Under-reporting in cyber incidents is a well-established problem. Due to reputational risk and the consequent financial impact, a large proportion of incidents are never disclosed to the public, especially if they don’t involve a breach of protected data. Generally, the problem of under-reporting is solved through a proportion-based approach, where the level of under-reporting in a data set is determined by comparison to data that is fully reported. In this work, cyber insurance claims data is used as the complete dataset. Unlike most other work, however, our goal is to quantify under-reporting with respect to multiple dimensions: company revenue, industry, and incident categorization. We show that there is a dramatic difference in under-reporting—a factor of 100—as a function of these variables. The output of this work is an under-reporting model that can be used to correct incident frequencies derived from data sets of publicly reported incidents. This diminishes the “barrier to entry” in the development of cyber risk models, making it accessible to researchers who may not have the resources to acquire closely guarded cyber insurance claims data.