Joint Distributional Copula Regression for Insurance Claim Frequency and Severity via Boosting

Banghee So, Na Zhang

In this paper, we introduce a model-based boosting approach for fitting structured additive distributional copula regression models for bivariate responses, with particular emphasis on jointly modeling insurance claim frequency and severity. The approach uses regression-type base learners, such as linear terms and splines with low degrees of freedom. It builds on generalized additive models for location, scale, and shape (GAMLSS), which extend generalized additive models (GAMs) by allowing multiple distributional parameters, rather than only the conditional mean, to depend on covariates. The structured additive predictors make the model inherently interpretable, offering greater transparency and auditability than black-box approaches. These features are particularly relevant in regulated actuarial settings and applications requiring explainable artificial intelligence. By estimating the entire conditional joint distribution, the framework also enables insurance practitioners to examine conditional quantiles, tail behavior, and prediction intervals of risks. The copula component captures dependence between claim frequency and severity and allows practitioners to investigate how…