Acturial Analysis with Risk Updating: Case Study of Insurance Premium Calculation for Mechanical Vehicles

Abstract

This paper introduces an innovative approach to premium calculation in actuarial science by incorporating the concept of risk updating. Traditional methods rely on static data and assumptions, but with advancements in data collection and statistical techniques, there is an opportunity to redefine premiums based on continuously gathered client information. The objective of this research is to develop a dynamic framework for premium calculation that adapts to changes in individual risk profiles. By leveraging clientspecific data and statistical tools, insurers can adjust premiums to reflect evolving risks. Through a comprehensive review of existing literature and incorporation of advanced statistical methodologies, this research proposes a method to accurately model various risk factors, including demographics, claim history, and behavior patterns. Key considerations include addressing concerns regarding data privacy, accuracy of predictions, and monitoring changes in risk over time. The benefits of this approach include improved risk management, enhanced customer satisfaction, and fairer pricing based on individual risk profiles. By introducing a dynamic and adaptive method for premium calculation, this research revolutionizes actuarial science. It highlights the importance of real-time data and statistical analysis in determining accurate pricing models and optimizing risk portfolios.