Prediction Technologies and Optimal Loss Prevention

Predictions guide important prevention responses, from treating patients in hospitals to pretreating roads before snowstorms. Recent advances in machine learning and artificial intelligence have accelerated improvements in prediction accuracy. However, it is unclear how these improvements reshape preventive strategies and resource allocation. We develop a framework for forecast-based prevention, extending canonical loss-prevention models to explicitly incorporate prediction-based information updates. Our theoretical analysis provides three key insights with practical implications. First, improved predictions shift prevention toward more intense but less frequent responses. Second, as predictions resolve more uncertainty, risk preferences matter less in determining optimal loss-prevention, resulting in greater convergence of preventive strategies. Third, under identifiable conditions, average prevention spending may decline as prediction skill rises, especially for actions with elastic marginal benefits. These results highlight the importance of aligning preventive strategies and resource allocation with evolving prediction capabilities.