IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v11y2023i1p20-d1032785.html
   My bibliography  Save this article

Adversarial Artificial Intelligence in Insurance: From an Example to Some Potential Remedies

Author

Listed:
  • Behnaz Amerirad

    (Desautels Faculty of Management, McGill University, Montréal, QC H3A 1G5, Canada)

  • Matteo Cattaneo

    (Reale Group, 10122 Torino, Italy)

  • Ron S. Kenett

    (Samuel Neaman Institute, Technion City, Haifa 32000, Israel
    Department of Economics, Social Studies, Applied Mathematics and Statistics, University of Torino, 10134 Torino, Italy)

  • Elisa Luciano

    (Reale Group, 10122 Torino, Italy
    Department of Economics, Social Studies, Applied Mathematics and Statistics, University of Torino, 10134 Torino, Italy
    Collegio Carlo Alberto, 10122 Torino, Italy)

Abstract

Artificial intelligence (AI) is a tool that financial intermediaries and insurance companies use or are willing to use in almost all their activities. AI can have a positive impact on almost all aspects of the insurance value chain: pricing, underwriting, marketing, claims management, and after-sales services. While it is very important and useful, AI is not free of risks, including those related to its robustness against so-called adversarial attacks, which are conducted by external entities to misguide and defraud the AI algorithms. The paper is designed to review adversarial AI and to discuss its implications for the insurance sector. We give a taxonomy of adversarial attacks and present an original, fully fledged example of claims falsification in health insurance, as well as some remedies which are consistent with the current regulatory framework.

Suggested Citation

  • Behnaz Amerirad & Matteo Cattaneo & Ron S. Kenett & Elisa Luciano, 2023. "Adversarial Artificial Intelligence in Insurance: From an Example to Some Potential Remedies," Risks, MDPI, vol. 11(1), pages 1-17, January.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:1:p:20-:d:1032785
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/11/1/20/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/11/1/20/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jing Ai & Patrick L. Brockett & Linda L. Golden & Montserrat Guillén, 2013. "A Robust Unsupervised Method for Fraud Rate Estimation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(1), pages 121-143, March.
    Full references (including those not matched with items on IDEAS)

    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. Chen, Faan & Li, Yaxin & Feng, Qianqian & Dong, Zehao & Qian, Yiming & Yan, Yi & Ho, Mun S. & Ma, Qianchen & Zhang, Dashan & Jin, Yuanzhe, 2023. "Road safety performance rating through PSI-PRIDIT: A planning tool for designing policies and identifying best practices for EAS countries," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    2. Ina Garnefeld & Andreas Eggert & Markus Husemann-Kopetzky & Eva Böhm, 2019. "Exploring the link between payment schemes and customer fraud: a mental accounting perspective," Journal of the Academy of Marketing Science, Springer, vol. 47(4), pages 595-616, July.
    3. Urbina, Jilber & Guillén, Montserrat, 2013. "An application of capital allocation principles to operational risk," MPRA Paper 75726, University Library of Munich, Germany, revised Dec 2013.
    4. Andersson, Jonas & Olden, Andreas & Rusina, Aija, 2020. "Fraud detection by a multinomial model: Separating honesty from unobserved fraud," Discussion Papers 2020/15, Norwegian School of Economics, Department of Business and Management Science.

    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:gam:jrisks:v:11:y:2023:i:1:p:20-:d:1032785. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.