Because it is the degree of fraud that you catch that matters. Every financial system has fraud. In the old days - I used to work at a bank - the banks knew that there was fraud, but usually they didn't have a really good grip on how many of their transactions were fraudulent and how much of that they caught. The limitations on human resources alone made this a losing battle.

Just like any classification system a fraud control system has false positives, false negatives, true positives and true negatives. In an ideal version of such a system you have no false positives and false negatives. But that idealized version of that system (the one that perfectly classifies fraudulent transactions and non-fraudulent transactions) is static. It can't adapt to cases of fraud that it hasn't seen yet. Eventually a fraudulent transaction gets through.

And that transaction if not detected timely but after the fact (so now there is some fraud) will result in an update to the fraud detection system (probably it will need more than one example to be able to generalize from). Now the perfect system may start to generate false positives or false negatives on other transactions.

So that's why all of this is relevant. I hope I'm able to explain it clearly enough.