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ARM '20 > 19.Data > Flashcards

19.Data Flashcards

1
Q

Personal data

A

Information allowing individual to be identified, either on its own or in combination with other info

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2
Q

Sensitive personal data

A

Info which disclosure to others without consent can cause high level of distress/damage

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3
Q

Circumstances under which sensitive personal data can be processed

A

Explicit consent given
Required by law for employment purposes
Protect vital interests of individual/individual/another person
Needed for administration of justice/legal proceedings

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4
Q

Characteristics of big data

A
  • Large data sets
  • Brought together from different sources
  • Can be analysed quickly
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5
Q

Big data consideration

A
  • May be exessive/irrelvant
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6
Q

Data governance

A

Overall management of availability, usability, integrity and security of data employed in organisation

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7
Q

Data governance risks

A
  • Legal and regulatory non-compliance
  • Can’t rely on data to make decisions
  • Reputational issues
  • Additional costs from fines etc
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8
Q

Data risks

A
  • Inaccuracte/incomplete
  • Not sufficiently relevant for intended purpose
  • Not reflect future experience
  • Chosen data groups not optimal
  • Not available in appropriate form for intended purpose
  • Not credible due to insufficient volume, particularly due to estimation of extreme outsomes
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9
Q

Reasons why data may not reflect future

A
  • Past abnormal events
  • Once-off impacts
  • Future trends not sufficiently reflected
  • Changes in way past data was recorded
  • Significant random fluctuations
  • Changes in balance of any homogeneous groups
  • Heterogeneity with group to which assumptions relate
  • Not up to date
  • Other changes e.g. medical, social and economic
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10
Q

Algorithmic decision making

A

Automated trading involving buying/selling of financial securities electronically to capitalize on price discrepancies for same stock/assets in different markets

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11
Q

Data requirements

A

Must be controlled through single, integrated system

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12
Q

Advantages of keeping data in a single system

A
  • Reduced chance of corruption
  • Reduced chance of inconsistent treatment of information
  • Better control over who may change or enter info
  • Easier access to info
  • No need for reconciliation between systems
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13
Q

Sources of data

A 
Public data
    - Publsihed accounts
    - Overseas data
    - National statistics
    - Industry data
Internal data
Reinsurer
Industry-wide collection schemes
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14
Q

Reasons why data from industry collection schemes may not be comparable

A
  • Operate in different geo/socio-economic sectors of the market
  • Non-identical policies sold
  • Non-identical sales methods
  • Different practices e.g. underwriting
  • Differences in nature of data stored
  • Differences in coding used to code for risk factors
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15
Q

Other problems with data from industry wide collection schemes

A
  • Data may be less detailed/flexible
  • Data may be out of date
  • Data quality may be poor
  • Not all companies contribute, therefore not representative of whole market
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16
Q

Checks on data

A

• Past data can help verify current data
• Accounting data is useful to help verify income and outgo + value of assets
• Data on individual assets could be checked and verify:
- Existence of assets
- Allowed to be held for valuation purposes
- If valuation is restricted by legislation/regulation

17
Q

Assertions to check quality of data

A
  • Reconciliations of member/policy #s
  • Reconciliations of benefits + premiums
  • Reconciliation of beneficial owner and custodian records where assets are owned by 3rd party.
  • Records picked at random spot checks
  • Consistency between contribution and benefit levels with accounts
  • Consistency between average sum assured + premium for each class, and when compared with previous investigations
  • Consistency of asset income data and accounts
  • Consistency between start and end period shareholdings
  • Full deed audit for certain assets e.g. property
  • Validity of dates
  • Movement of data against accounts
18
Q

Lack of ideal data

A
  • Insufficient volume to provide credible result
  • Data may not be captured at a sufficiently detailed level
  • Actuary may only have summarised data …
  • … this is not suitable for all valuation purposes
19
Q

Sources of poor quality data

A
  • Poor management control of data/verification process

* Poor data system design

20
Q

Mechanisms that can be used to ensure good quality data

A
  1. Proposal form
  2. Claim form
  3. Input of data onto system
  4. Other
21
Q

Proposal form

A

Must be designed to:
 Collect data at appropriate level, incl data not currently used but may be needed in future
 Clear and unambiguous to give correct information
 Have inputs be as quantitative as possible

22
Q

Claim form

A

Must be clear and unambiguous and must link to proposal form so cross-checking can be done

23
Q

Input of data onto system

A

 Inputs must be in same order as in proposal form so person inputting info doesn’t need to interpret info
 Staff inputting info must be well trained
 Financial incentives for accuracy
 System must have validation checks, e.g. checks on
- blank entry fields
- sensible entry values e.g. sensible bounds on ages and sum assureds
 Insurer may send policyholder key info for verification

24
Q

Other features of good data system

A

 System must be capable of storing info, so that historical data can be used for future pricing exercises
 System must be robust and flexibles
 Secure- many can view but not many can amend
 At regular intervals, checks of movement analyses must be carried out and checks of changes in policy details, e.g. how sum assured is changing from year to year

25
Q

Use of proposal form to assess claims

A
  • Cross-check against claims info at time of claim to check validity of form
  • Can also check endorsements (changes to policy)
26
Q

Good quality data

A
  • Accurate
  • Complete
  • Up-to-date
  • At sufficient level as required
  • Consistent with past data

ARM '20 (43 decks)

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