Neural Networks Applied to Chain-Ladder Reserving

26 Pages Posted: 10 May 2017 Last revised: 19 Jul 2018

Date Written: July 6, 2018

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.

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

Wuthrich, Mario V., Neural Networks Applied to Chain-Ladder Reserving (July 6, 2018). Available at SSRN: https://ssrn.com/abstract=2966126 or http://dx.doi.org/10.2139/ssrn.2966126

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

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