CH 19: Data

Question 1

Define personal data

1

Answer 1

• Personal data is information that can identify and individual, or where the data combined with other information could allow the individual to be identified

Question 2

Eight principles which must be followed when processing personal data

POPIA ACT

Answer 2

Personal data must:
1. ACCOUNTABILITY: Party processes data is accountable for compliance
2. PROCESSING LIMITATION: Be processed fairly and lawfully with consent
3. PURPOSE SPECIFICATION: Be obtained and processed for specified purposes
4. SECURITY SAFEGUARDS: Integrity and confidentiality ensured

5. FURTHER PROCESS LIMITATION: Must be compatible with initial purpose.
6. OPENESS: Transparency on use and documentation available
7. DATA QUALITY: Completeness,accuracy updated by holder
8. DATA SUBJECTS: request confirmation,correction,deletion

APPS FODD

Question 3

Competition Legislation

2

Answer 3

• Anti-Competitive Agreements: data shared amongst groups of companies to fix prices
• Abuse of market position: Setting unfair trading terms in monopoly

Question 4

Examples of what might count as ‘sensitive personal data’

7

Answer 4

Sensitive personal data can include information related to:

1. Racial or ethnic origin
2. Political opinions
3. Religious or other beliefs
4. Membership of trade unions
5. Physical or mental health or condition
6. Sexual life
7. Convictions, proceedings and criminal acts

Stricter regulation than other personal data

Question 5

Give examples of circumstances when sensitive personal information may be legitimately processed

4

Answer 5

1. The data subject has given explicit consent
2. It is required by law for employment purposes
3. It is needed in order to protect the vital interests of the individual or another person
4. It is needed in connection with the administration of justice or legal proceedings

Question 6

State three characteristics of ‘big data’

3

Answer 6

1. The data sets are very large
2. Data is brought together from different sources
3. Data can be analyzed very quickly, for example in real time

Question 7

Data protection considerations

2,3

Answer 7

• Hold personal data as big data same regulation will apply
• Anonymisation is used to make big data non personal

Big data must:
* Have a clear goal
* All data processed must be relevant
* Transparency in processing +consent

Question 8

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

1,6

Answer 8

• 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:

1. The specific roles and responsibilities of individuals in the organization with regards to data
2. How an organization 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 organization

Question 9

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

4

Answer 9

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 10

Data Usage risks

5

Answer 10

• Data errors/omissions
• Data incorrect form
• Insufficient historical data for predictions and analysis
• No data available for certain scenarios
• Historical != Future

Question 11

Define algorithmic trading

Answer 11

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 12

Explain the risks of algorithmic trading

Answer 12

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 13

How should data be controlled and managed?

3

Answer 13

• There should be one single, integrated data system so that the data used for different applications is consistent
• Reduced chance of corruption/inconsistent treatment
• Better access control over data and changes
• Easier to secure

Question 14

List the main sources of data

4

Answer 14

Tables
Reinsurers
Abroad (data from overseas contracts)
Industry data
National statistics
Experience investigations on the existing contract
Regulatory reports and company accounts
Similar contracts

• Public
• Internal
• External
• Data collection scheme

TRAINERS

Question 15

Reasons why industry data is not directly comparable (heterogeneity)

6

Answer 15

1. Different geographical and socio-economic markets
2. Different policies
3. Different sales methods
4. Different practices, e.g. underwriting and claims settlement processes
5. Different nature of data stored
6. Different coding of risk factors

Question 16

Four negatives off industry data:

4

Answer 16

1. Less detailed and flexible than internal data
2. More out-of-date than internal data
3. Data quality depends on the quality of the data systems of all its contributors
4. Not all organizations contribute, and those that do may not be representative of the market

Question 17

How can good quality data be ensured from an insurance proposal and claims form?

7

Answer 17

1. Questions should be well designed and unambiguous so that full information is given and so that applications / claims can be easily processed
2. Use questions with quantitative or tick-box answers wherever possible
3. Questions should be in the same order as the input into the administration systems, for quick processing of applications / claims
4. Ask the policyholder to verify the key information
5. All rating factors should be readily identifiable so that the composition of the final premium can be determined
6. Underwriting results should be added to the proposal form
7. Forms should be designed so that information can be easily analyzed, and cross checks made between the two sources

Question 18

Why is it important to have access to the information given on the proposal form?

3

Answer 18

1. To check the validity of the claim
2. Update information
3. Record changes made to policy

Question 19

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

1

Answer 19

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 20

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

3

Answer 20

• 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 as needs to provide all significant info

Question 21

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

4

Answer 21

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 22

Data checks that need to be done

4

Answer 22

• Verify current data : reconicile from old data
• Accounting data: audited can be used to verfiy data
• Asset data: Valuation must be consistent with liabilities
• Data assertions: Completeness and accuracy of data investigated
• Reconciliation, cross,reasonable,spot

Question 23

Give examples of reconciliation checks that can be performed on data

4

Answer 23

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 24

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

4

Answer 24

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 25

Give examples of reasonableness checks that can be performed on data

Answer 25

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 26

Give examples of spot checks that can be performed on data

Answer 26

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 27

Outline three problems with using summarized data | 3

Answer 27

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 28

What is risk classification and what is its main aim? | 2

Answer 28

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 29

27) When is data 'Consistent'?

Answer 29

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