As a professional working for a credit card company, you're tasked with developing a custom fraud detection model using AutoML Tables, based on historical data. Your primary goals are to maximize the detection of fraudulent transactions while keeping false positives to a minimum. The dataset is highly imbalanced, with fraudulent transactions representing less than 1% of all transactions. Additionally, the company emphasizes the importance of minimizing false positives to avoid customer dissatisfaction and unnecessary fraud investigations. Given these constraints, which optimization objective should you select for training your model to best meet the company's requirements? Choose one correct option.