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

The insurance market is changing due to new distribution channels, requiring insurers to update their pricing models. We propose a mathematical approach using Bayesian generalized linear models (GLM) to adjust insurance pricing. Our strategy modifies the pricing model by incorporating distribution channels while utilizing the initial model as a baseline. Bayesian 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.