Neural networks meet least squares Monte Carlo at internal model data

Jonen, Christian, Meyhoefer, Tamino and Nikolic, Zoran ORCID: 0000-0002-2133-1533 . Neural networks meet least squares Monte Carlo at internal model data. Eur. Actuar. J.. HEIDELBERG: SPRINGER HEIDELBERG. ISSN 2190-9741

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DOI:10.1007/s13385-022-00321-5

Abstract

In August 2020 we published Comprehensive Internal Model Data for Three Portfolios as an outcome of our work for the committee Actuarial Data Science of the German Actuarial Association. The data sets include realistic cash-flow models outputs used for proxy modelling of life and health insurers. Using these data, we implement the hitherto most promising model in proxy modeling consisting of ensembles of feed-forward neural networks and compare the results with the least squares Monte Carlo (LSMC) polynomial regression. To date, the latter represents-to our best knowledge-the most accurate proxy function productively in use by insurance companies. An additional goal of this publication is a more precise description of Comprehensive Internal Model Data for Three Portfolios for other researchers, practitioners and regulators interested in developing solvency capital requirement (SCR) proxy models.

Item Type:

Journal Article

Creators:

Creators	Email	ORCID	ORCID Put Code
Jonen, Christian	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Meyhoefer, Tamino	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Nikolic, Zoran	UNSPECIFIED	orcid.org/0000-0002-2133-1533	UNSPECIFIED