Large financial companies have a problem: determining which of their customers are good customers, and which are bad. For a long time there has been a solution to this problem (albeit unrefined in it's current state). It's called predictive
Predictive modeling is a process in which an analyst studies historical data that the company has collected about it's customers. (Data like: balance transactions, salary, address, credit history, etc.) The analyst knows which of these customers are "good" and "bad" customers because it's historical data; they can look for patterns in this old data that are consistent with positive or negative customer behavior and then use those patterns as a template to to try to predict which of the company's existing customers will also turn out to be "good" or "bad".
For the last few decades these financial companies have had only two options for producing predictive models – both of which are slow and expensive.