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An Individual Claims History Simulation Machine

Author

Listed:
  • Andrea Gabrielli

    (RiskLab, Department of Mathematics, ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland)

  • Mario V. Wüthrich

    (RiskLab, Department of Mathematics, ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland)

Abstract

The aim of this project is to develop a stochastic simulation machine that generates individual claims histories of non-life insurance claims. This simulation machine is based on neural networks to incorporate individual claims feature information. We provide a fully calibrated stochastic scenario generator that is based on real non-life insurance data. This stochastic simulation machine allows everyone to simulate their own synthetic insurance portfolio of individual claims histories and back-test thier preferred claims reserving method.

Suggested Citation

  • Andrea Gabrielli & Mario V. Wüthrich, 2018. "An Individual Claims History Simulation Machine," Risks, MDPI, vol. 6(2), pages 1-32, March.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:2:p:29-:d:138840
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    References listed on IDEAS

    as
    1. Taylor, Greg & McGuire, Gráinne & Sullivan, James, 2008. "Individual Claim Loss Reserving Conditioned by Case Estimates," Annals of Actuarial Science, Cambridge University Press, vol. 3(1-2), pages 215-256, September.
    2. Pigeon, Mathieu & Antonio, Katrien & Denuit, Michel, 2013. "Individual Loss Reserving With The Multivariate Skew Normal Framework," ASTIN Bulletin, Cambridge University Press, vol. 43(3), pages 399-428, September.
    3. Mack, Thomas, 1993. "Distribution-free Calculation of the Standard Error of Chain Ladder Reserve Estimates," ASTIN Bulletin, Cambridge University Press, vol. 23(2), pages 213-225, November.
    4. Anonymous, 2016. "The link between classical reserving and granular reserving through double chain ladder and its extensions †Abstract of the London Discussion," British Actuarial Journal, Cambridge University Press, vol. 21(1), pages 117-133, March.
    5. Hiabu, M. & Margraf, C. & Martínez-Miranda, M. D. & Nielsen, J. P., 2016. "The link between classical reserving and granular reserving through double chain ladder and its extensions," British Actuarial Journal, Cambridge University Press, vol. 21(1), pages 97-116, March.
    6. Mario V. Wüthrich, 2018. "Machine learning in individual claims reserving," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2018(6), pages 465-480, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Zan Yu & Lianzeng Zhang, 2024. "Computing the Gerber-Shiu function with interest and a constant dividend barrier by physics-informed neural networks," Papers 2401.04378, arXiv.org.
    2. Benjamin Avanzi & Gregory Clive Taylor & Melantha Wang & Bernard Wong, 2020. "SynthETIC: an individual insurance claim simulator with feature control," Papers 2008.05693, arXiv.org, revised Aug 2021.
    3. Łukasz Delong & Mario V. Wüthrich, 2020. "Neural Networks for the Joint Development of Individual Payments and Claim Incurred," Risks, MDPI, vol. 8(2), pages 1-34, April.
    4. Kevin Kuo, 2019. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Risks, MDPI, vol. 7(3), pages 1-12, September.
    5. Muhammed Taher Al-Mudafer & Benjamin Avanzi & Greg Taylor & Bernard Wong, 2021. "Stochastic loss reserving with mixture density neural networks," Papers 2108.07924, arXiv.org.
    6. Christopher Blier-Wong & Hélène Cossette & Luc Lamontagne & Etienne Marceau, 2020. "Machine Learning in P&C Insurance: A Review for Pricing and Reserving," Risks, MDPI, vol. 9(1), pages 1-26, December.
    7. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2022. "Mack-Net model: Blending Mack's model with Recurrent Neural Networks," Papers 2205.07334, arXiv.org.
    8. Avanzi, Benjamin & Taylor, Greg & Wang, Melantha & Wong, Bernard, 2021. "SynthETIC: An individual insurance claim simulator with feature control," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 296-308.
    9. Banghee So & Jean-Philippe Boucher & Emiliano A. Valdez, 2021. "Synthetic Dataset Generation of Driver Telematics," Risks, MDPI, vol. 9(4), pages 1-19, March.
    10. Kevin Kuo, 2019. "Generative Synthesis of Insurance Datasets," Papers 1912.02423, arXiv.org, revised Aug 2020.
    11. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2020. "Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network," Papers 2008.07564, arXiv.org.
    12. Ihsan Chaoubi & Camille Besse & H'el`ene Cossette & Marie-Pier C^ot'e, 2022. "Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network," Papers 2201.13267, arXiv.org.
    13. Gao, Guangyuan & Meng, Shengwang & Shi, Yanlin, 2021. "Dispersion modelling of outstanding claims with double Poisson regression models," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 572-586.
    14. Dawud Thongtha & Nathakhun Wiroonsri, 2023. "Normal Approximation for Fire Incident Simulation Using Permanental Cox Processes," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-20, March.
    15. Hong Li & Qifan Song & Jianxi Su, 2021. "Robust estimates of insurance misrepresentation through kernel quantile regression mixtures," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 625-663, September.
    16. Valandis Elpidorou & Carolin Margraf & María Dolores Martínez-Miranda & Bent Nielsen, 2019. "A Likelihood Approach to Bornhuetter–Ferguson Analysis," Risks, MDPI, vol. 7(4), pages 1-20, December.
    17. Benjamin Avanzi & Gregory Clive Taylor & Melantha Wang, 2021. "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator," Papers 2109.04058, arXiv.org, revised Mar 2022.

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