Extreme Imbalance in Fraud Detection

Scenario

You are training a binary classifier to detect ad fraud, where positive examples are very rare and the cost of missing fraud is high. You need a training and evaluation approach that works when standard accuracy can look strong even if the model is not useful.

Question

How do you handle extreme class imbalance when training models for ad fraud detection?