You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. Given the nature of fraud detection, the dataset is highly imbalanced with a far greater number of legitimate transactions compared to fraudulent ones. You need to prioritize detection of fraudulent transactions while minimizing false positives to avoid unnecessarily flagging valid transactions. Which optimization objective should you use when training the model?