Regression Tree Approach to Estimation of Health Insurance Premium

Authors : Başak Bulut Karageyik

Pages : 81-99

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Publication Date : 2023-12-31

Article Type : Research Paper

Abstract :This paper proposes an approach to predicting insurance premiums in health insurance by combining traditional generalized linear models (GLM) with advanced machine learning-driven regression tree analysis. The study first uses GLM on real complementary health insurance data to examine the importance of variables, focusing on those variables that have a large impact on premium estimates. Subsequently, it is investigated whether the variables identified as significant by GLM can also be identified as significant by regression tree analysis. In the application of machine learning, the effect of stratified sampling in accordance with the data structure in terms of the risk variables considered in premium forecasts is also analyzed. This study contributes to the actuarial understanding of premium estimation and provides insurers with a concrete framework to help them negotiate the complex world of health insurance data. By integrating the advantages of GLM and regression trees, this study provides a comprehensive comparison for insurers to adapt to changing risk factors. This study represents a innovative attempt to incorporate a regression tree methodology, providing a novel and accurate estimation of premium amounts in the realm of insurance analysis.
Keywords : Aktüeryal prim tahmini, Regresyon ağacı, Makine öğrenme teknikleri, Genelleştirilmiş doğrusal modeller