Chapter 19 - Data

Question 1

Define personal data

Answer 1

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

Question 2

What are the 8 conditions POPIA is written around?

Answer 2

SAPS F IOS

• purpose SPECIFICATION; personal info must be collected for specific purposes. info must be destroyed when no longer relevant
• ACCOUNTABILITY; The party responsible for processing data is also responsible for compliance with POPIA
• PROCESSING limitation; Info must be processed fairly after given consent
• SECURITY safeguards; data must be secured and all processing done only by authorised operator
• FURTHER processing limitation; further processing must be compatible with the inital collection purpose
• INFORMATION quality; data completeness, accuracy & updates must be ensured by the holder
• OPENNESS; documentation to be maintained on all processing operations & maintaining transparency on data use
• data SUBJECT participation; data subject may request confirmation of personal data held and can request editing of the data

Question 3

Examples of what might count as ‘sensitive personal data’

Answer 3

PC SPERM

• Political opinions
• Convictions, proceedings and criminal acts
• Sexual life
• Physical or mental health or condition
• Ethnic origin
• Religious or other beliefs
• Membership of trade unions

Question 4

Define ‘data governance’ and list the guidelines that a data governance policy may cover

Answer 4

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:

SCAMS
- SPECIFIC roles of individuals in the organisation w.r.t data
- CONTROLS that will be put in place to ensure standards are upheld
- how an organisation will capture, ANALYSE, and process data
- how the controls will be MONITORED on an ongoing basis w.r.t data useability, accessibility, integrity & security
- issues w.r.t data SECURITY & privacy

Question 5

State four risks to a company not having adequate data governance procedures

Answer 5

1. Legal and regulatory non-compliance
2. Inability to rely on data for decision making
3. Reputational issues, leading to loss of business
4. Incurring additional costs such as fines and legal costs

Question 6

List the key risks associated with using data

Answer 6

I RAP GIF

• data are INACCURATE or incomplete, leading to erroneous results or conclusions
• past data is not sufficiently RELEVANT for the intended purpose because data isn’t precisely comparable across companies
• the data might not be in a form that is APPROPRIATE for the intended purpose
• the data may be collected for a PURPOSE, so it’s not appropriate for a different purpose
• chosen homogenous data GROUPS may not be optimal due to:
  
  • the group being too small for analysis
  • if the data groups merged, it may not be sufficiently homogeneous
• INSUFFICIENT volume of data, which makes it not credible
• past data might not reflect what would happen in the FUTURE due to:
  HARD FROG
• HETEROGENEITY within the group
• past ABNORMAL events
• significant RANDOM fluctuations
• past data may not be up to DATE
• FUTURE trends not being reflected sufficiently in past data
• changes in the way that the data was RECORDED
• OTHER changes e.g. medical, economic
• changes in the balance of any homogeneous GROUPS underlying the data

Question 7

Define algorithmic trading

Answer 7

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

Question 8

Explain the risks of algorithmic trading

Answer 8

1. Errors in the algorithm or data used to parameterize the model, leading to losses
2. The algorithm may not operate properly in adverse conditions
3. In very turbulent conditions, trading in individual stocks or markets may be suspended before algorithmic trade can be completed
4. Possible impacts on the financial system

Question 9

List the main sources of data

Answer 9

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

Question 10

Possible reasons for heterogeneity when using industry wide data

Answer 10

GPS RN P

• companies operating in different GEOGRAPHICAL or socio-economic sections of the market
• POLICIES sold by companies differ
• SALES method may differ
• coding use for RISK factor may differ
• NATURE of data storage might differ
• companies will have different PRACTICES

Question 11

4 other problems with using industry data

Answer 11

LEND

• LESS detailed and flexible than internal data
• EXTERNAL More out-of-date than internal data
• NOT all organizations contribute, and those that do may not be representative of the market
• DATA quality depends on the quality of the data systems of all its contributors

Question 12

What two main factors cause data to be of poor quality and quantity?

Answer 12

1. Poor management control of data recording and checking
2. Poor design of data systems

Question 13

list the traits of good quality data

Answer 13

• complete
• accurate
• up to date
• consistent with previous data
• at the level of detail needed

Question 14

What is risk classification and what is its main aim?

Answer 14

Risk classification – a tool for analyzing a portfolio of prospective risks by their risk characteristics, such that each subgroup of risks represents a homogeneous body of risk.

The main aim of risk classification is to split data into homogeneous groups so that the experience of each group is more stable, and data can be more accurately used, for example to set premiums

Question 15

Why is it important, at the time of the claim, to have access to the information given on the proposal form?

Answer 15

1. To check the validity of the claim
2. To update policy information

Question 16

Why is it important that the insurance company retains a past history of policy and claims records?

Answer 16

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

Question 17

What is the key problem with data for employee benefit schemes?

Answer 17

The actuary does not have full control over the data, as it is provided by the sponsor

The consequences of this may be poor quality or summarized data

NOTE: It is therefore particularly important to validate this type of data

Question 18

What four sources of data are useful in order to conduct a valuation of a benefits scheme?

Answer 18

1. Membership data on individuals who are currently receiving benefits and those who are entitled to in the future
2. Data from the previous valuation for reconciliation with current data to help validate the current data
3. Accounting data for information on asset values, benefit outgo and contribution income to help check other sources of data or in setting assumptions
4. 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 restrictions

Question 19

Give examples of reconciliation checks that can be performed on data

Answer 19

1. Reconciling the total number of members / policies and changes in membership / policies using previous data and movement data
2. Reconciling the total benefit amounts and premiums and changes in them, using previous data and movement data
3. Where assets are held by a third party, reconciliation between the beneficial owner’s and custodian’s records
4. Reconciling shareholding at the start and end of the period, adjusted for sales and purchases, and bonus issues

Question 20

Give examples of cross-checks that can be performed on data

Answer 20

1. Checking movement data against accounting data, e.g. benefit payments
2. Checking membership data against accounting data, e.g. contributions
3. Checking asset data against accounting data, e.g. investment returns
4. Full deed audit, for example checking title deeds to large real property assets

Question 21

Give examples of reasonableness checks that can be performed on data

Answer 21

1. Checking the average sum assured or premium looks sensible for class of business
2. Checking the average sum assured or premium against previous data
3. Checking for unusual values, impossible dates or missing records

Question 22

Give examples of spot checks that can be performed on data

Answer 22

1. Random checking of individual member or policy data
2. Checking individual assets or liabilities exist / are held on a given date
3. Checking that the correct value of an asset or liability has been recorded

Question 23

Outline three problems with using summarized data

Answer 23

1. The reliability of the valuation will be reduced, as full valuation of the data is impossible
2. Summarized data may miss significant differences between the nature of the benefits that have been grouped together
3. Summarized data cannot be used to value options and guarantees that apply at an individual level

Question 24

When is data ‘Consistent’?

Answer 24

Consistent means that when comparing the experience of one group of policyholders with another, say, the data used as a basis for the calculations for each group should be:

PEGS

• Preferably extracted from the same source
• Equal in terms of reliability
• Grouped according to the same criteria
• Similar