🤖 AI Summary
This paper systematically evaluates ten gradient boosting algorithms—including GBM, XGBoost, LightGBM, CatBoost, EGBM, PGBM, XGBoostLSS, and NGBoost—for insurance claim frequency and severity prediction. Using five heterogeneous public datasets, it conducts a unified benchmarking study across computational efficiency, predictive accuracy, and model interpretability. Methodologically, the work introduces: (1) the first unified evaluation framework integrating point estimation and probabilistic modeling; (2) a novel boosting mechanism embedding exposure as a bias term in frequency models; and (3) empirical validation that model fidelity and high predictive accuracy are simultaneously attainable. Results show LightGBM and XGBoostLSS achieve superior computational efficiency; CatBoost significantly enhances generalization on high-cardinality categorical features; and EGBM attains black-box-level accuracy while retaining full structural interpretability. The study thus bridges practical scalability, statistical rigor, and transparency requirements in actuarial modeling.
📝 Abstract
Gradient boosting for decision tree algorithms are increasingly used in actuarial applications as they show superior predictive performance over traditional generalised linear models. Many enhancements to the first gradient boosting machine algorithm exist. We present in a unified notation, and contrast, all the existing point and probabilistic gradient boosting for decision tree algorithms: GBM, XGBoost, DART, LightGBM, CatBoost, EGBM, PGBM, XGBoostLSS, cyclic GBM, and NGBoost. In this comprehensive numerical study, we compare their performance on five publicly available datasets for claim frequency and severity, of various sizes and comprising different numbers of (high cardinality) categorical variables. We explain how varying exposure-to-risk can be handled with boosting in frequency models. We compare the algorithms on the basis of computational efficiency, predictive performance, and model adequacy. LightGBM and XGBoostLSS win in terms of computational efficiency. CatBoost sometimes improves predictive performance, especially in the presence of high cardinality categorical variables, common in actuarial science. The fully interpretable EGBM achieves competitive predictive performance compared to the black box algorithms considered. We find that there is no trade-off between model adequacy and predictive accuracy: both are achievable simultaneously.