Conference Agenda

Risk reserving is a fundamental task in non-life insurance and is performed on a regular basis. It is typically carried out using parametric estimation and prediction methods applied to aggregated data structured in so-called run-off triangles. In this talk, we present nonparametric, functional-based reserving alternatives that rely on the completion of MNAR functional segments in the underlying run-off triangles.

In addition to the theoretical and methodological framework, we focus on algorithmic details implemented in the recent R package ProfileLadder. The package offers a flexible and computationally efficient tools for pointwise and distributional reserve prediction and includes relevant visualization and diagnostic tools implemented via standard S3 methods. These nonparametric approaches provide modern, transparent, and extensible alternatives to classical reserving methods used by researchers, actuarial scientists, or insurance practitioners.