They're not generic, though. Those providers build custom ML models specifically from your data (and they pull in a ton of signals for this), and then have the data of many other merchants to work with.

At a previous employer we found both of those products to be effective.

These systems are the most frustrating to me, because it is the unusual transactions that are more important than the usual ones.

If I have eggs delivered every week, and one of those transaction fails, who cares?

If I buy a plane ticket for a flight that leaves in 30 minutes because of an emergency, I will be fucking enraged if that transaction is blocked.

Similarly if I can't pay the hotel at check-out and because of this delay I miss my flight.

Or when I arrive in a new country and can't pay for the taxi ride, and the ATMs don't work either.

All these events have happened to me.

Effective at preventing fraud - sure But what about false positives?

If the question is whether you'll have any - of course you will! You will no matter what method you go with. At scale it's a pretty easy curve to optimize revenue on top of though, and you have to be willing to accept that bit. You know your customer LTV, you know your fraud rate/cost.

Sift gives you direct control of expected false positive rate, Radar hides that optimization under the hood I think?