# Neural networks applied to chain–ladder reserving

Original Research Paper

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## 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## Notes

### Acknowledgements

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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© EAJ Association 2018