When Data Cannot Do Insurance

Here is David Brooks on what Data can’t do:

Data struggles with the social. Your brain is pretty bad at math (quick, what’s the square root of 437), but it’s excellent at social cognition. People are really good at mirroring each other’s emotional states, at detecting uncooperative behavior and at assigning value to things through emotion.

Computer-driven data analysis, on the other hand, excels at measuring the quantity of social interactions but not the quality. Network scientists can map your interactions with the six co-workers you see during 76 percent of your days, but they can’t capture your devotion to the childhood friends you see twice a year, let alone Dante’s love for Beatrice, whom he met twice.

In insurance we care about scale (the law of large numbers) and not getting f*@#ed over (avoiding moral hazard). Data has definitely helped where moral hazard is somewhat easily guarded against: such as in homeowners or auto liability insurance.

And these are the largest insurance markets on earth. Consumers have no doubt benefited, either through lower insurance premiums or (more likely) through a far more generous tort system and subsidy for people to build their homes in flood planes and on fault lines and  hurricane tracks.

For more complicated lines of insurance, we still need really expensive underwriters to decide who is worthy of trust. Data has a long way to go there.

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