Conformal prediction for frequency-severity modeling

• URL: http://arxiv.org/abs/2307.13124v3
• Date: Thu, 1 Aug 2024 11:51:44 GMT
• Title: Conformal prediction for frequency-severity modeling
• Authors: Helton Graziadei, Paulo C. Marques F., Eduardo F. L. de Melo, Rodrigo S. Targino,
• Abstract summary: We present a model-agnostic framework for the construction of prediction intervals of insurance claims. We extend the technique of split conformal prediction to the domain of two-stage frequency-severity modeling.
• Score: 1.4999444543328293
• License: http://creativecommons.org/licenses/by/4.0/
• 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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