In the aftermath of the COVID-19 pandemic, insurance has become increasingly essential in helping individuals mitigate financial shocks from unexpected adverse events. Nevertheless, insurers face the persistent challenge of premium pricing calibration, a process imperative for maintaining financial solvency and actuarial equity. Among various machine learning techniques, the Bayesian framework stands out due to its unique ability to incorporate new data in real-time, making it particularly suitable for dynamic risk environments. This study conducts a systematic review of Bayesian methodologies, emphasizing their deployment in risk assessment and actuarial pricing. It examines the strengths of Bayesian methods in uncertainty modeling across high-stakes industries, as well as their limitationssuch as computational complexity, lack of interpretability, and sensitivity to prior assumptions. Furthermore, the investigation interrogates cutting-edge innovationssuch as hybrid Bayesian-machine learning hybrids and Bayesian AIdesigned to mitigate aforementioned constraints and extend the operational scope of Bayesian frameworks. This study concludes that while the Bayesian framework offers a powerful approach for dynamic risk modeling, its future practicality hinges on the development of hybrid models that can effectively balance predictive accuracy, interpretability, and computational feasibility. Future research should focus on real-world case studies to further validate these advancements.
Xiaoyi Yang (Wed,) studied this question.