Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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A study on the Bayesian nonparametric model for predicting group health claims

  • Muna Mauliza (Department of Statistics and Actuarial Science, Soongsil University) ;
  • Jimin Hong (Department of Statistics and Actuarial Science, Soongsil University)
  • Received : 2023.09.07
  • Accepted : 2023.12.26
  • Published : 2024.05.31
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Abstract

The accurate forecasting of insurance claims is a critical component for insurers' risk management decisions. Hierarchical Bayesian parametric (BP) models can be used for health insurance claims forecasting, but they are unsatisfactory to describe the claims distribution. Therefore, Bayesian nonparametric (BNP) models can be a more suitable alternative to deal with the complex characteristics of the health insurance claims distribution, including heavy tails, skewness, and multimodality. In this study, we apply both a BP model and a BNP model to predict group health claims using simulated and real-world data for a private life insurer in Indonesia. The findings show that the BNP model outperforms the BP model in terms of claims prediction accuracy. Furthermore, our analysis highlights the flexibility and robustness of BNP models in handling diverse data structures in health insurance claims.

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