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From Chain-Ladder to Individual Claims Reserving

Author

Listed:
  • Ronald Richman
  • Mario V. Wuthrich

Abstract

The chain-ladder (CL) method is the most widely used claims reserving technique in non-life insurance. This manuscript introduces a novel approach to computing the CL reserves based on a fundamental restructuring of the data utilization for the CL prediction procedure. Instead of rolling forward the cumulative claims with estimated CL factors, we estimate multi-period factors that project the latest observations directly to the ultimate claims. This alternative perspective on CL reserving creates a natural pathway for the application of machine learning techniques to individual claims reserving. As a proof of concept, we present a small-scale real data application employing neural networks for individual claims reserving.

Suggested Citation

  • Ronald Richman & Mario V. Wuthrich, 2026. "From Chain-Ladder to Individual Claims Reserving," Papers 2602.15385, arXiv.org, revised Feb 2026.
    • Handle: RePEc:arx:papers:2602.15385
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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. Rosenlund, Stig, 2012. "Bootstrapping Individual Claim Histories," ASTIN Bulletin, Cambridge University Press, vol. 42(1), pages 291-324, May.
    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. Kevin Kuo, 2018. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Papers 1804.09253, arXiv.org, revised Sep 2019.
    5. Łukasz Delong & Mathias Lindholm & Mario V. Wüthrich, 2022. "Collective reserving using individual claims data," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2022(1), pages 1-28, January.
    6. Lopez, Olivier & Milhaud, Xavier & Thérond, Pierre-E., 2019. "A Tree-Based Algorithm Adapted To Microlevel Reserving And Long Development Claims – Erratum," ASTIN Bulletin, Cambridge University Press, vol. 49(3), pages 919-919, September.
    7. Olivier Lopez & Xavier Milhaud, 2021. "Individual reserving and nonparametric estimation of claim amounts subject to large reporting delays," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2021(1), pages 34-53, January.
    8. 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.
    9. Judith C. Schneider & Brandon Schwab, 2025. "Advancing loss reserving: A hybrid neural network approach for individual claim development prediction," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 92(2), pages 389-423, June.
    10. Benjamin Avanzi & Ronald Richman & Bernard Wong & Mario Wuthrich & Yagebu Xie, 2026. "Reinforcement Learning for Micro-Level Claims Reserving," Papers 2601.07637, arXiv.org.
    11. Bladt, Martin & Pittarello, Gabriele, 2025. "Individual claims reserving using the Aalen–Johansen estimator," ASTIN Bulletin, Cambridge University Press, vol. 55(1), pages 29-49, January.
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    Cited by:

    1. Ronald Richman & Mario V. Wuthrich, 2026. "One-Shot Individual Claims Reserving," Papers 2603.11660, arXiv.org.

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