INSURANCE PREMIUM PREDICTION USING MACHINE LEARNING

Abstract

The accurate estimation of insurance premiums is vital for insurers to maintain competitiveness and financial stability. Traditional methods often struggle to account for individual risk factors, necessitating more advanced, datadriven approaches.This research harnesses machine learning (ML) techniques to construct a robust model for precise premium prediction, with the objective of optimizing insurance underwriting procedures. By conducting an extensive literature review, gathering and preprocessing data, and developing sophisticated models, we significantly enhance

accuracy and reliability. Ethical considerations are paramount throughout the research process to ensure responsible and fair utilization of ML technologies. By leveraging our findings, insurers gain actionable insights that facilitate

informed decision-making in a dynamic and intricate marketplace.Our study bridges the gap between traditional underwriting methods and modern data analytics, offering a novel framework for insurers to adapt to evolving risk landscapes. The integration of ML enables the identification of subtle risk patterns, leading to more tailored and precise premium estimations.Ultimately, our research empowers insurers to enhance their competitive edge while

maintaining financial sustainability. By embracing data-driven approaches, insurers can better navigate complexities within the insurance industry, ultimately benefiting both companies and policyholders.