Insurance Sales Forecast Using Machine Learning Algorithms

Abstract

Car accidents and the possible resulting loss of assets or life are issues for every car owner that must contend with some point in their driving life. Driving is an inherently dangerous act, even if it does not seem so at first, resulting in greater than 33,000 fatal vehi le crashes in USA in 2019 alone. However, the loss of life and possible damages can be reduced with the help of insurances. Insurance is an arrangement under which a person or agency receives financial security or reimbursement from an insurance provider in the form of a policy. Insurances help limit the losses of the customers when an undesirable event occurs, such as a car crash or a heart attack. Vehicle insurance provides customers monetary compensation after unfortunate accidents, provided they annually pay premium fees to the companies first. Our goal is to develop a machine learning algorithm that predicts customers who are interested in getting or renewing their vehicle insurance with the help of personal, vehicle, contact, and previous insurance data. The insurance sales forecast is helpful to companies, since they can then accordingly plan its communication strategy to reach out to those customers and optimize its business model and revenue, while also being beneficial to customers, who can go through the process and the aftermath of car accidents easier thanks to their monetary compensation. In this paper, the Health Insurance Cross-Sell Prediction dataset is used. The proposed model tries getting the value by training itself on a train and test dataset and will result in a categorical response feature based on the aforementioned data with the aid of well-known machine learning algorithms: k-nearest neighbors, random forest, support vector machines, Naive Bayes, and logistic regression.