AI for Loan Default Prediction: How It Works and Where It Breaks
Your credit model has never met the people you declined Repayment is observed only for applicants who were approved. They passed your existing policy, so the training data describes the relationship between characteristics and default conditional on having already cleared a cutoff. The model is then applied to everyone who applies, including the population it has never seen. This is the defining structural problem in credit modelling, and no choice of algorithm addresses it. It is also the one most often handled with a technique that sounds like a solution and is actually an assumption. What selection actually breaks Confidence without evidence. The model produces scores for applicants in regions of the feature space where it has no observations. Those scores are extrapolations, and nothing in the output distinguishes them from scores backed by thousands of outcomes. Validation does not rescue you. The holdout comes from the same approved population, so strong validat...