Abstract
Insurance pricing requires pragmatism and creativity due to the unpredictable nature of risk [3]. This paper explores Bayesian hierarchical models to model health insurance premiums using individual and group predictors like demographics, health status, and geography. Data from Kaggle on health insurance policyholders was utilized, with prior distributions enhancing model interpretability and credibility. Bayesian models improve predictive accuracy and provide valuable insights for actuaries and policymakers, highlighting the signifcant impact of factors such as age and BMI on premium pricing.
Semester
2024
STARS Citation
Kongyir, Bennedict and Agbemade, Emil, "Modeling Health Insurance Premium Using Bayesian Hierarchical Models" (2024). Data Science and Data Mining. 25.
https://stars.library.ucf.edu/data-science-mining/25
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