Predictive modeling for claims in automobile insurance

The rise of advanced machine learning methods has revolutionized the landscape of predictive modeling in the automobile insurance sector. This paper presents the relevant literature review on the use of machine learning methods, including gradient boosting, random forests, and decision trees, to model claims in automobile insurance. By synthesizing findings from key studies, we conclude on the predictive performance of these methods compared to traditional actuarial models and identify emerging trends and challenges in this domain. Our analysis highlights how data-driven approaches enhance pricing accuracy, optimize risk assessment, and improve operational efficiency. Furthermore, the paper addresses critical issues such as model interpretability, fairness, and ethical considerations in adopting machine learning technologies. This literature review contributes to the ongoing discourse on improving automobile insurance practices through predictive analytics and provides a foundation for future research.