(I work at Sift Science.)

One thing to note -- our system analyzes a whole bunch of patterns for each user. So just shopping at 3am by itself won't cause problems, nor will using a prepaid gift card by itself. But if a user matches multiple fraud patterns, then they're likely to get a high fraud score.

Why does people complain if I enter my bank with a ski mask? What if a person simply wants to protect their privacy?

In other words: A lot of things can have perfectly legitimate reasons but still strongly suggest that you're up to no good.

The tricky thing is to combine sufficient number of signals to get a high enough confidence to act on it without angering users with legitimate reasons.

I think it's best to rephrase that data and say "using a real credit card makes the transaction look more legit". (But it should be obvious that fraudsters want to "protect their privacy". In this case, it's the difference between going to jail and not!)

Then they are making a purchase in a category that is most often fraud though they are not committing fraud. Its not like the two things can't exist at the same time.

If I want to protect my privacy, I can wear a ski mask to any place with surveillance cameras, such as banks and gas stations. But this does tend to strongly suggest that I'm there to rob the joint.

The same thing than purchasing something at 3am. Those are only behaviors associated with higher chances of fraud. With the models they work p(fraud|prepaid_card) > p(fraud|!prepaid_card)

And how do you defraud a retailer using a prepaid gift card anyway?