Machine learning is widely used to support decision-making in financial institutions. Credit scorecards are a prominent example. Such models are trained over the labeled data of previously accepted applicants, whose repayment behavior has been observed and ignore the rejected applicants. 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.