Chapter 19: Data Flashcards
Personal data
Relates to information in respect to an individual where the individual can be identified, or where data, combined with other information can be used to identify the individual.
Eight conditions covering the processing of personal information by individuals and companies in South Africa
Which ACT covers this
I nformation quality - Data completeness, accuracy and updates to be ensured by the holder of the data.
O penness - Documentation to be maintained on all processing operations and maintaining transparency on data use
S ecurity safeguards - Integrity and confidentiality of personal data must be secured and all processing done only by authorised operator. Notification to be done on security compromise.
A ccountability - The party responsible for processing the data is also responsible for compliance with POPIA - not just the collector
P urpose specification - 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.
P rocessing limitation - Information must be processed in a fair, lawful and relevant manner, after consent is given by the data subject.
Further processing limitation - Further processing must be compatible with inital collection purpose.
Data subject partcipation - The data subject may request confirmation of personal data held and request correction or deletion of any inaccurate, misleading or outdated information held.
When is personal data considered sensitive
When its disclosure can cause damage or distress to the individual - subject to stricter regualtion than regular personal data
Competition legislation
Limits ways in which data may be put to use. Usually prohibits:
* Sharing of data to particpate in price collusions.
* Monopolies - imposing unfair trading terms such as exclusivity.
Big data
- Very large data sets
- Data brought together from different sources
- Data that can be analysed quickly - such as in real time.
Big data analytics
The process of analysing the large data sets to uncover patterns, trends, correlations and other details that can be used to inform decision-making within an organisation.
Data governance
Describes the overall management of the availability, usability, intergrity and security of the data employed in an organisation
Data governace policy
Documented set of guidelines for ensuring the proper management of the organisation’s data
What will a data governance policy set out guidelines with respect to ?
- The specific roles and responsibilities of individuals in the organisation with regards to data.
- How an organisation will capture, analyse and process data.
- Issues with respect to data privacy and security.
- The controls that will be put in place to ensure that the required data standards are applied.
- How the adequacy of the controls will be monitored on an ongoing basis with respect to data usability, accessibility, integrity and security.
Possible risks that an actuary faces when dealing with data.
- A ppropriate form - Data may not be in the form appropriate for the purposes required.
- L ack of confidence in the available data
- F uture experience -even if there were enough data to estimate the future in normal situations, it might not be enough to estimate the future experience in adverse situations.
- A vailable data may have omissions or errors - leading to erronous results or conclusions.
- S pecific form - The collected data may have been for a specific purpose, and cannot be used for a different purpose.
- H istorical data may not be a good reflection of future experience.
- I nsufficient historical data to credibly estimate the extent of a risk or etimate the lielihood of occurence of that risk in future.
- H omogeneous groups - There are risks when trying to group data into homogenous groups - having small groups, and resulting data not being homogenous
- I ndustry data - Where there is insufficient data, industry data may be used, at the risk of the industry data not being a good proxy for estimating the risk.
Algorithmic trading
A form of automated trading that involves buying or selling of financial securities electronically to capitalise on price discrepencies for the same stock or assetin different markets.
Why should data for an organisation be integrated into one system
- C orruption - Reduced chance of existing data being corrupted.
- R econcilliation - Time will not need to be spent reconciling data from different systems
- A ccess - Information will be easier to access
- I nconsistency = Reduced chance of inconsistent treatment of information.
- C ontrol - Better chance of controlling who can enter and amend information.
Data sources
- Publicly available data
- Internal data
- External sources
- Industry-wide data collection schemes
Sources of data quality issues
- Poor management control of data recording or its verification process
- Poor design of the data systems
Checks on data
- Reconcilliations of member/policy numbers
- Reconcilliations of benefits and premiums
- Movement data against accounts
- Validity of dates
- Consistency of benefit level with the accounts
- Consistency between avaerage sum assured and premium for each class, and when compared to previous investigations
- Consistency of asset income data and accounts
- Full deed audit for certain assets.
- Records picked at random for spot checks