Profit commission
Profit commission is commission paid by the reinsurer to a cedant.
The commission is DEPENDANT ON THE PROFITABILITY or claims experience of the total business ceded during each accounting period.
Risks-attaching basis
The basis under which reinsurance is provided for claims arising from policies COMMENCING DURING THE PERIOD to which the reinsurance relates,
irrespective of when the claims are incurred or reported.
A.k.a. Policies-incepting basis.
Corresponds to an UNDERWRITING PERIOD COHORT.
Reinsurance contracts to which profit commission is most applicable
Profit commission is most likely to be used for:
Reinsurance contracts to which a risks-attaching basis is most applicable
A risks-attaching basis is a natural arrangement for PROPORTIONAL reinsurance.
Pure risk premium
The premium required to cover the EXPECTED CLAIM AMOUNT ONLY.
No allowance is made for expenses or profit.
We may express it as a nominal amount, but it is usually expressed as a rate per unit of exposure.
Steps involved in calculating a pure risk premium
burning cost method
EXPERIENCE-BASED method that takes the actual cost of claims during a past period of years, expressed as an annual rate per unit of exposure.
This could apply to a single risk or to a portfolio of similar risks.
The technique may be
purely based on past claims without adjustment, although an improvement would be to
adjust past claims for trends and develop the claims to ultimate, but often this is not done in practice. If trending is applied to claims, exposure should also be adjusted.
The burning cost approach is commonly applied to aggregate claims, but may also be applied to frequency and severity separately.
The burning cost method is most suitable when there are lots of credible data. When the data are not credible, the burning cost premium should be combined with book rates using credibility techniques to obtain a more accurate premium.
Original loss curves
Original loss curves are an EXPOSURE-BASED rating method.
The main principle of exposure rating is to not use historic claims experience at all, but instead to base premium rates on the amount of risk (ie exposure) that policies bring to the portfolio.
In exposure rating, we USE A BENCHMARK to represent a market severity distribution for the line of business and territory being covered. The benchmark may even be directly derived from the market severity distribution.
Original loss curves (exposure curves or ILF curves) are used to estimate the cost to the layer based on the exposure and premium information provided by the cedant rather than the actual cost and past exposure.
In particular, we commonly use original loss curves in excess of loss insurance pricing to infer prices for layers at which the data are too sparse to derive a credible experience rate.
So for example we might use them in place of a burning cost approach (which requires lots of credible data) when calculating the risk premium net of a layer of reinsurance with a high excess point, or perhaps even calculating the risk premium for the layer of reinsurance (from the reinsurer’s perspective).
Benefits provided under personal accident cover
The insurance provides a fixed amount in the event that the insured party suffers the loss of one or more limbs or other specified injury, or accidental death.
Such cover is usually included in:
Factors impacting whether an insurer may decide not to match its liabilities
Characteristics of the liabilities under Personal Accident cover
Matching assets for Personal Accident cover
CASH / MONEY MARKET INSTRUMENTS
SHORT-DATED (< 3-YEAR) BONDS
5 Different types of technical reserve
IBNR IBNER UPR URR Catastrophe Reserve
IBNR
A reserve to provide for claims in respect of claim events that have occurred before the valuation data but have not yet been reported to the insurer at the valuation date.
IBNER
A reserve reflecting expected changes in estimates for reported claims only.
UPR
The amount set aside from premiums written before the valuation date to cover risks incurred after the date.
URR
The reserve required to cover claims and expenses that are expected to emerge from unexpired periods of cover.
Catastrophe reserve
A reserve built up over period between catastrophes to smooth the reported results over a number of years. The purpose of the catastrophe reserve is smoothing, not solvency.
Explain one important consideration you should apply when using industry benchmarks
It is important to consider whether the source from which the benchmarks are derived has characteristics that are appropriate to the business for which the reserves are being derived.
E.g.
6 ratios/quantities you could compare to industry benchmarks when assessing the level of IBNR reserves.
6 Aspects for which an insurer might be able to receive technical assistance from a pure reinsurance company
PRODUCT DESIGN, including advising on appropriate terms and conditions
PRICING,
the reinsurer will be able to provide past claims data to assist in setting pricing assumption.
ASSIST WITH DEVELOPMENT OF MODELS for pricing, catastrophe modelling, etc
RESERVE ASSUMPTIONS
INITIAL UNDERWRITING POLICIES
CLAIMS CONTROLS
Refining POLICY WORDING to avoid ambiguities.
Development of suitable ADMINISTRATION SYSTEMS
MARKETING STRATEGY - distribution channels and selecting target markets.
Discuss the initial analyses which should be conducted on the data and explain why they are done before conducting a GLM analysis for pricing with multiple rating factors.
Before conducting an analysis using a GLM it is appropriate to firstly check and have an understanding of the raw data that will be used in the model.
The raw data need to be :
- checked for COMPLETENESS,
- checked that it has NOT BEEN CORRUPTED
- compared with that used in a previous review to ensure that the most recent and appropriate data are
being used.
We need to achieve a balance between the number of rating factors and the homogeneity of the risks.
- choose each additional rating factor to remove as much of the residual heterogeneity as possible.
If the factors do not sufficiently distinguish between
different levels of risks, insurers are likely to attract the underpriced risks and lose the over-priced ones.
However, if too many factors are chosen, insurers may experience difficulty due to high administrative costs and resistance of the market and brokers.
Having a good understanding of the underlying data will help when deciding on the appropriate balance of the number of rating factors to include into the analysis. A correlation analysis will explain why the multivariate results for a particular factor differ from the univariate results. It also indicates which factors may be affected by the removal or inclusion of any other factor in the generalized linear model. Cramer’s V statistic can
be used to understand the correlation between two variables.
We should also assess the validity of other risk groupings by stochastic analyses to test for differential results. We should adjust the theory for practicalities, including the availability of information and the applicability of systems. If we can’t get the data in a
reliable easy-to-use format, we may need to compromise our calculations.
Finally, a DISTRIBUTION ANALYSIS for claim amounts could be analyzed for various segments of the data.
One-way ANOVA
In the case of one-way ANOVA, we investigate the amount of variability explained by each factor without taking into consideration the correlation between factors.
We may find that when we split policyholders into different age groups, for example, the variability of claims experience within each age group is small relative to the variability in the overall portfolio of risks. Hence the factor “age” helps to ‘explain” the variability
because, after grouping the policyholders by age, there is little residual variability left within the groups.
Two-way ANOVA
In a two-way analysis of variance, we investigate each factor and the correlations between any two of the factors.
This can explain the variability better than a one-way analysis.
For example, the one-way analysis may show that the size of a household claim is highly related to both the number of bedrooms and the value of the contents.
A two-way analysis may reveal that these two factors are in fact highly correlated, so that only one should be
included in the pricing factors.
It also helps in identifying the interaction effect between factors and can reveal the exposure and claim numbers for various combinations of levels from a pair of factors.