Risk Analysis And Premium Calculation For Health Insurance

The requirement for thorough risk analysis and precise premium calculation in health insurance grows more crucially as the healthcare industry changes. It is important to examine the evolving landscape of health insurance and highlight the growing need for careful risk analysis and accurate premium calculations as the healthcare industry undergoes significant changes. This research paper introduces an innovative project aimed at addressing the complexities of health insurance by bringing diverse perspectives together. The project initiates a systematic examination of the current state of health insurance, identifying significant barriers and opportunities. By conducting an extensive literature analysis, it sheds light on various risk factors affecting health insurance, encompassing demographic shifts, medical improvements, technological breakthroughs, and epidemiological patterns. This comprehensive understanding forms the basis for crafting a robust framework for risk analysis. Using advanced techniques such as machine learning (ML) and advanced data analytics, the project attempts to assess health risks both at the individual and population levels. The model aims to use datasets of previous claims, socio-economic metrics, and lifestyle indicators to improve prediction accuracy, ultimately contributing to more informed decision-making processes for insurers. Moreover, by aligning pricing strategies with risk profiles, investigation equivalences three regression model’s performance: decision tree regression, multiple linear regression, and random forest (RF) regression, highlighting RF regression algorithm’s superiority. While predicted insurance amount aids in understanding coverage needs, it’s emphasized that accuracy varies and should not be the sole factor in selecting a plan. Nonetheless, estimating insurance costs provides valuable insights for optimizing coverage and maximizing benefits.