Neuronal networks applied to chain-ladder reserving

Speaker: Mario V. Wüthrich

Classical claims reserving works on so-called claims reserving triangles which are aggregated insurance portfolios. A crucial assumption in classical claims reserving is that these aggregated portfolios are sufficiently homogeneous so that a coarse algorithm can be applied. We start from such a coarse method, which in our case is Mack's chain-ladder model, and show how this model can be refined for heterogeneity and individual claims feature information using neural networks.