To expedite the model deployment process by randomly selecting hyperparameters, thereby meeting the regulatory deadlines without thorough validation.

To systematically search for the optimal combination of hyperparameters that maximizes the model's performance on validation data, ensuring it meets both accuracy and compliance standards, while also considering computational efficiency.

To preprocess the financial data to remove any sensitive information before model training begins, focusing solely on data privacy without addressing model performance.

To select the most relevant dataset from a pool of available financial datasets that complies with regulatory requirements, ignoring the model's hyperparameter configuration.