[alexbeloi]

> how much do the "obvious" checks help - highly unlikely purchases (3 iphones) timing or physical activity (I probably won't buy books on amazon, clothes on boohoo and petrol in a garage in the same five minutes) versus the more ML / secret squirrel stuff?

ML solutions very often are just learning to codify these 'obvious' scenarios, and as a bonus sometimes less obvious ones.

You could sit in meetings for hours/days listing all the cases the fraud detection should catch, and you'll still end up missing lots. If you're stripe, you have tons of data about fraudulent purchases that you can use to learn and codify these scenarios. Importantly, you can learn which scenarios are most prevalent in your system in particular, fraud at Stripe is very likely to look different than fraud at (say) Wells Fargo.

[lifeisstillgood]

This raises another question i guess - how to tell the difference between a chargeback that was fraud and a chargeback that was "i don't like it" i assume they get reasons for chargebacks?