Machine Learning in Nested Simulations Under Actuarial Uncertainty

Gilberto Castellani (), Ugo Fiore (), Zelda Marino (), Luca Passalacqua (), Francesca Perla (), Salvatore Scognamiglio () and Paolo Zanetti ()
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Gilberto Castellani: Sapienza University of Rome
Ugo Fiore: University of Naples Parthenope
Zelda Marino: University of Naples Parthenope
Luca Passalacqua: Sapienza University of Rome
Francesca Perla: University of Naples Parthenope
Salvatore Scognamiglio: University of Naples Parthenope
Paolo Zanetti: University of Naples Parthenope

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2021, pp 137-143 from Springer

Abstract: Abstract The Solvency II directive states that in order to be solvent the insurance undertakings must to hold eligible own funds covering the Solvency Capital Requirement (SCR), which is defined as the Value-at-Risk of the NAV probability distribution (PDF in the directive) at a confidence level of 99.5% over a one-year period. The estimation of the SCR requires the evaluation of the NAV (under risk-neutral probabilities) conditionally to the economic and actuarial scenarios estimated under real-world probabilities and involve nested Monte Carlo simulations. This approach usually presents unacceptable computational costs. In this paper we analyse the performance of Machine Learning techniques on some insurance portfolios considering a multivariate stochastic model for actuarial risks including mortality, lapse and expense risks. Experiments are aimed not only to analyse the performance of these techniques in a large-dimensional risk framework, but also to investigate variability and robustness of the obtained estimations.

Keywords: Solvency capital requirement; Life insurance; Support vector machines; Deep neural networks; Actuarial risks (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-78965-7_21

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

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