LEVERAGING THE HEALTH INSURANCE RATES MODELLING METHODS TO DEVISE A PREDICTIVE ANALYTICS MODEL TO ENHANCE THE HEALTH CARE FUNDS

Abstract

The primary focus of this study is the development of predictive models for precise forecasts of medical insurance premiums, which aims to improve healthcare finance. The study creates models that enable insurance companies to price policies competitively while balancing fairness and profitability. A substantial dataset and cutting-edge analytics are used to achieve this. The review inspects the impact of critical factors like age, orientation, BMI, and local medical services costs on premium assessments, utilizing a scope of cutting-edge AI techniques, including slope helping, choice trees, irregular backwoods, and direct relapse. Predictive modelling has the potential to improve insurance pricing and risk management strategies, as shown by our findings. The report additionally highlights the useful ramifications for the medical care protection industry, featuring how information-driven methodologies can elevate impartial admittance to medical care and upgrade functional effectiveness. This research makes a significant contribution to the field of insurance analytics by examining the factors that influence premiums, determining the most efficient modelling methods, and outlining the significant benefits for both insurers and policyholders.