Non-life insurance: The state of the art of determining the superior method for pricing automobile insurance premiums using archival technique

Abstract

The pricing of insurance premiums in the non-life insurance sector remains a challenging and complex task. It demands a delicate balance between accurately estimating risk exposure and ensuring profitability for insurers. Generalised Linear Regression Models (GLMs) have become the preferred methods for premium price modelling in the motor insurance sector. While the approach of using a single superior model on which predictions are based ignores the use of robust estimator models. This paper examines various methodologies and sheds light on superiority of twenty-two models compared to each other for pricing automobile insurance. These methods vary from traditional actuarial methods to the modern statistical models such as machine learning algorithms. By using archival technique, their inferiority and superiority are explored, considering the ever-changing landscape of risk factors and market dynamics. Furthermore, it highlights the potential benefits of leveraging these methods and the mechanism for pricing short-term insurance, particularly in motor vehicle insurance. It also develops a framework that can be used in pricing to cater to risk analysis constituents to mitigate uncertainties and provide good services to clients. Our findings show that ANN, NN, XGB, random forest (RF) are superior models, and we conclude that the modern statistical methods can accurately estimate the risk exposure as compared to traditional methods such as the GLMs.