In his book, Loss Coverage: Why Insurance Works Better with Some Adverse Selection
This is particularly relevant given the ambition of some InsurTech players to hyper select risks or price on many more factors than are traditionally used in order to gain a competitive advantage.  Thomas doesn’t argue that it will be individual insurers’ interests to allow adverse selection, but if these companies are successful it may then have implications for policy makers.
Incidentally, there are some interesting reasons for insurers themselves (with commercial interests) to be wary of selecting too well, counterintuitive as that may seem, but more on that for another time.
The book itself is a mixture of qualitative arguments and gentle reasoning with enough maths to keep you interested if you are that way inclined. I struggled to finish the book and found the points belaboured after a while.
The main reason I struggled with the book though is that I believe from the start it assumes its conclusion.- that “increased loss coverage is universally a good thing”. I’m going to explain a little bit of that here. If you are going to read the book, it might be useful to have these thoughts in mind going into it and see whether you agree.
The premise of the book
Loss Coverage is defined as the expected amount of claims covered by insurance. They demonstrate that, under certain pricing and behavioural assumptions, more adverse selection leads to higher loss coverage.
The rationale is that without accurate pricing, some premiums will be set a level too high and some too low for the specific risk. Some good risks who feel the premium overstates their will decline cover. Since more of the higher risk customers are getting a good deal and recognise the good deal, they will retain cover. Since some of those high risk customers will have felt an accurate price was too high, there could be an increase in high risk customers with cover. They contribute disproportionately to loss coverage given their higher probability of claim and therefore overall loss coverage can go up.
These assumptions are not particularly robust and the book even deals with examples “taken to the logical extreme” that show no increase in loss coverage and a substantial decrease in the number of lives covered.
A limited view on the value of insurance
An increase in the loss coverage measure means that more claims are expected to be covered by insurance as a result of allowing some adverse selection. However, this is at the cost of fewer individual risks being covered by insurance in total.
The argument thus neglects part of the value of insurance. Having insurance, even if one is fortunate enough not to claim, allows a less anxious existence, and the ownership and use of precious assets that would be irrational without having transferred the risk to an insurer.
This not merely a “peace of mind” value. The ability to optimise one’s risk budget by reducing certain risks and taking on others allows for risk-taking and economic growth. Insurance exists in the first place because it is not efficient for individuals to bear their own risks without pooling or transfer.
Questionable measures on ultra high probability claims
Why is the “risk greatest for those with the highest probability of claiming”? Under certain definitions of risk that is the case. But it’s not universal. If the probability of claim is 100%, I’d argue there is no “risk” at all. That might be a trivial case, but what about where the probability is 75%?
This reminds me of a product idea I never liked – insurance for taxi tyres. Taxi tyres are a consumable item that are replaced sometimes two or three times per year. The risk of having to replace them slightly earlier than planned hardly feels like a risk worth insuring. The probability of claim is too high and the cost of the claim too low. Would optimal loss coverage have all of these tyres insured?
And the book?
The book should have been a paper. There isn’t enough there to warrant the length or the price, nor is it sufficiently interesting or amusing to want me to finish it.
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