  We present a model-agnostic framework for the construction of 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 framework effectiveness is showcased
with simulated and real datasets using classical parametric models and
contemporary machine learning methods. When the underlying severity model is a
random forest, we extend the two-stage split conformal prediction algorithm,
showing how the out-of-bag mechanism can be leveraged to eliminate the need for
a calibration set in the conformal procedure.
