European Actuarial Journal

, Volume 8, Issue 2, pp 407–436 | Cite as

Neural networks applied to chain–ladder reserving

  • Mario V. WüthrichEmail author
Original Research Paper


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.


Claims reserving Mack’s CL model Individual claims reserving Micro-level reserving Neural networks Individual claims features Claims covariates 



We would like to kindly thank Philipp Reinmann (AXA) and Ronald Richman (AIG) who have provided very useful remarks on previous versions of this manuscript.


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Copyright information

© EAJ Association 2018

Authors and Affiliations

  1. 1.Department of MathematicsETH Zurich, RiskLabZurichSwitzerland

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