Published Paper Details:

In the contemporary landscape, both insurance companies and customers (insurers) are increasingly motivated to secure the most advantageous deals, particularly in the realm of health insurance where the need for coverage is of paramount concern. Customers, in particular, exhibit a heightened sense of caution when evaluating health insurance options. Fuzzy logic is a computational approach that deals with uncertainty and imprecision, has emerged as a versatile solution across various real-world domains such as electronics, computer science, finance and medical science. This study offers a comprehensive exploration of the application of fuzzy sets methodologies within the field of actuarial science, focusing on health insurance risk assessment. It delves into the foundational mathematics underpinning fuzzy sets, providing a quick overview of their development and utilization. Moreover, the study demonstrates how fuzzy logic models can be effectively constructed and refined to adapt to changing rules and incorporate supplementary data, thus enhancing their predictive accuracy and practical utility. Here a model is presented, which is based on fuzzy expert system that will help the insurance companies to find out predictive modeling of health insurance premiums for cardiac patients using Fuzzy Inference Techniques. Through the application of fuzzy logic techniques, the study aims to derive actionable insights that can inform more informed decision-making processes in the realm of health insurance.