19. Data Flashcards
What is personal data
- information that relates to an individual which would allow that individual to be identified,
- or where the data combined with other information could allow the individual to be identified
Examples of what might be considered as personal data
4
- racial or ethnic origin
- political opinions
- religious or other similar beliefs
- membership of trade unions
- physical or mental health condition
- sexual orientation
- convictions, proceedings and criminal acts
What are the conditions of POPIA relating to personal data
8
- Accountability
- Processing limitation
- Purpose specification
- Further processing limitation
- Information quality
- Openness
- Security safeguards
- Data subject participation
Examples of competition legislation
- Anti-competitive agreements => small number of companies sharing data
- Abuse of dominant market position
What are the characteristics of big data
3
- Very large data sets
- Data brought together from different sources
- Data which can be analysed very quickly e.g real time analysis
What is data governance?
- The overall management of the availability, usability, integrity and security of data employed in an organisation
Data governance policy will set out guidelines with regards to what?
5
ICHES
- Issues with respect to data security and piracy
- Controls 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
- How an organisation will capture, analyse and proces data
- Ensuring that the relavant legal and regulatory requirements in relation to data management are met by the organisation
- Specific roles and responsibilites of individuals in the organisation wrt data
What are the risks relating data governance inadequacy and/or failure
- Legal and regulatory non-compliance
- Inability to rely on data for decision-making
- Reputational issues
- Incurring additional costs (e.g. fines and legal costs)
What are the risks relating to using data
7
N3 CLIP
* Not credible
* Not sufficiently relevant to the intended purpose
* Not available in an appropriate form for the intended purpose
* Chosen data groups are not optimal
* Lack of confidence in the data leads to a lack of confidence in the results
* Inaccurate or incomplete data
* Past data do not reflect what will happen in the future
Outline the key issues and risks arising in relation to the use of algorithmic tools, particularly for trading.
Key issues
- decisions that are unfairly biased
- the algorithm not performing as expected.
Risks
* errors in the algorithm or data used to parameterise the model, leading to losses
* the algorithm not operating properly in adverse conditions
* in very turbulent conditions, trading in individual stocks or markets may be suspended before the algorithmic trade can be completed
* possible impacts on the financial system
What is the overriding principle in relation to all the different uses of data?
- There should be one single integrated data system so that the data used for different applications is consistent
What are the main sources of data
TRAINERS
* Tables
* Reinsurers
* Abroad (data from overseas contracts)
* Industry data
* National statistics
* Experience investigations on the existing contract
* Regulatory reports and company accounts
* Similar contracts
What two main factors cause data to be of poor quality and quantity
- Poor management control of data recording and checking
- Poor design data systems
How can good quality data be ensured from an insurance proposal and claims form?
- Questions clear and not ambiguous
- Questions with quantitative or tick-box answers if possible
- Questions in the same order as in systems for quick processing
- Ask the policyholder to verify a copy of the key information
- All rating factors must be readily identifiable
- Underwriting results should be added to the proposal form
- Forms designed in a way that info can easily analysed and cross-checks made between the two sources
Why is it important, at the time of the claim, to have access to the information given on the proposal form
- To check the validity of the claim
- To update policy information
What is the key problem with data for employee benefit schemes?
- Actuary does not have full control over the data, as it is provided by the sponsor
- Consequently, poor data quality or summarized data
What are the sources of data for valuation of a benefit scheme
- Membership data (sufficiently detailed) on individuals who are currently receiving benefits and those who are entitled to benefits in the future
- Data from previous valuation for reconciliation with current data and validation
- Accounting data for information on asset values, benefit outgo and contribution income, to help check other sources of data or in setting assumptions
- A full listing of the actual assets held
Give examples of reconciliations and cross-checks that can be performed on data
6
- Comparing total members/policies and changes using prior and movement data.
- Matching total benefits and premiums with previous and movement data.
- Cross-checking movement data with accounting records (e.g., benefit payments).
- Validating membership data against accounting records (e.g., contributions).
- Ensuring asset data aligns with accounting records (e.g., investment income).
- Reconciling third-party-held assets between owner and custodian records.
Give examples of reasonableness and other checks that can be performed on data
4
- Verify that the average sum assured or premium is reasonable and consistent for the business class.
- Compare the average sum assured or premium with historical data.
- Identify unusual values, impossible dates, or missing records.
- Conduct spot checks on individual records (members, policies, or assets).
Outline three problems with using summarised data
- Valuation reliability decreases due to limited data validation.
- Summarised data may overlook key differences in benefit structures.
- Summarised data cannot be used to value options and guarantees.
- Suitable only if users acknowledge potential inaccuracies.
List four problems with industry data
- Less detailed and flexible than internal data
- More out of date than internal data
- Data quality depends on the quality of the data systems of all its contributors
- Not all organisations contribute, and those that do may not representative of the market
