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
Eight principles which must be followed when processing personal data
Personal data must:
- Be processed fairly and lawfully
- Be obtained and processed for specified purposes
- Be adequate, relevant and not excessive for the purposes concerned
- Be accurate, and where necessary, kept up to date
- Not be kept longer than necessary for the purposes concerned
- Be processed in accordance with the individual’s rights
- Be processed securely
- 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’
Sensitive personal data can include information related to:
- Racial or ethnic origin
- Political opinions
- Religious or other beliefs
- Membership of trade unions
- Physical or mental health or condition
- Sexual life
- Convictions, proceedings and criminal acts
Give examples of circumstances when sensitive personal information may be legitimately 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 three characteristics of ‘big data’
Big data can be characterised by:
- 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 four 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 the guidelines that a data governance policy may cover
Data governance – the overall management of the availability, usability, security and integrity of data employed in an organization
A data governance policy will set out guidelines with regards to:
- The specific roles and responsibilities of individuals in the organization with regards to data
- How an organization will capture, analyze 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 controls will be monitored on an ongoing basis with respect to data usability, accessibility, integrity and security
- Ensuring that the relevant legal and regulatory requirements in relation to data management are met by the organization 122
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 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..
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 outcomes.
- 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 for the intended purpose
- Lack of confidence in the data leads to a lack of confidence in the results obtained from using it
What two 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 the 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 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 analyzed, 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