Machine Learning and Frequency–Severity Decomposition for Insurance Pricing

Insurance pricing plays a central role in risk management and financial decision-making, 2 as accurate premium estimation directly impacts portfolio stability and profitability. This 3 study investigates insurance pure premium estimation by integrating classical actuar- 4 ial models with modern machine learning techniques. We compare the traditional fre- 5 quency–severity decomposition framework with direct modeling approaches, including 6 XGBoost and Tweedie models. For claim frequency, we evaluate Poisson-based models, 7 generalized additive models, and XGBoost. For claim severity, we compare a Gamma gen- 8 eralized linear model with XGBoost. The results show that XGBoost significantly improves 9 predictive performance for both components. Within the decomposition framework, the 10 XGBoost–XGBoost model achieves the best overall prediction accuracy. However, lift-based 11 analysis reveals that the XGBoost–Gamma model provides superior risk segmentation, 12 highlighting a trade-off between prediction accuracy and risk ranking. Direct modeling 13 approaches, while competitive, do not outperform the decomposition framework. Overall, 14 the findings demonstrate that machine learning enhances predictive performance, but its 15 effectiveness is maximized within the frequency–severity framework. The results further 16 indicate that claim frequency is the primary driver of risk differentiation, while claim sever- 17 ity contributes more to prediction accuracy. These findings have important implications for 18 risk management and pricing strategies in insurance portfolios.