> They don’t need to be 100% accurate. In many cases, abusing such data will also be profitable even if the result estimate is highly noisy. Just as an exampl

> They don’t need to be 100% accurate. In many cases, abusing such data will also be profitable even if the result estimate is highly noisy. Just as an example, let's assume that an insurance company knows your given name when you apply and they have statistical data which shows that if your name belongs to one set of names, you are smoking with a probability of 51%. If you have another name, you are smoking with a probability of 49%, because the correlation is noisy. It is still more lucrative for the company to exclude applicants from the "more likely to smoke" names group, because smoking causes cost. You might never have tried a single cigarette in your life but with the information the company has, it is more profitable to not insure you. Of course the above is an exaggerated example which would not work out quantitatively (the profit they make by insuring you might be higher than the expected value of risk from smoking) but the principle still holds; it is used for credit scoring and similar things.