Tree-Based Machine Learning Methods For Vehicle Insurance Claims Size Prediction
This work addresses the problem of efficient claims handling for vehicle insurance providers, but it is incremental as it applies existing tree-based methods to a specific domain without introducing new techniques.
The study tackled vehicle insurance claims size prediction by applying tree-based ensemble machine learning methods like bagging, random forest, and gradient boosting to insurance big data, finding that these methods outperform the classical least squares approach.
Vehicle insurance claims size prediction needs methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solve this problem. Tree-based ensemble learning algorithms are highly effective and widely used ML methods. This study considers how vehicle insurance providers incorporate ML methods in their companies and explores how the models can be applied to insurance big data. We utilize various tree-based ML methods, such as bagging, random forest, and gradient boosting, to determine the relative importance of predictors in predicting claims size and to explore the relationships between claims size and predictors. Furthermore, we evaluate and compare these models' performances. The results show that tree-based ensemble methods are better than the classical least square method. Keywords: claims size prediction; machine learning; tree-based ensemble methods; vehicle insurance.