I am using logistic regression to create a credit scorecard from past loan data. We will not approve loans in the future if the applicant has an insufficient credit score (no credit or insufficient data for a score from credit bureaus).
These borrowers were however granted loan approvals in the past; therefore, there is extensive data on these loans with insufficient credit scores. Should I include the past loan data of all these borrowers in the logistic regression model to train the model and compute the best model? Or omit them from the model since we will not approve them in the future?