Performance Evaluation of Financial Industry Related Expense Forecasting Using Various Regression Algorithms for Machine Learning

Abstract

Insurance costs refer to the fees charged by insurance companies to customers to pay for possible risks and losses. Insurance costs are usually based on the personal information of the insured, such as age, gender, occupation, health status and so on. For insurance companies, it is very important to accurately predict insurance costs, because it is directly related to the company's profits and risk control capabilities. The purpose of using regression algorithm to predict insurance expenses is to make insurance companies evaluate customers' risks more accurately and make more reasonable insurance expenses, so as to better manage risks and improve the company's profitability. In addition, for individuals, knowing their own insurance cost forecast results will also help them make better decisions and choose the most suitable insurance products to protect themselves and their families.In order to improve the pricing accuracy and profit rate of insurance companies, this study uses regression algorithm to predict insurance costs. It uses real anonymous data sets, which contain information of the insured from different regions, different ages, different sexes and different smoking status. It uses the comparison algorithm function of regression algorithm, which contains dozens of algorithms and covers all regression algorithms and compare their prediction performance. Our data set takes into account various factors that affect the insurance cost, such as age, gender, body mass index, smoking status and so on. And add them to the model as independent variables. It uses cross-validation to evaluate the generalization ability of the model and R2 index to evaluate the prediction performance. The results show that GBR has the best prediction performance, with R2 of 87%. Our research provides an accurate method for insurance companies to predict insurance costs, which is helpful for insurance companies to formulate more reasonable pricing strategies and improve market competitiveness.