Imagine some type of insurance that typically costs 100 USD&#x2F;month in premiums per person and where the insurance company typically pays out on average 90 USD&#x2F;month for damages per person. Now, imagine that one insurance company has figured out a way to detect a certain group of people who will only have damages of on average 50 USD&#x2F;month, which make up about 5% of the potential customers, and detecting which of their (potential) customers is in that group costs on average 0.10 USD&#x2F;month per customer. Do you agree that they will start offering lower premiums for that group of people, because that will attract new customers, and thus increase income?<p>And what if one company does this - what will other companies do? Will they keep the same price for that group as for everyone else? Then that group will leave for the company that&#x27;s cheaper for them, right? Leaving the other companies with the higher-risk customers, right? So, they will have to pay out more for damages, right? Now, will they just go bankrupt? Or will they increase premiums to cover the costs?<p>Noone says that insurance companies aren&#x27;t making inferences from data. It&#x27;s just that the more data there is available to them and the more powerful computers and algorithms get, the better they will be able to model risks. And individual companies won&#x27;t be able to ignore that, even if they want to. And it&#x27;s the exact opposite of what insurance is intended to do: It&#x27;s intended to distribute risk. The more exact insurance companies are able to model risks, the more insurance will become unaffordable to those who need it, and the cheaper it will become for those who don&#x27;t need it.