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SynthETIC: An individual insurance claim simulator with feature control

Author

Listed:
  • Avanzi, Benjamin
  • Taylor, Greg
  • Wang, Melantha
  • Wong, Bernard

Abstract

Recent years have seen rapid increase in the application of machine learning to insurance loss reserving. They yield most value when applied to large data sets, such as individual claims, or large claim triangles. In short, they are likely to be useful in the analysis of any data set whose volume is sufficient to obscure a naked-eye view of its features. Unfortunately, such large data sets are in short supply in the actuarial literature. Accordingly, one needs to turn to synthetic data. Although the ultimate objective of these methods is application to real data, the use of synthetic data containing features commonly observed in real data is also to be encouraged.

Suggested Citation

  • Avanzi, Benjamin & Taylor, Greg & Wang, Melantha & Wong, Bernard, 2021. "SynthETIC: An individual insurance claim simulator with feature control," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 296-308.
    • Handle: RePEc:eee:insuma:v:100:y:2021:i:c:p:296-308
    DOI: 10.1016/j.insmatheco.2021.06.004
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    References listed on IDEAS

    as
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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 Jun 2024.
    2. Muhammed Taher Al-Mudafer & Benjamin Avanzi & Greg Taylor & Bernard Wong, 2021. "Stochastic loss reserving with mixture density neural networks," Papers 2108.07924, arXiv.org.
    3. Jessup, Sébastien & Mailhot, Mélina & Pigeon, Mathieu, 2025. "Uncertainty in heteroscedastic Bayesian model averaging," Insurance: Mathematics and Economics, Elsevier, vol. 121(C), pages 63-78.
    4. Gao, Shiying & Zhang, Yuning & Li, Ruikun & Choy, S.T. Boris & Gao, Junbin, 2026. "Probabilistic loss reserving prediction via denoising diffusion model," Insurance: Mathematics and Economics, Elsevier, vol. 127(C).
    5. Benjamin Avanzi & Matthew Lambrianidis & Greg Taylor & Bernard Wong, 2025. "On the use of case estimate and transactional payment data in neural networks for individual loss reserving," Papers 2601.05274, arXiv.org.
    6. Benjamin Avanzi & Gregory Clive Taylor & Melantha Wang, 2021. "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator," Papers 2109.04058, arXiv.org, revised Mar 2022.
    7. Benjamin Avanzi & Ronald Richman & Bernard Wong & Mario Wuthrich & Yagebu Xie, 2026. "Reinforcement Learning for Micro-Level Claims Reserving," Papers 2601.07637, arXiv.org.

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    Keywords

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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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