  Machine learning methods have garnered increasing interest among actuaries in
recent years. However, their adoption by practitioners has been limited, partly
due to the lack of transparency of these methods, as compared to generalized
linear models. In this paper, we discuss the need for model interpretability in
property & casualty insurance ratemaking, propose a framework for explaining
models, and present a case study to illustrate the framework.
