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Stochastic loss reserving with mixture density neural networks

Author

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  • Al-Mudafer, Muhammed Taher
  • Avanzi, Benjamin
  • Taylor, Greg
  • Wong, Bernard

Abstract

In recent years, new techniques based on artificial intelligence and machine learning in particular have been making a revolution in the work of actuaries, including in loss reserving. A particularly promising technique is that of neural networks, which have been shown to offer a versatile, flexible and accurate approach to loss reserving. However, applications of neural networks in loss reserving to date have been primarily focused on the (important) problem of fitting accurate central estimates of the outstanding claims. In practice, properties regarding the variability of outstanding claims are equally important (e.g., quantiles for regulatory purposes).

Suggested Citation

  • Al-Mudafer, Muhammed Taher & Avanzi, Benjamin & Taylor, Greg & Wong, Bernard, 2022. "Stochastic loss reserving with mixture density neural networks," Insurance: Mathematics and Economics, Elsevier, vol. 105(C), pages 144-174.
    • Handle: RePEc:eee:insuma:v:105:y:2022:i:c:p:144-174
    DOI: 10.1016/j.insmatheco.2022.03.010
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    Citations

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    Cited by:

    1. Benjamin Avanzi & Yanfeng Li & Bernard Wong & Alan Xian, 2022. "Ensemble distributional forecasting for insurance loss reserving," Papers 2206.08541, arXiv.org, revised Feb 2024.
    2. Benjamin Avanzi & Greg Taylor & Melantha Wang & Bernard Wong, 2023. "Machine Learning with High-Cardinality Categorical Features in Actuarial Applications," Papers 2301.12710, arXiv.org.

    More about this item

    Keywords

    Loss reserving; Neural network; Mixture density network; Distributional forecasting; Machine learning;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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