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@article{167121,
        author = {Kushal Pal Singh and Bhopal Singh Sharma},
        title = {Fuzzy Logic and Health Insurance Premiums: A Modern Approach to Pricing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {590-597},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167121},
        abstract = {The estimation of health insurance premiums is a complex task, traditionally based on statistical and actuarial methods. However, these conventional techniques often fail to account for the inherent uncertainty and vagueness in healthcare data. This paper explores the application of fuzzy logic as a modern approach to pricing health insurance premiums. Fuzzy logic, with its ability to handle imprecision and model nonlinear relationships, offers a more flexible and robust framework for premium estimation. By incorporating variables such as age, medical history, lifestyle, and genetic predispositions, fuzzy logic systems can provide more accurate and individualized premium calculations. This approach not only enhances the precision of premium estimates but also improves customer satisfaction by offering fairer pricing. The paper includes a comprehensive review of fuzzy logic principles, the development of a fuzzy inference system for premium estimation, and a comparative analysis with traditional methods. The results demonstrate that fuzzy logic significantly improves the accuracy and reliability of health insurance premium estimations, paving the way for its adoption in the insurance industry.},
        keywords = {Fuzzy Logic, Health Insurance, Premium Estimation, Insurance Pricing, Computational Intelligence},
        month = {August},
        }