AI in Banking: Fraud Detection and Risk Management in Practice
Your fraud model is grading its own homework You only observe outcomes for transactions you allowed. Everything the model blocked produces no label at all, so each retraining cycle learns from a dataset shaped by the model's own past decisions. The consequence is directional, not random. The model becomes progressively more confident about a world it helped create, and its blind spots persist because nothing in the data ever contradicts them. Credit has the same structure: declined applicants never demonstrate whether they would have repaid, so the scorecard is validated only on the population it already approved. Most of what matters in bank fraud and risk modelling follows from this problem and the operational limits around it. Your metrics will not show you this Precision and recall computed on observed labels can look excellent while the denominator is quietly wrong. Accuracy is worse than useless, since a model that approves everything scores very well on a rare event and ca...