Prediction vs Explanation in Regression

One issue in using regression analysis is to determine whether you are developing a model to predict an outcome, or to explain an outcome. It's often a little bit hazy which one you are actually doing - in science, we like to say that we are explaining, but it's difficult (not impossible) to argue that we're doing much more than predicting.

However, one place where prediction is all that matters is in finance. Credit card companies like to lend people money, but they only like to lend people money who are going to give it back. And they don't care why people don't give them their money back, they just want to predict who will give them their money back.

But this policy often leads to confusion, as in Amex lowers your credit limit if you shop where deadbeats shop. At the moment, credit card companies are feeling kind of nervous - they think that a lot of people might not be giving them their money back, and so they are running regression models to predict who those people might be. They find that people who shop in some stores are less likely to pay them back, and so if you look like one of those people, they might lower your credit limit - this is pure prediction.

In science, it's common to mistake prediction for explanation - I've found a correlation, and so you think I've found the reason something happens. But in this credit card example, it's the other way around - all they have is a prediction. It doesn't mean anything, but people interpret it as some sort of slight against them.