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2022: CP1 A311 Actuarial Risk Management > Chapter 19: Data > Flashcards

Chapter 19: Data Flashcards

1
Q

What is personal data?

A
  • Personal data is data that can allow someone to identify a specific individual.
  • Organisations have to respect personal data when making decisions
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2
Q

What are the consequences of breaching data protection acts?

A
  • Criminal offences
  • Prosecution
  • Fine
  • Jail time
  • Reputational damage
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3
Q

One example of how data can be used by competitors, is data is against consumer protection laws?

A
  • Sharing data among competitors to fix prices
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4
Q

What is sensitive personal data?

A

Data that if it is disclosed without the individual’s consent can cause an individual a high level of stress or damage.

Subject to much stricter legislation than normal personal data

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5
Q

List seven examples of sensitive personal data

A
  1. Racial or ethnic origin
  2. Political opinions
  3. Religious or other similar beliefs
  4. Membership in a trade union
  5. Physical or mental health
  6. Sexual life
  7. Convictions, proceedings and criminal acts
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6
Q

What are the three main characteristics of big data?

A
  • Very large data sets
  • Data brought together by different sources
  • Data that can be analysed very quickly
  • Anonymise to protect individuals relates to
  • Data is relevant and not excessive
  • Transparent when they collect data and explain to what use the data will be put - get consent to use data
  • Held securely
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7
Q

What is data governance

A

A term used to describe the overall management of the availability, usability, integrity and security of the data in an organisation

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8
Q

What is the aim of the data governance policy?

A

A document that sets out the proper data guidelines

  • Specific role and responsibility of individuals in an organisation wrt to data
  • How data is captured, analysed and processed
  • issues wrt data security
  • Controls in place to ensure proper data standards apply
  • How the adequacy of these controls will be measured
  • Ensure legal requirements are met
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9
Q

What risks can an organisation face if the governance policy is not adhered to (4)

A

1 Legal and regulatory non-compliance
2 Inability to rely on data for decision making
3 Reputational damage
4 Ensuring additional costs such as fines

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10
Q

What risks are associated with the use of data? (9)

QUERIED

A
  1. Data contains errors or omissions leading to incorrect conclusions being made
  2. Insufficient credible data to provide credible results
  3. There might be enough data to provide credible results but not enough to give a credible estimate of an adverse circumstance, i.e., what occurs in the tails of the distribution
  4. External sources might not be relevant or appropriate for this circumstance
  5. Historical data might not be a good representation of the future experience
    - Past abnormal event
    - Significant random fluctuations
    - Future trends not being reflected in past data
    - Change in the way data was recorded
    - Changes in homogenous groups
    - Past data is not up to date
  6. Difficulties in creating homogenous groups due to
    - Groups being too small to be credible
    - Merging with other groups can cause it not to be homogenous anymore
  7. Data might not be in the appropriate form
  8. Data collected for a specific purpose, so it is not appropriate for this purpose
  9. Lack of confidence in the available data reduce confidence in final conclusion
Q - Quantity (credibility)
U - Up-to-date
E - Errors
R - Relevance (heterogeneity)
I - Incomplete
E - Exceptionals
D - Detail and format
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11
Q

List six areas of actuarial work where data would be required

A
  1. Setting provisions
  2. Pricing/setting contributions
  3. Investment management
  4. Risk management
  5. Management information / financial control
  6. Accounts/ statutory or supervisory reporting
  7. Experience statistics/analyses
  8. Marketing
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12
Q

What makes data poor quality?

A
  1. Errors
  2. Insufficient quantity
  3. Insufficient detail
  4. Lack of relevance
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13
Q

How can errors in data be curbed?

A
  • Asking clear and unambiguous questions on the proposal form
  • Carrying out data checks - ensuring now blanks or impossible values
  • Reconciliation with previous years
  • Cross-checking with other sources
  • Consistency checks
  • Random spot checks
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14
Q

How can an insufficient quantity of data be curbed?

A

Obtain external data - care to ensure it is relevant

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15
Q

How can an insufficient detail of data be curbed?

A

Ensure all required fields are captured

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16
Q

How can a lack of relevance in data be curbed?

A

Collated into homogenous groups (while ensuring data is still credible)

Adjustments to data (especially external data) to fit a specific purpose

17
Q

What allowances can be made to poor quality data

A
  • Include risk margins or contingency loadings - based on actuarial judgement
  • Disclosed to client
  • Warning on the extent to which data can be relied on
18
Q

What needs to be considered when designing an insurance proposal form

A

General

  • Proposal form must generate relevant information as it provides information for a range of purposes
  • Questions should be designed and unambiguous so that the information given is easy to verify and interpret
  • Easy to process by admin and underwriting teams, e.g., occur in the same order as they are loaded onto the system
  • Any medical or occupational underwriting should be added
  • Ask for information pertaining to each rating factor in personal lines GI
  • Information will be sued to cross-check against claims information

Underwriters

  • Sufficient information needs to be gathered for an accurate rating
  • The form should identify risks for which further information will be required, e.g., further information on an illness suffered

Policyholders

  • The questions should be in line with policyholder expectations and market practice
  • Form shouldn’t be too excessive so that it is unattractive to policyholders

Regulator
* Form shouldn’t breach any regulatory guidelines, e.g., asking for genetic testing

19
Q

What assertions can be examined relating to data

A
  • Liability or asset exists on a given date
  • Liability held / asset is owned on a given date
  • Even recorded, the time the event occurs and the associated income or expenditure are allocated to the correct accounting periods
  • Data is complete, no unrecorded liabilities, assets or events
  • Appropriate value of an asset or liability has been recorded
20
Q

How can data be checked?

A

Reconciliations

  • Total members/policies
  • Total contributions/benefits/premiums
  • Beginning + On - Off = End
  • Check movements inaccordance to accounting data

Cross checks

  • Against other sources of data and any discrepancies should be investigates
  • Check against accounting and custodian data
  • Is data valid?
  • How does it compare to previous years’ data

Consistency checks

  • Contributions/Pension benefits/Sum assured paid should be consistent with the number of active members/ pensioners/ premium
  • Average sum assured and average premiums to be consistent, if not we expect data to be missing
  • Investment income should be consistent to the value of assets

Spot checks

  • Unusual values, e.g., adding to many 0s
  • Full deed audit
21
Q

What are the 8 sources data?

TRAINERS

A

T - Tables (mortality tables0
R - Reinsurers
A - Abroad
I - Industry (e.g., companies collecting data from members and sharing it with other members
N - National statistics
E - Experience with existing contracts (internal)
R - Regulatory reports and company accounts
S - Similar contracts (internal)

22
Q

Why should companies have a single integrated data system? (5)

A
  1. Reduce the chance of data being corrupted between different sources
  2. Reduced risk of data being used inconsistently through time
  3. Better controls on who can enter/amend data
  4. No need to reconcile different sources
  5. Information is easier to access
23
Q

Problems with industry data?

DR DONEQ

A

D - Detail insufficient (usually summarised)
R - Risk factors coded in a different way, e.g., use of age bands instead of individual ages

D - Differences between insurers in the industry (heterogenous)
O - Out of date (e.g., collected only every 5 years
N - Not everyone contributes (not a picture of the entire market)
E - Errors in data
Q - Quality only as good as that of contributors

24
Q

Causes of heterogeneity in industry data - cause differences - cause different claim experiences? (8)

A

Differences in:

  1. Econ/Social status
  2. Type of policies sold
  3. Sales method
  4. Practices (e.g., underwriting or claim settlement)
  5. Coding of risk factors
  6. Stored differently
  7. Geographical errors
  8. Contract terms

2022: CP1 A311 Actuarial Risk Management (51 decks)

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