Life Insurance Customer Prediction and Sustainbility Analysis Using Machine Learning Techniques

Abstract

The insurance sector is being transformed through the incorporation of cutting-edge technology like Machine Learning (ML) to advance operational efficiency, customer behavioural prediction, and sustainability. In this paper we carry out a systematic analysis on the utilization of ML techniques to predict the behavior of life insurance customers and study its sustainability effect in the industry. Using multiple ML techniques such as decision trees, support vector machines and neural networks, the model attempts to forecast;customer retention; claims and chances of a customer renewing a policy given historical data. Furthermore, the paper discusses how sustainable operations in life insurance firms could be improved through predictive analytics, eco-friendly operations and building customer loyalty in the form of green policies. The findings indicate a great prospects of ML model for predicting customer behaviorss as well as sustainable growth. In light of the emerging body of knowledge on data analytics in the insurance industry, this research provides important insights into both data-driven customer retention strategies and environmentally friendly actions.