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Adapting the insurance pricing model for distribution channel expansion using the Bayesian generalized linear model

Author

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  • Carina Gunawan
  • Muhammad Ivan Faizal
  • Nanang Susyanto

Abstract

The insurance market is changing due to new distribution channels, requiring insurers to update their pricing models. We propose a mathematical approach using Bayesian GLM to adjust insurance pricing. Our strategy modifies the pricing model by incorporating distribution channels while utilizing the initial model as a baseline. Bayesian generalized linear models (GLM) enable effective model updates while incorporating existing knowledge. We validated our approach using data from the general insurance sector, comparing it with the traditional approach. Results show that Bayesian GLM outperforms the traditional method in accurately estimating pricing. This superiority highlights its potential as a powerful tool for insurers to remain competitive in a rapidly changing market. Our approach makes a significant mathematical contribution to insurance pricing, allowing insurers to adapt to market conditions and enhance their competitive edge.

Suggested Citation

  • Carina Gunawan & Muhammad Ivan Faizal & Nanang Susyanto, 2024. "Adapting the insurance pricing model for distribution channel expansion using the Bayesian generalized linear model," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(4), pages 67-79.
    • Handle: RePEc:wut:journl:v:34:y:2024:i:4:p:67-79:id:4
    DOI: 10.37190/ord240404
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    References listed on IDEAS

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    1. Oskar Tufvesson & Johan Lindström & Erik Lindström, 2019. "Spatial statistical modelling of insurance risk: a spatial epidemiological approach to car insurance," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2019(6), pages 508-522, July.
    2. Ida Scheel & Egil Ferkingstad & Arnoldo Frigessi & Ola Haug & Mikkel Hinnerichsen & Elisabeth Meze-Hausken, 2013. "A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(1), pages 85-100, January.
    3. A. Racine & A. P. Grieve & H. Flühler & A. F. M. Smith, 1986. "Bayesian Methods in Practice: Experiences in the Pharmaceutical Industry," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(2), pages 93-120, June.
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