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arXiv · 2307.13124

Conformal prediction for frequency-severity modeling

Helton Graziadei·Paulo C. Marques F.·Eduardo F. L. de Melo·Rodrigo S. Targino
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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.

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Helton Graziadei, Paulo C. Marques F., Eduardo F. L. de Melo, Rodrigo S. Targino. 2023-07-24. Conformal prediction for frequency-severity modeling. https://doi.org/10.1080/02664763.2025.2567988

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