Modeling Health Insurance Premium Using Bayesian Hierarchical Models

Author(s)

Bennedict Kongyir, Oklahoma State University - Main CampusFollow
Emil Agbemade, University of Central FloridaFollow

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

Accessibility Status

PDF accessibility verified using Adobe Acrobat Pro Accessibility Checker