A data scientist trains a scikit-learn binary classifier to detect fraudulent transactions. Only about 2% of transactions are fraudulent, and the cost of missing a fraudulent transaction (false negative) is far higher than the cost of flagging a legitimate one (false positive). Which single evaluation metric should the data scientist prioritize when comparing candidate models?