Exploring the Potential of Machine Learning Techniques for Predicting Travel Insurance Claims: A Comparative Analysis of Four Models

Abstract

Travel insurance is a crucial component for any traveller as it offers protection against financial losses resulting from unforeseen events during a trip, such as trip cancellations, medical emergencies, lost luggage, and related issues. This study aims to investigate the potential of machine learning (ME) techniques for predicting the probability of travel insurance claims. In order to tackle the issue of managing extensive and intricate datasets, advanced statistical techniques were employed, including keyword extraction, feature extraction, and Chi-squared tests. Our evaluation of four popular ML models, namely balanced random forest (BRF), support vector machines (SVM), logistic regression (LR), and balanced bagging (BB), highlight that the BRF model outperforms the other models in predicting travel insurance claims. Our study emphasises the advantages of utilising machine learning algorithms in processing large datasets, producing predictions on future insurance claims, and adapting to changing circumstances, thus serving as a valuable tool for practitioners in the travel insurance industry.