Prediction of Insurance Premium using Machine Learning with an Adaptive Approach

Abstract

The insurance market is very large and expanding day by day. There are many parameters to consider before deciding on insurance premiums. Sometimes it becomes difficult to browse all the documents before applying for insurance, so it is necessary to understand the insurance industry and point out issues related to competition in that industry. This type of company is very interested in forecasting. The goal of this article is to find accurate predictions based on considering different dimensions of machine learning to reduce the company's financial losses. Machine learning helps companies to optimize their services with greater accuracy and fewer losses. It can also help insurance companies effectively screen cases, evaluate them more accurately, and make accurate cost forecasts. This research work uses machine learning-based methods like linear regression, KStar, and Random Forest and suggests a suitable method to produce results with high accuracy and less relative error. In addition to this, it demonstrates how to create a specific data subset that can be used to test and train a machine learning system. The effectiveness of the suggested strategy is assessed by contrasting the estimated value with the actual value of the simulated data. Insurance firms are capable to construct consistent financial structures, such as monthly premiums or payroll taxes, to provide funds to pay for the medical benefit agreements that are defined in insurance policies by calculating the whole risk of the expenses associated with health care and the medical system.