In my experience, what you're describing would more specifically be called Fraud Prevention rather than Fraud Detection. Both tend to coexist and are complementary in a mature setup.

For Prevention, you're always going to be constrained by latency requirements, available data and an incomplete picture of user behaviour. You make a quick decision using ML and rules that deals with the majority of cases. But those constraints make it impossible to precisely prevent all fraud.

Detection deals with the downstream consequences of this. A team of analysts will typically analyse the accepted transactions for signs of fraud. This is particularly important for fraud types where you don't get an external signal like a chargeback or customer complaint. Platform integrity is one such example. But Fintechs will also see this building anti-money laundering systems - you need to go looking for the fraud. This is the process the article is describing.

I say they're complementary because the detected transactions become the labels for training and evaluating the next iteration of prevention models.