Machine Learning Enhancements for Car Insurance Claim Prediction

One of the biggest concerns of car insurance industry is efficiently managing car insurance claims while minimizing fraudulent submissions. This study explores the use of machine learning models in the prediction of auto insurance claims. Unlike traditional rule-based systems, which often struggle with intricate patterns and dynamic data, machine learning offers powerful, efficient, and often more accurate methods for predicting car insurance claims. By analyzing historical data on car insurance claims, various machine learning algorithms can be employed to develop a robust predictive model. This research highlights the pivotal role of machine learning in revolutionizing the insurance landscape. Accurate predictions empower insurers to allocate resources efficiently, streamline processes, and ultimately enhance the experience for policyholders. This study focuses primarily on enhancing claim processing efficiency. In terms of accuracy, we found the XG Boost performs the best classifier with an accuracy of 0.84.