Precision and Recall
The two metrics that actually tell you whether a classifier works when one class is rare.
Precision asks: of everything the model flagged, how much was right? Recall asks: of everything it should have flagged, how much did it catch? They trade off against each other, and which one you optimise is a product decision, not a technical one. Accuracy hides both.
In practice: A fraud model catching 60% of fraud (recall) with 90% of its alerts genuine (precision) may beat a 99.9%-accurate one that catches nothing.