Surely this depends on how the vendor sets their prices? If you're going to buy something from a website to test a stolen credit card you don't just get to make up your own prices.
And I think you may be over-indexing on the US "prices don't include tax" thing. Elsewhere, round-number prices are extremely common.
In fact a lot of the rest of the stuff in the post seems like it wouldn't work very well either. (E.g. you're flagging anyone who has done a transaction in the last 90 days outside the range of hours at which they have 2+ transactions? Wouldn't that be like 50% of people?).
It's unclear to me whether this article is an attempt at breaking down complex expertise into over-simplified SQL queries, or whether it is all speculative and made up.
There is a conflict between "Six SQL patterns I use to catch transaction fraud" and "Nothing here comes from anything I’ve actually worked on or seen".
Coffee usually _is_ a round number in my experience, and I know of people who aim for round numbers when filling their car, and of fuel stations which require a pre-set value, often 10, 20, 50€ etc
If a card swipes in Chicago and seven minutes later swipes in Los Angeles, one of those swipes is fake.
Can also imagine an edge case: couple shares an online account, one is traveling and purchases with the saved card details.
This is an underrated CX factor: If my card gets denied when i’m a new customer or exhibiting a new pattern, i’m impressed with their software.
However if they deny a transaction where there is any previous history of me authenticating, then I’m frustrated by their naive paranoid algorithm.
Or, the cardholder is trying to do the cannonball run:
> The roundness is the signal.
> Slight pain, same result.
to point at a few.
And my favourite most hated pattern, the no no no:
> Not machine learning, not graph databases, not whatever Gartner is hyping this year.