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Abstract: This research delves into optimizing healthcare finance through predictive modeling to forecast medical insurance premiums accurately. By harnessing a robust dataset and integrating advanced analytics, this study meticulously constructs models that allow insurance companies to price their policies competitively, ensuring both profitability and fairness. Employing a variety of machine learning algorithms, including linear regression, decision trees, random forests, and gradient boosting, we thoroughly assess the influence of critical factors such as age, BMI, gender, and regional healthcare costs on premium costs. Our analysis not only showcases the precision of predictive modeling in refining insurance pricing strategies and risk management but also illuminates its broader implications for the healthcare insurance sector. By systematically exploring the factors affecting premiums, identifying the most efficacious modeling techniques, and delineating the potential benefits for insurers and policyholders, this paper significantly contributes to the ongoing discourse on leveraging data-driven approaches to enhance the insurance industry's operational efficiency and promote equitable access to healthcare coverage.
Kavya Chandran (Sun,) studied this question.