We present a nonparametric model-agnostic framework for building prediction
intervals of insurance claims, with finite sample statistical guarantees,
extending the technique of split conformal prediction to the domain of
two-stage frequency-severity modeling. The effectiveness of the framework is
showcased with simulated and real datasets. When the underlying severity model
is a random forest, we extend the two-stage split conformal prediction
procedure, showing how the out-of-bag mechanism can be leveraged to eliminate
the need for a calibration set and to enable the production of prediction
intervals with adaptive width