Feature Selection for CASCO Insurance Pricing in Bosnia and Herzegovina: A Machine Learning Approach with XGBoost

Digitalization has expanded insurers' databases, making machine learning (ML) a valuable tool for identifying key rsik factors in casco insurance pricing. While Generalized Linear Models (GLM) remain the standard, modelling claims frequency and severity separately is essential for accurately assessing the impact of each feature. Extreme gradient boosting (XGBoost), an ensemble ML method, can complement traditional GLM by improving predictive accuracy through feature selection. This study investigates a proposed hybrid XGBoost-GLM approach using a real-world dataset of 116.205 casco policies from a Bosnian insurer covering the period 2011-2023. The results show that XGBoost-based feature selection did not outperform the baseline GLM, which included all initial features without any selection. Deviance analysis confirmed that the baseline GLM with the full initial set of features achieved lower deviance and represented the optimal specification for the frequency-severity models. The modelling process was conducted in the R software environment (R Core Team, 2025).