In a globalized education market where the stakes of testing are higher than ever, the Economics of Cheating offers a new, data-informed framework for improving test security. By modeling cheating as a rational, cost-sensitive decision, this approach uses real-world data on cheating methods, detection rates, and resource costs to simulate and predict risk. Attendees will learn how interventions like AI detection and secondary cameras shift the economic feasibility of cheating, enabling smarter, more equitable investments in security. This session will demonstrate how predictive modeling and behavioral economics can help institutions stay ahead in the evolving arms race against test fraud, treating security not as a burden, but as a strategic innovation opportunity.