Neural Networks Applied to Chain-Ladder Reserving

Abstract

Classical claims reserving methods act 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 reserving algorithm can be applied. We start from such a coarse reserving method, which in our case is Mack's chain-ladder method, and show how this approach can be refined for heterogeneity and individual claims feature information using neural networks.