  Traditional non-life reserving models largely neglect the vast amount of
information collected over the lifetime of a claim. This information includes
covariates describing the policy, claim cause as well as the detailed history
collected during a claim's development over time. We present the hierarchical
reserving model as a modular framework for integrating a claim's history and
claim-specific covariates into the development process. Hierarchical reserving
models decompose the joint likelihood of the development process over time.
Moreover, they are tailored to the portfolio at hand by adding a layer to the
model for each of the events registered during the development of a claim (e.g.
settlement, payment). Layers are modelled with statistical learning (e.g.
generalized linear models) or machine learning methods (e.g. gradient boosting
machines) and use claim-specific covariates. As a result of its flexibility,
this framework incorporates many existing reserving models, ranging from
aggregate models designed for run-off triangles to individual models using
claim-specific covariates. This connection allows us to develop a data-driven
strategy for choosing between aggregate and individual reserving; an important
decision for reserving practitioners. We illustrate our method with a case
study on a real insurance data set and deduce new insights in the covariates
driving the development of claims. Moreover, we evaluate the method's
performance on a large number of simulated portfolios representing several
realistic development scenarios and demonstrate the flexibility and robustness
of the hierarchical reserving model.
