  Two-part models are important to and used throughout insurance and actuarial
science. Since insurance is required for registering a car, obtaining a
mortgage, and participating in certain businesses, it is especially important
that the models which price insurance policies are fair and non-discriminatory.
Black box models can make it very difficult to know which covariates are
influencing the results. SHAP values enable interpretation of various black box
models, but little progress has been made in two-part models. In this paper, we
propose mSHAP (or multiplicative SHAP), a method for computing SHAP values of
two-part models using the SHAP values of the individual models. This method
will allow for the predictions of two-part models to be explained at an
individual observation level. After developing mSHAP, we perform an in-depth
simulation study. Although the kernelSHAP algorithm is also capable of
computing approximate SHAP values for a two-part model, a comparison with our
method demonstrates that mSHAP is exponentially faster. Ultimately, we apply
mSHAP to a two-part ratemaking model for personal auto property damage
insurance coverage. Additionally, an R package (mshap) is available to easily
implement the method in a wide variety of applications.
