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Explainable Artificial Intelligence (XAI) in Insurance

Author

Listed:
  • Emer Owens

    (Department of Accounting and Finance, University of Limerick, V94 PH93 Limerick, Ireland)

  • Barry Sheehan

    (Department of Accounting and Finance, University of Limerick, V94 PH93 Limerick, Ireland)

  • Martin Mullins

    (Department of Accounting and Finance, University of Limerick, V94 PH93 Limerick, Ireland)

  • Martin Cunneen

    (Department of Accounting and Finance, University of Limerick, V94 PH93 Limerick, Ireland)

  • Juliane Ressel

    (Department of Accounting and Finance, University of Limerick, V94 PH93 Limerick, Ireland
    Research Center for the Insurance Market, Institute for Insurance Studies, TH Köln, 50968 Cologne, Germany)

  • German Castignani

    (Motion-S S.A., Avenue des Bains 4, Mondorf-les-Bains, L-5610 Luxembourg, Luxembourg
    Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, Esch-sur-Alzette, L-4365 Luxembourg, Luxembourg)

Abstract

Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry’s vast stores of sensitive data on policyholders and centrality in societal progress and innovation. This paper analyses current Artificial Intelligence (AI) applications in insurance industry practices and insurance research to assess their degree of explainability. Using search terms representative of (X)AI applications in insurance, 419 original research articles were screened from IEEE Xplore, ACM Digital Library, Scopus, Web of Science and Business Source Complete and EconLit. The resulting 103 articles (between the years 2000–2021) representing the current state-of-the-art of XAI in insurance literature are analysed and classified, highlighting the prevalence of XAI methods at the various stages of the insurance value chain. The study finds that XAI methods are particularly prevalent in claims management, underwriting and actuarial pricing practices. Simplification methods, called knowledge distillation and rule extraction, are identified as the primary XAI technique used within the insurance value chain. This is important as the combination of large models to create a smaller, more manageable model with distinct association rules aids in building XAI models which are regularly understandable. XAI is an important evolution of AI to ensure trust, transparency and moral values are embedded within the system’s ecosystem. The assessment of these XAI foci in the context of the insurance industry proves a worthwhile exploration into the unique advantages of XAI, highlighting to industry professionals, regulators and XAI developers where particular focus should be directed in the further development of XAI. This is the first study to analyse XAI’s current applications within the insurance industry, while simultaneously contributing to the interdisciplinary understanding of applied XAI. Advancing the literature on adequate XAI definitions, the authors propose an adapted definition of XAI informed by the systematic review of XAI literature in insurance.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:12:p:230-:d:990714
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    References listed on IDEAS

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    Cited by:

    1. Bermúdez, Lluís & Anaya, David & Belles-Sampera, Jaume, 2023. "Explainable AI for paid-up risk management in life insurance products," Finance Research Letters, Elsevier, vol. 57(C).

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