You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.
What should you do?

A. Increase the size of the dataset by collecting additional data. B. Train a linear regression to predict a credit default risk score. C. Remove the bias from the data and collect applications that have been declined loans. D. Match loan applicants with their social profiles to enable feature engineering.