each class when compared to previous investigations
  7. Consistency of asset income data and accounts
  8. Full deed audit for certain assets
  9. Consistency between start and end periods
  10. Records picked at random for spot checks
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 accelerated
  3. Inappropriate business decisions can be made.

Actuarial Risk Management (A311) (40 decks)

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