Only 1% of transactions are fraudulent. A classifier that always predicts legitimate achieves 99% accuracy. Why is that score insufficient for this use case?

1. It guarantees that precision and recall are both 99%
2. It proves that all fraudulent transactions were identified
3. It means the dataset contains no labels
4. It hides the model's failure to detect any fraudulent transactions