Machine Learning and Frequency–Severity Decomposition for Insurance Pricing

Insurance pricing plays a central role in risk management and financial decision-making, as accurate premium estimation directly impacts portfolio stability and profitability. This study investigates insurance pure premium estimation by integrating classical actuarial models with modern machine learning techniques. We compare the traditional frequency–severity decomposition framework with direct modeling approaches, including XGBoost and Tweedie models. For claim frequency, we evaluate Poisson-based models, generalized additive models, and XGBoost. For claim severity, we compare a Gamma generalized linear model with XGBoost. The results show that XGBoost improves predictive performance for both components based on the evaluation metrics considered. Within the decomposition framework, the XGBoost–XGBoost model achieves the lowest prediction error among the models considered. However, lift-based analysis reveals that the XGBoost–Gamma model provides superior risk segmentation, highlighting a trade-off between prediction accuracy and risk ranking. Direct modeling approaches, while competitive, do not consistently achieve lower error than the decomposition framework across the evaluation metrics considered. Overall, the findings demonstrate that machine learning enhances predictive performance, but its effectiveness is maximized within the frequency–severity framework. The results highlight the importance of both frequency and severity modeling in insurance pricing, while suggesting that their relative contributions to risk segmentation depend on model specification and evaluation criteria. These findings have important implications for risk management and pricing strategies in insurance portfolios.