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Loss Reserving Models: Granular and Machine Learning Forms

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  • Greg Taylor

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

Abstract

The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these models from their predecessors, and the early sections recount relevant archaeological vignettes from the history of loss reserving. However, the larger part of the paper is concerned with the granular models and machine learning models. Their relative merits are discussed, as are the factors governing the choice between them and the older, more primitive models. Concluding sections briefly consider the possible further development of these models in the future.

Suggested Citation

  • Greg Taylor, 2019. "Loss Reserving Models: Granular and Machine Learning Forms," Risks, MDPI, vol. 7(3), pages 1-18, July.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:3:p:82-:d:250013
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    References listed on IDEAS

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    1. Venter, Gary & Şahın, Şule, 2018. "Parsimonious Parameterization Of Age-Period-Cohort Models By Bayesian Shrinkage - Erratum," ASTIN Bulletin, Cambridge University Press, vol. 48(1), pages 479-479, January.
    2. 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.
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    4. 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.
    5. Huang, Jinlong & Wu, Xianyi & Zhou, Xian, 2016. "Asymptotic behaviors of stochastic reserving: Aggregate versus individual models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 657-666.
    6. 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.
    7. Venter, Gary & Şahın, Şule, 2018. "Parsimonious Parameterization Of Age-Period-Cohort Models By Bayesian Shrinkage," ASTIN Bulletin, Cambridge University Press, vol. 48(1), pages 89-110, January.
    8. Hesselager, Ole, 1994. "A Markov Model for Loss Reserving," ASTIN Bulletin, Cambridge University Press, vol. 24(2), pages 183-193, November.
    9. Norberg, Ragnar, 1999. "Prediction of Outstanding Liabilities II. Model Variations and Extensions," ASTIN Bulletin, Cambridge University Press, vol. 29(1), pages 5-25, May.
    10. Gao, Guangyuan & Meng, Shengwang, 2018. "Stochastic Claims Reserving Via A Bayesian Spline Model With Random Loss Ratio Effects," ASTIN Bulletin, Cambridge University Press, vol. 48(1), pages 55-88, January.
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    Citations

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    Cited by:

    1. Greg Taylor, 2019. "Risks Special Issue on “Granular Models and Machine Learning Models”," Risks, MDPI, vol. 8(1), pages 1-2, December.
    2. Daniel J. Geiger & Akim Adekpedjou, 2022. "Analysis of IBNR Liabilities with Interevent Times Depending on Claim Counts," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 815-829, June.
    3. Simon CK Lee, 2020. "Delta Boosting Implementation of Negative Binomial Regression in Actuarial Pricing," Risks, MDPI, vol. 8(1), pages 1-21, February.
    4. Lu Xiong & Vajira Manathunga & Jiyao Luo & Nicholas Dennison & Ruicheng Zhang & Zhenhai Xiang, 2023. "AutoReserve: A Web-Based Tool for Personal Auto Insurance Loss Reserving with Classical and Machine Learning Methods," Risks, MDPI, vol. 11(7), pages 1-17, July.
    5. Stephan M. Bischofberger, 2020. "In-Sample Hazard Forecasting Based on Survival Models with Operational Time," Risks, MDPI, vol. 8(1), pages 1-17, January.
    6. Nataliya Chukhrova & Arne Johannssen, 2021. "Stochastic Claims Reserving Methods with State Space Representations: A Review," Risks, MDPI, vol. 9(11), pages 1-55, November.
    7. 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.
    8. 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.

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