Conformal prediction for frequency-severity modeling
Methodology
2025-10-29 v4 Machine Learning
Machine Learning
Abstract
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.
Cite
@article{arxiv.2307.13124,
title = {Conformal prediction for frequency-severity modeling},
author = {Helton Graziadei and Paulo C. Marques F. and Eduardo F. L. de Melo and Rodrigo S. Targino},
journal= {arXiv preprint arXiv:2307.13124},
year = {2025}
}