An Analytical Approach to Predict Auto Insurance Claim using Machine Learning Techniques

Abstract

Machine Learning business is regular in the insurance market to enhance the efficiency and predictive skills of the insurance industry linear regression as an initial and effective ml method is adopted in this work predicting automobile insurance claim is undertaken using these large datasets which provides the detailed driver characteristics vehicle characteristics and historical claim insights becomes possible to apply linear regression analyses to indicate and predict the likelihood and frequency of upcoming claims to insurers One of the primary motivations for linear regression is a very easy tool has it is pretty simple to use, easy to interpret and at the same time easy to scale it can benefit in managing and resolve the premium pricing and improvement of risk assessment along with the enhancement of the financial stability while applying linear regression the study explains how it can be utilized in auto insurance claims prediction the potential idea of using better ml models for more investigation and its pros and cons this model is mainly assessed in line with its predictive accuracy utilizing metrics reminiscent of mse and r-squared (R2).