Chapter 18: Rating methodologies and bases Flashcards
The rating methodology used will depend on:
- the class of business being priced
- the availability of relevant data
- the market in which the company is operating
Components of the risk premium
- the pure risk rate based on previous years’ experience
- a loading for catastrophe and/or large loss claims (which may or may not exist in the previous data)
Components of the office premium
- a loading for the net of cost reinsurance
- a loading for expenses including commission
- a capital charge to reflect the cost of capital (profit loading)
- investment income
- tax
Other considerations when calculating the premium to charge
- rating factors
- practical considerations concerning policy conditions, underwriting process, competition, etc.
Steps involved in calculating the pure risk premium
- collect relevant data, including past exposure data and claims arising from that exposure
- adjust the data to make it more relevant
- group data into risk groups
- select most appropriate rating model or estimation process for the specific case
- analyse the data
- set assumptions required by the model or process
- test the assumptions for goodness of fit or likelihood probability
- run the model or process to arrive at an estimate of future claims cost
- perform sensitivity and scenario testing, or apply other methods, to check the validity of the estimate
Examples of statistical approaches that can be used to derive a risk premium
- simple burning cost approach to premium rating, using aggregate claims data
- frequency-severity approach, where statistical distributions are fitted to the frequency and severity of claims separately and combined to give a risk premium
- multivariate models, including GLMs
- the “original loss curve” approach to premium rating
Data obtained from external sources, should be compared with the corresponding details for the policies the insurer intends to write, as far as possible:
- the terms of the policy offered
- levels of risk underwritten
- the loadings included for expense and profit in the premium
- socio-economic differences
External data is especially useful in some circumstances:
- for a company writing a new or modified class of business
- where the company’s own data is sparse
- to provide confirmation of results derived from internal data
Reasons why accurate past claims data might not be suitable for premium rating
May not be appropriate if future conditions are expected to be different from those in the past. Examples of possible differences are:
- past data isn’t from the classes we now wish to set premiums for
- we wish to change the premium structure and the past data isn’t credible enough to use
- the volume of past data is inadequate
- there have been sudden changes in the court treatment of certain types of claim
- policy conditions have changed, e.g. an increased excess
Past data may be useful in some cases but will need to be adjusted
Data for pricing
Credibility Theory
An approach of taking a weighted average of two extreme approaches on deciding which dataset to use:
- using an estimate based on the past data of the individual insurer - based on most relevant data
- using an estimate based on market-wide data - in some senses a more reliable figure
Things that affect the amount of data needed for full credibility
- the variance of the underlying process. The higher the variance, the more data is needed
- the criteria chosen (distance from the true mean and confidence level)
Subdivision of data
Where possible and statistically relevant, we split the data into risk cells - homogenous subsets based on factors that contribute to higher or lower claims experience
Why is data subdivided?
- Enables us to better understand the risks being handled
- Helps us avoid cross-subsidies = profit won’t depend on particular cross-section of risks
What should we do if the experience in the base period does not appear to be typical?
- choose another base year that is more typical
- aggregate more years’ experience
- apply an adjustment factor to the affected base year. Such a factor will be rather subjective, although market figures may be available
To help assess what adjustments to the base period are needed, we could:
- gather information on results of other insurers, to establish whether the deterioration was industry-wide or specific to the insurer
- establish whether there are any global climatic or economic factors that would explain the unexpected experience (and how they are expected to affect future experience)
- look at previous years’ results to try and identify trends or cycles
Changes in the risk may arise because of changes in:
- the mix of underlying risks
- cover/policy conditions
- claims handling/underwriting strategy
- method of distribution
- level of reinsurance coverage
Matters in which significant inconsistencies may arise:
- policy acceptance
- policy coverage
- method of distribution
- claims settlement procedures
Major changes in policy conditions are likely to be in:
- the perils covered
- limits or excesses applied to any claim
Examples of environmental factors that affect claims experience
- legislative factors
- advances in technology
- medical advances
- changes in construction of property
Time delays that may result in adjustment to the data may occur because of:
- time take for sufficient claims experience to develop from the historical data
- time taken to analyse the claims experience
- time taken to reach and agree on the new premium rates and premium structure
- time taken to administer and implement the new rates
- time delay between the risk period and the payment of claims due to reporting and settlement delays
- time taken for any approval needed from a regulatory body to introduce rates
We need to allow for the expected effect of inflation on claims between:
- the mean payment date of claims in the base period and
- the mean payment date of claims arising during the new exposure period
When revaluing base values for future premium rates, there are two parts to the calculation:
- inflating base values to the present day using (broadly) known inflation rates
- projecting from the present day to the future using estimated future inflation rates
Approaches for adjusting for large individual (non-catastrophic) losses:
- omit them from the analysis and allow for them seperately in the risk premium
- truncate large claims at a set point and spread any cost above this level across the larger portfolio of risks
- leave large claims in the data, although we rarely do this because this would implicitly assume future occurences will replicate those seen in the past
Ways of allowing for the cost of reinsurance in a premium rate
- as the net cost of reinsurance (reinsurance less recoveries) in a premium rate based on a gross risk premium (not considering reinsurance recoveries)
- as the gross cost of reinsurance (reinsurance premium) in a premium rate based on a net risk premium (considering reinsurance recoveries)
For most insurers, the expenses, in roughly decending order might be:
- staff salaries, pensions costs, national insurance contributions, etc
- commission payments
- office rent and related costs
- office equipment (e.g. computers)
- office consumables (e.g. stationary)
Stages involved in determining the required expense loadings
- allocate the expenses by class or product and, possibly, by major rating factor groups
- allocate expenses by function
- decide on how to allow for each expense type in the rating formula
Theoretically, we can divide expenses into:
- underwriting and brokers’ commission
- policy administration
- claims handling
- overheads
- levies to state insurance schemes, e.g. to SASRIA
Ways of charging for commissions, expenses and other margins:
- add an overall percentage to the risk premium, based on evidence of previous year’s underwriting results
- take into account the split between fixed and variable expenses and likely volumes of business in the future
Factors to consider when selecting an assumed rate of investment return
- likely investment conditions at the time when the premiums are receivable
- assets likely to be held (which should be matched to the nature of the liabilities)
- consistency with the inflation assumption used to project expected claims and expenses
Pricing with limited data
- use of other data
- margins
- use of ILFs or exposure curves
- qualitative methods
Using a mixture of quantitative and qualitive data is used when quantitive data is:
- incomplete
- inaccurate
- sparse
- where risk perception plays an important element in price determination
Inadequate data may distort calculations of actual projections of the new rating requirements. This may be due to errors in:
- the apparent size of business in force, and its value expressed in exposure units and premium
- the apparent claims experience and its trends, on which the projected future costs are being based
If we adopt a deficient set of rates as a result of faulty data, the insurer might:
- suffer underwriting losses if rates are too low
- suffer loss of market share if rates are too high
- attract undesirable risks, causing deterioration in underwriting experience if rates for such risks are too low
