There is a growing interest in prevention in several policy areas and this provides a strong motivation for an improved integration of forecasting with machine learning into models of decision making. In this article we propose a framework to tackle conflict prevention. A key problem of conflict forecasting for prevention is that predicting the start of conflict in previously peaceful countries needs to overcome a low baseline risk. To make progress in this hard problem this project combines a newspaper-text corpus of more than 4 million articles with unsupervised and supervised machine learning. The output of the forecast model is then integrated into a simple static framework in which a decision maker decides on the optimal number of interventions to minimize the total cost of conflict and intervention. This exercise highlights the potential cost savings of prevention for which reliable forecasts are a prerequisite.