Chapter 19: Data Flashcards
What is personal data?
Personal data relates to information in respect of an individual where the individual can be identified, or where the data combined with other information could allow the individual to be identified.
What are the 8 principles around which POPIA is written?
- The party responsible for processing the data is also responsible for compliance with the laws
- Information must be processed in a fair, lawful and relevant manner, after consent is given by the data subject
- Personal information must be collected for a specific purpose.
- Further processing must be compatible with the initial collection purpose
- Data completeness, accuracy and updates to be ensured by holder of the data
- Documentation to be maintained on all processing operations and maintaining transparency on data use.
- Integrity and confidentiality of personal data must be secured and all processing done only by authorised operator.
- The data subject may request confirmation of personal data held and request correction or deletion of any inaccurate, misleading or outdated information held.
Give examples of prohibited actions under competition legislaiton, as well as the consequences of non-compliance
- Anti-competitive agreements
- Abuse of dominant market position
The consequences of non-compliance with competition laws can be significant, including:
* Fines
* Awards for damages
* Disqualification of company directors.
LIst information that could be considered sensitive personal data
Broadly speaking, personal data is considered ‘sensitive’ if its disclosure to others without consent could cause the individual a high level of distress or damage.
- Racial or ethnic origin
- Political opinions
- Religious or other similar beliefs
- Membership of trade unions
- Physical or mental healht condition
- Sexual orientation
- Convictions, proceedings and criminal acts
What are the characteristics of big data?
- Very large data sets
- Data brought together from different sources
- Data which can be analysed very quickly - such as in real time.
What is data governance?
Data governance is the term used to describe the overall management of the availability, usability, integrity and security of data employed in an organisation.
What is a data governance policy?
A data governance policy is a documented set of guidelines for ensuring the proper management of an organisation’s data.
A data governance policy will set out guidelines with regards 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 security and privacy
* 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.
The data governance policy will also provide a mechanism for ensuring that the relevant legal and regulatory requirements in relation to data management are met by the organisation.
List the risks that an organisation can be exposed to if they do not have adequate data governance procedures
- legal and regulatory non-compliance
- inability to rely on data for decision making
- reputational issues
- incurring additional costs
What risks are the company exposed to by not having adequate data governance procedures?
- Legal and regulatory non-compliance
- Inability to rely on data for decision making
- Reputational issues
- Incurring additional costs
What are the key risks relating to data?
- The data is inaccurate or incomplete
- The data is not credible due to being of insufficient volume, particularly for the estimation of extreme outcomes
- The data is not sufficiently relevant to the intended purpose
- Past data may not reflect what will happen in the future
- Chosen data groups may not be optimal
- The data is not available in an appropriate form for the intended purpose
What is algorithmic trading?
Algorithmic trading is a form of automated trading that involves buying or selling financial securities electronically to capitalise on price discrepancies for the same stock or asset in different markets.
Explain the benefits of algorithmic trading
- Algorithmic trading can potentially lead to quicker, more consistent and fairer decisions being made.
- Advance is computing science meant that even larger amounts of data can be collected, stored and analysed much more quickly than in the past. Increasingly algorithmic tools are being used in decision making processes across many sectors.
- Electronic trading has the advantage of increased speed and efficiency of trading, and can result in lower dealing costs on trades.
- Automated trading can potentially facilitate the execution of complex trading strategies that would not have previously been possible.
Explain the risks of algorithmic trading
- Errors in the algorithm or data used to parameterise the model, leading to losses
- The algorithm may not operate properly in adverse conditions
- In very turbulent conditions, trading in individual stocks or markets may be suspended before algorithmic trade can be completed
- Possible impacts on the financial system - failure of one market could impact other markets and asset classes.
What is the overriding principle in relation to all the different uses of data?
The overriding principle is that the data for all tasks should be controlled through one single, integrated system.
If data is controlled by one single, integrated system, then:
* there is a reduced chance of existing data being corrupted
* there is a reduced chance of inconsistent treatment of information, between products or over time
* there is likely to be a better level of control over those who may enter information or amend information
* information will be easier to access, as it will not involve collecting information from several systems
* time will not need to be spent reconciling data from different systems
List 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
When using industry-wide data, there is a potential for distrotions arising from heterogeneity.
This is because data supplied by different organisations may not be precisely comparable. Why?
- Companies operate in different geological or socio-economic sections of the market
- The policies sold by different companies are not identical
- Sales methods are not identical
- The companies will have different practices
- The nature of the data stored by different companies will not always be the same
- The coding used for the risk factors may vary from organisation to organisation.
Other problems with using industry-wide data may be:
* the data will usually be less detailed, or less flexible, than those available internally.
* external data are often much more out of date than internal data
* the data quality will depend on the quality of the data systems of all of its contributors
* not all organisations contribute, and the organisations that do contribute are not representative of the market as a whole
What are the two main factors that cause data to be of poor quality and quantity?
- Poor manegement control of data recording or its verification processes.
- Poor design of the data systems
How can good quality data be ensured from an insurance proposal and claims form?
- Questions should be well designed and unambiguous so that full information is given and so that applications / claims can be easily processed.
- Use questions with quantitative or tick-box answers wherever possible
- Questions should be in the same order as the input into the administration systems, for quick processing of applications / claims
- Ask the policyholder to verify the key information
- All rating factors should be readily indentifiable so that the composition of the final premium can be determined
- Underwriting results should be added to the proposal form
- Forms should be designed so that information can be easily analysed, and cross checks made between the two sources.
Why is it important that insurance companies retain a past history of policy and claims records?
The policy data is necessary to know its exposure to risk during the year, especially if a policy conversion was made through the year.
Holding the basic policy information should enable the automatic checking of the validity of the claim and the updating of the policy information.
Discuss data issues for employee benefit schemes
There may be occasions where the actuary does not have full control over the data available.
In this instance it is important to emphasise to the sponsor the importance of good quality data, which means that the data is:
* complete
* accurate
* up to date
* consistent with previous data
* at the level of detail requested
Data will be required in respect of:
* individuals who have an entitlement to receive a benefit in the future
* individuals who are currently receiving benefits
The data will need to be sufficiently detailed to provide all information that is likely to be financially significant to the level or timing of future benefits.
Give examples of reconciliation checks that can be performed on data
Any equivalent data used when previously valuing benefits will be useful to the actuary as it will enable reconciliations to be performed that help to indicate the validity of the current data.
For example, for a benefit scheme the actuary will examine:
* the membership movements over the inter-valuation period
* changes in averages over the inter-valuation period
* individual records
What are some assertions that should be made and checked by an actuary when using data?
- That a liability or asset exists on a given date
- That a liability is held or an asset is owned on a given date
- That when an event is recorded, the time of the event is recorded, the time of the event and the associated income or expenditure are allocated to the correct accounting period
- That data is complete, i.e. there are no unrecorded liabilities, assets or events
- That the appropriate value of an asset or liability has been recorded.
List possible checks that could be used with these assertions
- Reconcilliation of the total number of members / policies and changes in membership / policies, using previous data and movement data
- Reconcilliation of the total benefit amounts and premiums and charges in them, using previous data and movement data
- The movement data should be checked against any appropriate accounting data, especially with regard to benefit payments
- Checks should be made for any unusual values, such as impossible dataes of birth, retirement ages or start dates.
- Consistency between slaary-related contributions and in-payment benefit levels indicated by membership data and the corresponding figures in the accounts
- Consistency between the average sum assured or premium for each class of business should be sensible and consistent with the figure for the previous investigation
- Consistency between investment income implied by the asset data and the corresponding totals in the accounts.
- Where assets are held by a third party, reconciliation between the beneficial owner’s and the custodian’s records
- Full deed audit for certain assets, such as checking the title deeds to large real property assets
- Consistency between shareholdings at the start and end of the period, adjusted for sales and purchases, and also bonus issues.
- Random spot checks on data for individual members / policies or assets
What are the 2 main circumstances where ideal data are not available?
- There is insufficient volume of relevant data to be credible
- There is insufficient detail captured within the data, i.e. the data available are not in an appropriate form for the intended purpose.
Discuss the use of summarised data
When valuing benefits it may be appropriate to use summarised data instead of detailed membership data in some circumstances.
However, it should be recognised that the reliability of the values will be reduced, as full validation of the data will be impossible.
Additionally, the summarised data may miss significant differences between the nature of benefits that have been grouped together.
It is also unlikely that summarised data could be used to value options or guarantees that may or may not apply on an individual basis
Summarised data is therefore only suitable if such inaccuracy is recognised by the users of the results of the calculations.
