Can't really talk about features on here. Any smart fraudster should be watching every single thing I say :)
We're using logistic regression not because it performs the best, but because it's the most understandable. When cases get flagged for manual review people need to know exactly what seems dodgy about the account, and with Logistic Regression you can read the exact contribution from each feature to the final fraud probability. Seen as the features mean something real and tangible (unlike in neural nets), this means a manual reviewer immediately knows which aspects of someone's behaviour are out of the ordinary when they get presented with a new case (we have a really nice internal UI for presenting this). This saves several minutes per case which really adds up.
Performance-wise Logistic Regression is good, but it can't automatically learn non-linearities in a feature value and its propensity for fraud, and it can't learn about two features that together should indicate a probability of fraud greater than the sum of its parts* . If this becomes a problem for us we'll start looking into nonlinear models where the inner workings are somewhat communicable to the manual review team.
* You can alter feature definitions manually to capture nonlinearities (e.g. a feature which is "user_has_done_x_and_has_done_y_too", but this is very very manual, and needs to be potentially rewritten/manually re-optimised on every retrain. We don't do this.
Just a note on human readability of models: for sure glm gives you a human readeable representation for "free" but there are many ways to get the same kind of readability for neural Networks. Great article, though, cheers!
Also, how did you settle on logistic regression? Have you tried any other models?