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Reserving by Conditioning on Markers of Individual Claims: A Case Study Using Historical Simulation

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  • Els Godecharle
  • Katrien Antonio

Abstract

This article explores the use of claim specific characteristics, so-called claim markers, for loss reserving with individual claims. Starting from the approach of Rosenlund and using the technique of historical simulation we develop a stochastic Reserve by Detailed Conditioning method that is applicable to a microlevel data set with detailed information on individual claims. We construct the predictive distribution of the outstanding loss reserve by simulating future payments of a claim, given its claim markers. We demonstrate the performance of the method on a portfolio of general liability insurance policies for private individuals from a European insurance company. Hereby we explore how to incorporate different kinds of claim markers and evaluate the impact of the set of markers and their specification on the predictive distribution of the outstanding reserve.

Suggested Citation

  • Els Godecharle & Katrien Antonio, 2015. "Reserving by Conditioning on Markers of Individual Claims: A Case Study Using Historical Simulation," North American Actuarial Journal, Taylor & Francis Journals, vol. 19(4), pages 273-288, October.
    • Handle: RePEc:taf:uaajxx:v:19:y:2015:i:4:p:273-288
    DOI: 10.1080/10920277.2015.1046607
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    Cited by:

    1. Crevecoeur, Jonas & Robben, Jens & Antonio, Katrien, 2022. "A hierarchical reserving model for reported non-life insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 158-184.
    2. Crevecoeur, Jonas & Antonio, Katrien & Verbelen, Roel, 2019. "Modeling the number of hidden events subject to observation delay," European Journal of Operational Research, Elsevier, vol. 277(3), pages 930-944.
    3. Maciak, Matúš & Okhrin, Ostap & Pešta, Michal, 2021. "Infinitely stochastic micro reserving," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 30-58.
    4. Mat'uv{s} Maciak & Ostap Okhrin & Michal Pev{s}ta, 2019. "Infinitely Stochastic Micro Forecasting," Papers 1908.10636, arXiv.org, revised Sep 2019.

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