Research on Accurate Recommendation of Personal Accident Insurance based on Actuarial Theory

Abstract

This paper explores the development of a precise recommendation system for personal accident insurance grounded in actuarial principles. As societal needs for risk management and tailored services increase, the challenge of effectively recommending insurance products to specific customers has emerged as a pressing issue within the insurance sector. Initially, this study examines the context and importance of researching personal accident insurance, followed by an analysis of the accident insurance market to grasp the current state of recommendation systems. Additionally, it investigates how data and machine learning can facilitate accurate recommendations for insurance products. By gathering and evaluating historical data alongside customer behavior patterns and preferences, a personalized recommendation framework is established. This system aims to deliver customized insurance options based on various customer attributes such as age, gender, occupation, health condition, and individual needs for coverage—ultimately enhancing customer satisfaction. Findings indicate that an accurate recommendation system rooted in actuarial science significantly boosts the precision of personal accident insurance suggestions while offering a solid foundation for pricing strategies and effective marketing approaches for insurers. Looking ahead, with ongoing technological advancements and data accumulation, the role of actuarial science in refining personal accident insurance recommendations will likely expand further, providing robust support for sustainable growth within the industry.