IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2003.10674.html

Towards Explainability of Machine Learning Models in Insurance Pricing

Author

Listed:
  • Kevin Kuo
  • Daniel Lupton

Abstract

Machine learning methods have garnered increasing interest among actuaries in recent years. However, their adoption by practitioners has been limited, partly due to the lack of transparency of these methods, as compared to generalized linear models. In this paper, we discuss the need for model interpretability in property & casualty insurance ratemaking, propose a framework for explaining models, and present a case study to illustrate the framework.

Suggested Citation

  • Kevin Kuo & Daniel Lupton, 2020. "Towards Explainability of Machine Learning Models in Insurance Pricing," Papers 2003.10674, arXiv.org.
    • Handle: RePEc:arx:papers:2003.10674
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2003.10674
    File Function: Latest version
    Download Restriction: no
    ---><---

    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. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unravelling the predictive power of telematics data in car insurance pricing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1275-1304, November.
    3. Kevin Kuo, 2018. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Papers 1804.09253, arXiv.org, revised Sep 2019.
    4. Yi Yang & Wei Qian & Hui Zou, 2018. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(3), pages 456-470, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shengkun Xie & Kun Shi, 2023. "Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective," Mathematics, MDPI, vol. 11(2), pages 1-24, January.
    2. McDonnell, Kevin & Sheehan, Barry & Murphy, Finbarr, 2026. "Bridging transparency in insurance claims prediction: A comparative study of explainable AI and traditional linear models using vehicle telematics data," Technological Forecasting and Social Change, Elsevier, vol. 223(C).
    3. Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.
    4. 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.
    5. Shengkun Xie & Rebecca Luo, 2022. "Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions," Mathematics, MDPI, vol. 10(10), pages 1-19, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Simon CK Lee, 2020. "Delta Boosting Implementation of Negative Binomial Regression in Actuarial Pricing," Risks, MDPI, vol. 8(1), pages 1-21, February.
    2. 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.
    3. 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.
    4. Greg Taylor, 2019. "Risks Special Issue on “Granular Models and Machine Learning Models”," Risks, MDPI, vol. 8(1), pages 1-2, December.
    5. Benjamin Avanzi & Yanfeng Li & Bernard Wong & Alan Xian, 2022. "Ensemble distributional forecasting for insurance loss reserving," Papers 2206.08541, arXiv.org, revised Jun 2024.
    6. Catalina Lozano-Murcia & Francisco P. Romero & Jesus Serrano-Guerrero & Arturo Peralta & Jose A. Olivas, 2024. "Potential Applications of Explainable Artificial Intelligence to Actuarial Problems," Mathematics, MDPI, vol. 12(5), pages 1-13, February.
    7. Xu, Shuzhe & Zhang, Chuanlong & Hong, Don, 2022. "BERT-based NLP techniques for classification and severity modeling in basic warranty data study," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 57-67.
    8. Zhiyu Quan & Changyue Hu & Panyi Dong & Emiliano A. Valdez, 2024. "Improving Business Insurance Loss Models by Leveraging InsurTech Innovation," Papers 2401.16723, arXiv.org.
    9. 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.
    10. Stephan M. Bischofberger, 2020. "In-Sample Hazard Forecasting Based on Survival Models with Operational Time," Risks, MDPI, vol. 8(1), pages 1-17, January.
    11. Yang Qiao & Chou-Wen Wang & Wenjun Zhu, 2024. "Machine learning in long-term mortality forecasting," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 340-362, April.
    12. Muhammed Taher Al-Mudafer & Benjamin Avanzi & Greg Taylor & Bernard Wong, 2021. "Stochastic loss reserving with mixture density neural networks," Papers 2108.07924, arXiv.org.
    13. Łukasz Delong & Mario V. Wüthrich, 2020. "Neural Networks for the Joint Development of Individual Payments and Claim Incurred," Risks, MDPI, vol. 8(2), pages 1-34, April.
    14. Benjamin Avanzi & Ronald Richman & Bernard Wong & Mario Wuthrich & Yagebu Xie, 2026. "Reinforcement Learning for Micro-Level Claims Reserving," Papers 2601.07637, arXiv.org.
    15. Valandis Elpidorou & Carolin Margraf & María Dolores Martínez-Miranda & Bent Nielsen, 2019. "A Likelihood Approach to Bornhuetter–Ferguson Analysis," Risks, MDPI, vol. 7(4), pages 1-20, December.
    16. Ronald Richman & Mario V. Wuthrich, 2026. "From Chain-Ladder to Individual Claims Reserving," Papers 2602.15385, arXiv.org, revised Feb 2026.
    17. Shengkun Xie, 2021. "Improving Explainability of Major Risk Factors in Artificial Neural Networks for Auto Insurance Rate Regulation," Risks, MDPI, vol. 9(7), pages 1-21, July.
    18. Zan Yu & Lianzeng Zhang, 2024. "Computing the Gerber-Shiu function with interest and a constant dividend barrier by physics-informed neural networks," Papers 2401.04378, arXiv.org.
    19. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2021. "Pricing service maintenance contracts using predictive analytics," European Journal of Operational Research, Elsevier, vol. 290(2), pages 530-545.
    20. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2003.10674. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.