In financial institutions, ML-based credit scorecards are only trained over the labeled data of previously accepted applicants, whose repayment behavior has been observed. This creates sampling bias: the training data represent a limited region of the distribution on which the model is deployed for screening new customers. In this talk, I will illustrate the adverse impact of sampling bias on training and evaluation of scoring models. I will also overview possible methods to address this problem.