What is the best metric to evaluate highly imbalanaced binary classifiction? (such as fraud detection in credit card)
Simple precision/recall/F-score is perfectly suitable for imbalanced data. It should be computed on the minority class of course.


precision says how often the system is correct when predicting an instance in the minority class.
recall says how often the system detects an instance which belongs to the minority class (this is usually the hardest part with imbalanced data)
As usual F-score can be used as a "summary metric" of both precision and recall.


ROC and AUC (which are also based on precision/recall) can be used only with a soft classifier, i.e. when the system predicts a numerical value instead of a binary label.

Macro/micro/weighted average (usually over F-score) are irrelevant: averaging over the two classes would take into account the majority class which is very easy to predict, so it would mask the useful information about the minority class.