Our Big Problem With ‘Noise’

A new book co-written by behavioral economist extraordinaire Daniel Kahneman points out a major problem that numerous industries, including insurance, only sort of know they have and is surely worse than they recognize. He calls the problem ‘noise.’

He says insurers are very aware of potential bias based on age, race, gender, etc., especially as they evaluate algorithms driven by artificial intelligence — insurers know to look for consistent favoritism toward, say, white men.

But, he says, insurers tend to gloss over the problem of inconsistency, or ‘noise’ — the fact that people come to very different conclusions based on the same set of facts, even when bias is removed from the equation.

Kahneman, who won the Nobel Prize in Economics in 2002 and who has driven so much of the progress on behavioral economics for decades, cites a study he did in 2015 that presented a series of cases to 48 underwriters at a large insurance company.

Executives predicted that there would be roughly a 10% variance between the high and low prices that the underwriters provided after assessing the risks — but the typical variance was 55%. Many variances were even more extreme.