Machine Learning in Nested Simulations Under Actuarial Uncertainty

Castellani, Gilberto; Fiore, Ugo; Marino, Zelda; Passalacqua, L.; Perla, Francesca; Scognamiglio, Salvatore; Zanetti, Paolo

doi:10.1007/978-3-030-78965-7_21

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2022

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(2 citation statements)

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“…Recently, in Castellani et al [5] several methods were compared and again deep neural networks have demonstrated the best performance.…”

Section: Related Literaturementioning

confidence: 99%

See 1 more Smart Citation

Neural networks meet least squares Monte Carlo at internal model data

Jonen¹,

Meyhöfer²,

Nikolić

2022

Eur. Actuar. J.

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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.

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“…Recently, in Castellani et al [5] several methods were compared and again deep neural networks have demonstrated the best performance.…”

Section: Related Literaturementioning

confidence: 99%

“…Often the natural model, that is the brain, is used as a motivation for an introduction of neural networks. Although there are some striking analogies between "natural" learning and its "machine" counterpart, 5 this perspective is not really useful for a mathematical understanding of the underlying functions and algorithms.…”

Section: Neural Networkmentioning

confidence: 99%