A burgeoning body of research seeks to develop ever more accurate automated credit evaluation systems. At the same time, reject inference can help financial institutions identify applicants who are mistakenly deemed non–creditworthy, or whose applications are approved even though they end up defaulting. However, both machine learning models and reject inference methods assume perfect decisions, the former for training and the latter for inference. In this work, we challenge this assumption, and explore the feasibility of identifying erroneously rejected (or accepted) loan applications using noisy and counterfactual learning. Experiments on a small benchmark and a large, real– world dataset, demonstrate the effectiveness of our approach.