Study Data from Parthenope University Update Knowledge of Machine Learning (Machine Learning Techniques In Nested Stochastic Simulations for Life Insurance)

Engineering Business Daily

2021 FEB 11 (NewsRx) -- By a News Reporter-Staff News Editor at Engineering Business Daily -- Current study results on Machine Learning have been published. According to news reporting from Naples, Italy, by NewsRx journalists, research stated, “The insurance regulatory regime introduced in the European Union by the ‘Solvency II’ Directive 2009/138, that has become applicable on 1 January 2016, is aimed to safeguard policyholders and beneficiaries by requiring insurance undertakings to hold own funds able to cover losses, in excess to the expected ones, at the 99.5% confidence level, over a 1-year period. In order to assess risks and evaluate the regulatory Solvency Capital Requirement, undertakings should compute the probability distribution of the Net Asset Value over a 1-year period, with a financially inspired market consistent approach.”

The news correspondents obtained a quote from the research from Parthenope University, “In life insurance, given the peculiarities of the contracts, the valuation of the Net Asset Value distribution requires a nested Monte Carlo simulation, which is extremely time-consuming. Machine learning techniques are considered a promising candidate to reduce the computational burden of nested simulations. This work investigates the potential of well-established methods, such as deep learning networks and support vector regressors, when applied to the valuation of the Solvency Capital Requirement of participating life insurance policies, by empirically assessing their effectiveness and by comparing their efficiency and accuracy, also w.r.t. the ‘traditional’ least squares Monte Carlo technique.”

According to the news reporters, the research concluded: “The work aims also to contribute to the global process of renewal of the European insurance industry, where Solvency II has made the board of directors fully responsible of the choice of evaluation techniques and algorithmic processes, under the periodic monitoring of national supervisory authorities.”

This research has been peer-reviewed.

For more information on this research see: Machine Learning Techniques In Nested Stochastic Simulations for Life Insurance. Applied Stochastic Models in Business and Industry, 2021. Applied Stochastic Models in Business and Industry can be contacted at: Wiley (John Wiley & Sons), 111 River St, Hoboken 07030-5774, NJ, USA. (Wiley-Blackwell - http://www.wiley.com/; Applied Stochastic Models in Business and Industry - http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1526-4025)

Our news journalists report that additional information may be obtained by contacting Francesca Perla, Parthenope University, Dept Management Studies & Quantitat Methods, Naples, Italy. Additional authors for this research include Gilberto Castellani, Luca Passalacqua, Ugo Fiore, Zelda Marino, Salvatore Scognamiglio and Paolo Zanetti.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1002/asmb.2607. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.