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Tuning a Deep Learning Network for Solvency II: Preliminary Results

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ugo Fiore & Zelda Marino & Luca Passalacqua & Francesca Perla & Salvatore Scognamiglio & Paolo Zanetti, 2018. "Tuning a Deep Learning Network for Solvency II: Preliminary Results," Springer Books, in: Marco Corazza & María Durbán & Aurea Grané & Cira Perna & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 351-355, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-89824-7_63
    DOI: 10.1007/978-3-319-89824-7_63
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    Cited by:

    1. Perla, Francesca & Scognamiglio, Salvatore & Spadaro, Andrea & Zanetti, Paolo, 2025. "Transformers-based least square Monte Carlo for solvency calculation in life insurance," Insurance: Mathematics and Economics, Elsevier, vol. 125(C).

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