Neural Networks Applied to Chain-Ladder Reserving
26 Pages Posted: 10 May 2017 Last revised: 19 Jul 2018
Date Written: July 6, 2018
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.
Keywords: claims reserving, Mack's CL model, individual claims reserving, micro-level reserving, neural networks, individual claims features, claims covariates
JEL Classification: G22, G28, C13, C14, C45
Suggested Citation: Suggested Citation