Bayesian Methods in Risk Assessment and Insurance Pricing: Strengths, Limitations, and Future Trends

Xiangchi Yang

doi:10.54254/2754-1169/2025.GL27550

Advances in Economics, Management and Political SciencesOpen access

Bayesian Methods in Risk Assessment and Insurance Pricing: Strengths, Limitations, and Future Trends

Research Article

Open Access

Bayesian Methods in Risk Assessment and Insurance Pricing: Strengths, Limitations, and Future Trends

Xiangchi Yang ^1*

¹ University of British Columbia

^*Corresponding author: xiangchi@student.ubc.ca

Published on 2 October 2025

AEMPS Vol.222

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-403-8

ISBN (Online): 978-1-80590-404-5

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Abstract

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 limitations—such as computational complexity, lack of interpretability, and sensitivity to prior assumptions. Furthermore, the investigation interrogates cutting-edge innovations—such as hybrid Bayesian-machine learning hybrids and Bayesian AI—designed 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.

Keywords:

Bayesian methods, Risk assessment, Insurance pricing, Uncertainty quantification, Hybrid modeling

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[17]. Pang, S., & Choi, C. (2022). Data-Driven Parametric Insurance Framework Using Bayesian Neural Networks. https: //doi.org/10.48550/arXiv.2209.05307

Cite this article

Yang,X. (2025). Bayesian Methods in Risk Assessment and Insurance Pricing: Strengths, Limitations, and Future Trends. Advances in Economics, Management and Political Sciences,222,24-30.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICFTBA 2025 Symposium: Financial Framework's Role in Economics and Management of Human-Centered Development

ISBN: 978-1-80590-403-8(Print) / 978-1-80590-404-5(Online)

Editor: Lukáš Vartiak, Habil. Florian Marcel Nuţă

Conference website: https://2025.icftba.org/Galati.html

Conference date: 17 October 2025

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.222

ISSN: 2754-1169(Print) / 2754-1177(Online)

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