1
Q
Uses of data
A
- -Statutory returns
- -Investment monitoring
- -Risk management
- -Management info & financial control
- -Accounting
- -Pricing
- -Experience stats and analysis
- -Marketing
- -Admin
- -Provisioning
2
Q
Sources of data
A
- -Tables
- -Reinsurers
- -Abroad
- -Industry
- -National statistics
- -Existing contract experience
- -Regulatory reports and competitor accounts
- -Similar contract experience
3
Q
Disadvantages of industry-wide data
A
--Heterogeneity (places, styles, processes, products) --less Detail --less Flexible --less Up-to-date --dependent on Members' data systems --not all Contribute
4
Q
Characteristics of good quality data
A
–Complete
–Accessible
–Consistent
(format, comparable across systems - unambiguous inputs)
–Credible
–Relevant
(up-to-date, detail)
–Accurate
–Quantity of data
5
Q
Data checks
A
--Cross-check (against other sources) --Consistency (ave, min, max) --Recon --Spot checks
6
Q
Factors affecting assumption setting
A
- -Financial significance of assumptions
- -Legislative and regulatory requirements
- -Use of the model to which the assumptions contribute
- -Clients’ needs
- -Consistency between assumptions
7
Q
Requirements of a good model (pt 2)
A
- -Valid
- -Adequately documented
- -Rigorous
- -Inputs to parameters need to be appropriate
- -Arbitrage free (economic interpretation)
- -Behave reasonably
- -Length not too long to run or too expensive
- -Easy to understand
- -Communicable and transparent workings/outputs
- -Representativeness (reflects risk profile of contracts being modelled)
- -Independent verification of outputs
- -Sensible joint behaviour of variables
- -Parameters allow for all significant features
- -Simple (parsimonious)
–Clear results (transparent)
–Adaptable (versatile in implementation)
–Refineable
–Developable
(evolution process for last 3)
...old: --appropriate Parameters --Communicable --Valid --Verifiable --Versatile (applicable to different contexts) --major Features captured
