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Risks Special Issue on “Granular Models and Machine Learning Models”

Author

Listed:
  • Greg Taylor

    (School of Risk and Actuarial Studies, University of New South Wales, Kensington, NSW 2052, Australia)

Abstract

It is probably fair to date loss reserving by means of claim modelling from the late 1960s [...]

Suggested Citation

  • Greg Taylor, 2019. "Risks Special Issue on “Granular Models and Machine Learning Models”," Risks, MDPI, vol. 8(1), pages 1-2, December.
    • Handle: RePEc:gam:jrisks:v:8:y:2019:i:1:p:1-:d:303264
    as

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    References listed on IDEAS

    as
    1. Kevin Kuo, 2019. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Risks, MDPI, vol. 7(3), pages 1-12, September.
    2. Jacky H. L. Poon, 2019. "Penalising Unexplainability in Neural Networks for Predicting Payments per Claim Incurred," Risks, MDPI, vol. 7(3), pages 1-11, September.
    3. Kevin Kuo, 2018. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Papers 1804.09253, arXiv.org, revised Sep 2019.
    4. Greg Taylor, 2019. "Loss Reserving Models: Granular and Machine Learning Forms," Risks, MDPI, vol. 7(3), pages 1-18, July.
    5. Francis Duval & Mathieu Pigeon, 2019. "Individual Loss Reserving Using a Gradient Boosting-Based Approach," Risks, MDPI, vol. 7(3), pages 1-18, July.
    6. Massimo De Felice & Franco Moriconi, 2019. "Claim Watching and Individual Claims Reserving Using Classification and Regression Trees," Risks, MDPI, vol. 7(4), pages 1-36, October.
    Full references (including those not matched with items on IDEAS)

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