To rigorously evaluate the model's performance using comprehensive metrics such as accuracy, recall, and precision, ensuring it aligns with the business's high standards and regulatory requirements.

To indiscriminately collect and utilize all available customer data, including highly sensitive financial information, without implementing necessary safeguards or considering data privacy laws.

To accurately predict which customers are at risk of churning, thereby enabling the company to deploy targeted and effective retention strategies that comply with financial regulations.

To meticulously preprocess the data by normalizing and encoding features, ensuring the model's input is optimized for training while adhering to data privacy standards.

Both A and C are correct because evaluating model performance is as critical as making accurate predictions for the project's success, especially in a regulated financial environment.