Chapter 20

Question 1

What are some problems with using past data?

Answer 1

• Different standards of underwriting
• Too out of date
• Poor raw data, especially old bc of systems
• Small volume
• Too heterogenous
• Not sufficient at some age ranges
• Extraordinar events
• Target market different

Question 2

Where can a company get data to make economic assumptions?

Answer 2

• Policy statements
• Budget declaration
• Leading indicators like new jobs in developed countries, stagflation, growing economy = inflation
• Economists projections
• Past data on asset returns and salary levels

Question 3

Where can a company get data to make demographic assumptions?

Answer 3

• Own data = best bc they are your target market, but the problem:
  i. is it might be limited (not have sufficient representation across all ages, might be limited in the number of years you’ve had data for)
  ii. you need enough years to be credible (around 5 years), but not too many years, you’re starting to use less credible data
  iii. past data might not be indicative of new product or the target market. From products aimed at young audience to elderly audience
  iv. most companies won’t have very credible data for people over 80 bc a lor of people who bought policies in the past and got to 80 have usually lapsed
  1. cohort effect – we know that mortality changes, some generations have better mortality improvements
  2. more variable for older people bc we have fewer insured lives
  3. so old age mortality is harder to get from old data than middle aged mortality
  v. How to tackle this?
• Industry data (liberty and discovery) can help glean expense ratios (doesn’t tell you per product, will be useless for pricing) = useful for group pricing for example pricing a mining company
• Standard actuarial tables of insured lives (2nd best of data) pooled industry data by appropriate product
  i. Anonymise data
  ii. Only as good as the pooled arrangement by participating industry players
• Usually use a combination, adjust standard actuarial tables
• Reinsurance
  i. Some life insurers usually take out quota share reinsurers
  1. Better for new players who aren’t capital rich, not enough capacity to take on extra risk
  ii. Most take aggregate XL with layers
  iii. So credibility of their data is going to be skewed towards larger products
  iv. Reinsurers also reinsurer the non-standard lives, so reinsurer has a greater idea of substandard lives, not ordinary lives
  v. Reinsurers are helpful for setting substandard terms, adapting own data or standard tables and they can give you the rates they’re willing to take on your mortality risk…. So, they won’t give you their mortality experience, so you can infer the price for the product but what they charge you is based on your target market AND YOUR RISK MANAGEMENT
• similar countries standard tables with some adjustments
• National population statistics (only last on the list for SA)
  i. Usually has a lag factor
  ii. Exposure is only based on census, only every 10 years
  iii. Not insured life statistics
  iv. Not very useful, especially in South Africa
• Population data from similar countries
  Where to get data- trend: (ranked)
• Projections from standard actuarial tables (so take the UK tables and use projection values)
  i. But in SA it’s a bit more difficult, nature of assured lives charged over time, we don’t have a homogenous group to track from far back
• Medical research on d=fundamental drivers of decrements
• Comparison of standard tables over time
• Medical advances and technological developments
  Where to get data – cycles
• Economic impact on demographic assumptions
• Analyse past cycles
• Identify leading indicators

Question 4

Examples of demographic assumptions

Answer 4

• Mortality rates
• Morbidity rates
• Lapse rates and surrender rates – withdrawals
• Renewal rates
• Recoveries
• Marriage rates

Question 5

Examples of economic assumptions

Answer 5

• Asset returns
• Company’s own expense inflation & salary inflation & price inflation
• Yield of fixed interest gov bond, could be a 10year gov bond, so yield curve
• Risk discount rate = return above risk-free than shareholders desire
• Exchange rates, important for benefits that have an international component
• Unemployment rate
• GDP growth