> Drawback: this doesn’t work until you have history. New accounts have no baseline.
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.
This is quite interesting, but the blatantly AI generated explanations are like an anti-signal for quality
> If a card swipes in Chicago and seven minutes later swipes in Los Angeles, one of those swipes is fake. The card is cloned.
Or, the cardholder is trying to do the cannonball run:
What if I go on a roadtrip and suddenly get gas at 2am?
I had this happen once - I flew to a city about 8 hours of driving time away to buy a motorcycle and landed late in the evening. My card was declined when I got gas a little after midnight and I had no cash or other card with me so I called the 24 hour support line. I had a quick conversation with a support agent explaining that I was traveling and the card needed to be reactivated right away. Within five minutes the card was working and I was back to working my way down a long chain of mistakes.
This seems interesting, but has so many signs of AI writing that I worry it's not just edited but generated from whole cloth. Probably still a lot of truth in there but it does give me pause!
> The roundness is the signal.
> Slight pain, same result.
to point at a few.
> Three filters. That’s it.
And my favourite most hated pattern, the no no no:
> Not machine learning, not graph databases, not whatever Gartner is hyping this year.
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.
Swiping a card (or inserting, or tapping) is a "card present" transaction. Online shopping, where you type in the card number, is a "card not present" transaction. Retailers and banks can tell the difference.
They can tell based on transaction metadata. Source: I worked at a cc company
I believe the system distinguishes between card present and card not present.
> Real cardholders almost never buy something for exactly $1.00. Coffee is $4.73, gas is $52.81. The roundness is the signal.
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".
Worse than that.
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
I'm seeing a few stores here and there which have a "round up to donate" option. I guess I'm a bit of a sucker and I always use that option. My groceries are always a round number as a result.