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29 Apr 17 13:34

Micro factors such as (re)insurance product design can contribute to (RE)insurance premium stability. This is on many minds in the industry today. Product design practices can support stable rates. Some drivers in underwriting practice are already in use: [1] Geospatial aggregation of insured policies to take account of diversification; [2] Designing accurate tiers (tranches) of tail CAT risk; [3] Optimizing coverage for intra-CAT event, and inter-(re)insured policy period conditionality.
A lot of research and articles are published on diversification and tranche design items [1] and [2], and much less on optional and conditional coverage [3]. Last year we put together a blog on one such type of optional and conditional coverage: In this article we covered inter CAT event conditionality for one campus – i.e. one grouping of (re)insured risks.
Similarly intra - event conditionality is also considered by (re)insurance coverage designers. The physical and scientific justification for this type of product design comes from measuring uneven or non-uniform historical and modeled likelihoods of loss occurrence at different and neighboring geo-spatial locations. But most typically this coverage is employed for larger commercial and industrial groupings – campuses of (re)insured risks. The actuarial – P&L accounting logic and justification is very simple on both sides of the equation – reinsurer and (re)insured. The conditional coverage of the drop down layer is cheaper than the fully stacked layered towers on each and all campuses
involved. Still it provides ‘more’ cover for the whole account than a single top layer on the ‘riskiest’ reinsured asset. Because in a scenario when this top layer is not exhausted on the ‘riskier’ insured campus, it will be reused on another group of risks if they experience loss from the same event.
Of course the worst case scenario of all campuses experiencing loss remains uncovered. A typical case would be providing single CAT event conditional drop down layers for campuses in different coastal flood zones, with different historical likelihoods of loss occurring in each one of them. When the (re)insurer understands the physics of the risk well, and the geospatial characteristics of the terrain and the peril, then he may be justified in proposing that the likelihood of loss experienced by all campuses at once is negligible. This warrants the deployment of single event drop down layers on the riskiest campuses – i.e. the ones situated in the 100 year flood zones, with a conditional reuse option for unexhausted capacity transferred to risks in the 500 year zones. Undoubtedly this is a cost saving measure for the (re)insured. Demonstrating flexible thinking and modeling along with desire to achieve cost optimality may tips the balance towards stable premium at the next renewal.  
To support this intuitive P&L thinking, we are attempting to work out a proof that the downward deltas for conditional layers are always lower than for stand alone non-conditinal ones, perhaps using information asymmetry.  Contributions are welcomed!

Category: Climate/natural disasters: Climate change, Floods/storms

Location: Cambridge, MA, United States


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