If predictions are too precise, is insurance not insurance?

Excerpts from two blog posts with philosophical questions about insurance. In a quantified, connected world, more data enables better predictions. But, there’s tension between personalized rates vs. pooled risks. Fundamentally, does the concept of insurance require a certain degree of uncertainty?

Black boxes for people are not yet standard, but wearable sensors can monitor movement, heart rate, and heart rhythm, blood pressure and blood-oxygen levels, and glucose levels and other health-related statistics. Such information can be recorded and reported through smartphone apps, watches, and other wearable devices. …

The actual problems with health insurance markets have less to do with information asymmetry and adverse selection than with too much information. That can make some people’s insurance very expensive at actuarially sound rates. For instance if you have a cancer of a given kind, this is verifiable to the outside world, and if the treatment costs are $200,000, the cost of an insurance policy will in turn be about $200,000. Buying the policy won’t be cheaper than buying the treatments, and in that sense the market for insurance is not always present. That is a very real public policy problem

The cheap sequencing of the genome may accelerate and intensify these issues. …

— Alex Tabarrok & Tyler Cowen, The End of Asymmetric Information

I think a quantified, connected society will have some interesting consequences for businesses based on managing uncertainty. As we get better at prediction, the economics of amortization get worse. 

There are entire industries predicated on amortizing risk across populations. Insurance is a good one. …

Prediction and amortization are fundamental opposites. If you know nothing about a population’s risk factors, you use a socialized medicine model where everyone is taxed equally. Risk is shared. If you know more, you have differentiated pricing based on pre-existing conditions, smoking, etc.

In any amortized model, even with some prediction and correlation, there is an element of uncertainty. Among the population of, say, fast drivers, or smokers, you don’t know which will have an accident or die of throat cancer.

So the “perfect” prediction would be an insurance policy tailored to one person. If I knew with absolute certainty the chance that you’d have an accident and the economic impact of that accident, then your monthly insurance payment would simply be monthly deposits into a savings account whose value would equal the cost of the accident on the day of the accident. Plus, of course, the insurer’s administration and profit margins.

In other words, a tax on your life.

Obviously, no prediction system is perfect. But as we use data to make more and more accurate decisions based on the latest information, things get absurd. In the split-second before a collision, when someone slams on the brakes, we have a very good idea about their chances of being in an accident. Can we revoke their coverage or up their premiums? …

— Alistair Croll, The Selfish Economics of Big Data

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