16:00 - "A flexible hierarchical insurance claims model with gradient boosting and copulas" 

Marie-Pier Côté (l’université Laval) 

A flexible hierarchical insurance claims model with gradient boosting and copulas 

Abstract:
Predicting future claims is an important task for actuaries, and sophisticating the claim modeling process allows insurers to be more competitive and to stay financially sound. We propose a hierarchical claim model that refines traditional methods by considering dependence between payment occurrences with a multinomial distribution and between payment amounts with copulas. We perform prediction with covariates using XGBoost, a scalable gradient boosting algorithm, gaining predictive power over the frequently used generalized linear models. The model construction and fitting is illustrated with a real auto insurance dataset from a large Canadian insurance company. The use of XGBoost is well suited for such big data containing a lot of insureds and covariates. Since the validity of the copula inference with gradient boosting margins has not been demonstrated in past literature, we perform a simulation study to assess the performance of methods based on ranks of residuals. We show some applications of our model and compare the performance with reference models