Chapter 19: Data Flashcards
Define personal data
Personal data is 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.
8 Principles which must be followed when processing personal data (POPIA)
Personal data must:
1. Be processed fairly and lawfully
2. Be obtained and processed for specified purposes
3. Be adequate, relevant and not excessive for the purposes concerned
4. Be accurate, and where necesaary, kept up to date
5. Not be kept longer than necessary for the purposes concerned
6. Be processed in accordance with the individual’s rights (compliance with POPIA)
7. Be processed securely
8. Not be processed to another company or country unless that party ensures an adequate level of protection
Examples of what might count as “sensitive personal data”
- Racial or ethnic origin
- Political opinions
- Religious or other beliefs
- Membership of trade unions
- Physical or mental health condition
- Sexual life
- Convictions, proceedings and criminal acts
Give examples of circumstances when sensitive personal information may be legitmately processed
- The data subject has given explicit consent
- It is required by law for employment purposes
- It is needed in order to protect the vital interests of the individual or another person
- It is needed in connection with the administration of justice or legal proceedings
State 3 characteristics of “big data”
- The data sets are very large
- Data is brought together from different sources
- Data can be analyzed very quickly, for example in real time
State 4 risks to a company not having adequate data governance procedures
- Legal and regulatory non-compliance
- Inability to rely on data for decision making
- Reputational issues, leading to loss of business
- Incurring additional costs such as fines and legal costs
Define “data governance” and list guidelines that a data governance policy may cover
Data governance - the overall management of the avialability, usability, security and integrity of data employed in an organization
A data governance policy will set out guidelines with regards to:
1. The specific roles and responsibilities of individuals in the organisation with regards to data
2. how an organisation will capture, analyze and process data
3. Issues with respect to data security and privacy
4. The controls that will be put in place to ensure that the required data standards are applied
5. How the adequacy of controls will be monitored on an ongoing basis with respect to data usability, accessibility, integrity and security
6. Ensuring that the relevant legal and regulatory requirements in relation to data management are met by the organisation
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
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
Define algorithmic trading
This is a form of automated trading that involves buying and selling financial securities electronically to capitalize on price discrepancies for the same stock or asset in different markets.
(can also refer to high frequency trading)
Explain the risks of algorithmic trading
- Errors in the algorithm or data used to parameterize the model, leading to losses
- The algorithm maynot 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
List the key risks associated with using data
- Data are inaccurate or incomplete, leading to erroneous results or conclusions
- Data are not credible due to insufficient volume, particularly for extreme events.
- Data are not sufficiently relevant to the intended purpose
- Historical data do not reflect what will happen in the future (abnormal events, significant random fluctuations, not up to date, homogeneous groups change)
- Chosen data groups are not optimal
- Data are not available in an appropriate form fo the intended purpose
- Lack of confidence in the data leads to a lack in confidence in the results obtained from using it
What 2 main factors cause data to be of poor quality and quantity?
- Poor management control of data recording and checking
- Poor design of 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 administration systems, for quick processing of applications / claims
- Ask the policyholder to verify the key information
- All rating factors should be readily identifiable so that 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 analyzed, and cross checks made between the two sources
Why is it important, at the time of the claim, to have access to information given on the proposal form?
- To check the validity of the claim
- To update policy information
Why is it important that the insurance company retains a past history of policy claims records?
When an insurance company analyses past experience in order to help set future assumptions, several years’ worth of data are often needed in order to give a sufficient volume of data, or to identify trends
What 4 sources of data are useful in order to conduct a valuation of benefits scheme?
- Memebership data on individuals who currently receiving benefits and those who are entitled to in the future
- Data from the previous valuation for reconciliation with current data to help validate the current data
- 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 to enable an accurate valuation of assets and to check whether they are permitted by regulation or subject to regulatory restictions
Give examples of reconciliation checks that can be performed on data
- Reconciling the total number of members / policies and changes in membership / policies using previous data and movement data
- Reconciling the total benefit amounts and premiums and changes in them, using previous data and movement data
- Where assets are held by a third party, reconciliation between the beneficial owner’s and custodian’s records
- Reconciling shareholding at the start and end of the period, adjusted for sales and purchases, and bonus issues
Give examples of cross-checks that can be performed on data
- Checking movement data against accoounting data, e.g. benefit payments
- Checking membership data against accounting data, e.g. contributions
- Checking asset data against accounting data, e.g. investment returns
- Full deed audit, for example checking title deeds to large real property assets
Give examples of reasonableness checks that can be performed on data
- Checking the average sum assured or premium looks sensible for class of business
- Checking the average sum assured or premium against previous data
- Checking for unusual values, impossible dates or missing records
Give examples of spot checks that can be performed on data
- Random checking of individual member or policy data
- Checking individual assets or liabilities exist / are held on a given data
- Checkong that the correct value of an asset or liability has been recorded
Outline 3 problems with using summarized data
- The reliability of the valuation will be reduced, as full valuation of the data is impossible
- Summarised data may miss significant differences between the nature of the benefits that have been grouped together
- Summarized data cannot be used to value options and guarentees that apply at an individual level
Reasons why industry data (FSCA) is not directly comparable
- Different geographical and socio-economic markets
- Different policies
- Different sales methods
- Different practices, e.g. underwriting and claims settlement processes
- Different nature of data stored
- Different coding of risk factors
