Modelling of Vehicle Insurance Claim with Stochastic Process using Hidden Markov Model

Abstract

The modeling of vehicle insurance claims plays a crucial role in the insurance industry to assess risk and determine appropriate premiums. However, existing research often relies on deterministic methods, neglecting the stochastic nature of claim occurrences and causing inaccuracies in risk estimation. This paper addresses the limitations in current research by proposing a novel approach using Hidden Markov Model to incorporate stochastic processes into the modeling of vehicle insurance claims. By capturing the dynamic and unpredictable nature of claim occurrences, the proposed model offers a more accurate representation of risk factors and enhances the predictive capabilities in insurance claim analysis. Through the integration of Hidden Markov Model, this study provides a innovative framework for modeling vehicle insurance claims that has the potential to significantly improve the efficiency and accuracy of risk assessment in the insurance industry.