Chapter 19 - Data Flashcards

1
Q

Personal data

A

Allow an individual to be identified
Many protection laws
Sensitive data is more private

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2
Q

Big data

A

Large data sets, different sources, analyzed quickly
May or may not include personal data, use anonimisation
Be transparent when collecting

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3
Q

Data governance

A

Management of availability, usability, integrity and security of data
Should have robust data governance policy otherwise, legal, operational, reputation al and expense risks
Data governance is key during merger and acquisition

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4
Q

Data risks

A
Inaccurate or incomplete
Not credible
Not sufficiently relevant
Past data does not reflect what will happen in the future
Chosen data groups might not be optimal
Data not available for intended purpose
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5
Q

Algorithmic decision making

A

Automated asset trading is example where model risk arises

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6
Q

Data requirements

Sources of data

A

Publicly available
Internal
Industry wide

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7
Q

Industry wide data collection schemes

Not precisely comparable

A

Companies operate in different geographical or socio economic sections
Policies sold by different companies are not identical
Sales methods are not identical
Companies will have different practices
Nature of data stored will not be the same
Coding for risk factors may differ

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8
Q

Industry wide data collection schemes

Additional problems

A

Data may be less detailed/flexible
Data may be more out of date
Data quality may be poor
Not all organisations contribute

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9
Q

Checks on data

A

Recon of member/policy numbers
Recon of benefits and premiums
Movement data against accounts
Validity of dates
Consistency of contribution
Consistency between sum assured and premium
Consisteny of asset income data and accounts
The recon of beneficial owner and custodian records where assets owned by third party
Full deed audit for certain assets
Consistency between start and end period shareholdings
Records picked at random for spot checks

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10
Q

Lack of ideal data

A

Insufficient volume

Data has not been captured at a sufficiently detailed level

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11
Q

Risk classification and reduction of heterogeneity

A

Aim to have homogeneous data since heterogeneity distorts results
Removal of heterogeneity needs to be balanced against having sufficient data in each group

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