Fraud and failure-scale imbalance, resampling and class weights, why accuracy is meaningless here, and evaluating at the operating point you will run.
3 questions in Machine Learning.
Work backwards from the review capacity. Fix the alert budget the team can clear, evaluate precision and recall at exactly that budget on data at the real prevalence, define the label and its arrival delay before modelling, and never report accuracy, which a do-nothing model scores 99.95% on.
Derive it from the two error costs. Acting is worthwhile when its expected cost falls below the cost of inaction, which gives a threshold of the false-positive cost over the sum of both. With calibrated probabilities that threshold is fixed as prevalence moves; under a fixed alert budget it must move.
None of them adds information. Weights change the loss without touching the data, oversampling duplicates rows and inflates any score computed after it, undersampling discards real observations, and SMOTE interpolates implausible points. All shift the implied prior, so the scores need recalibrating.