Tuning a Deep Learning Network for Solvency II: Preliminary Results

Ugo Fiore, Zelda Marino, Luca Passalacqua, Francesca Perla, Salvatore Scognamiglio () and Paolo Zanetti ()
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Ugo Fiore: Parthenope University, Department of Management and Quantitative Studies
Zelda Marino: Parthenope University, Department of Management and Quantitative Studies
Luca Passalacqua: Sapienza University, Department of Statistical Sciences
Francesca Perla: Parthenope University, Department of Management and Quantitative Studies
Salvatore Scognamiglio: Parthenope University, Department of Management and Quantitative Studies
Paolo Zanetti: Parthenope University, Department of Management and Quantitative Studies

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 351-355 from Springer

Abstract: Abstract Under the Solvency II Directive, insurance and reinsurance undertakings are required to perform continuous monitoring of risks and market consistent valuation of assets and liabilities. Solvency II application is particularly demanding, both theoretically and under the computational point of view. At present, any technique able to improve on accuracy or to reduce computing time is highly desirable. This works reports initial results on the design of a Deep Learning Network, aimed to reduce computing time by avoiding the standard full nested Monte Carlo approach.

Keywords: Solvency II; Deep learning; Monte Carlo; Profit insurance policies (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-89824-7_63

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