Prediction Technologies and Optimal Loss Prevention: Practical Implications for Decision Making

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.

Funding: This work was supported by the Wisconsin Alumni Research Foundation [Grant UW2020] at the University of Wisconsin–Madison.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2025.0500.