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Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

Author

Listed:
  • Catalina Lozano-Murcia

    (Department Information Systems and Technologies, University of Castilla La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
    Department of Mathematics, Escuela Colombiana de Ingeniería Julio Garavito, Bogotá 111166, Colombia
    Current address: School of Computer Sciene, University of Castilla la Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain.
    These authors contributed equally to this work.)

  • Francisco P. Romero

    (Department Information Systems and Technologies, University of Castilla La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
    Current address: School of Computer Sciene, University of Castilla la Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain.
    These authors contributed equally to this work.)

  • Jesus Serrano-Guerrero

    (Department Information Systems and Technologies, University of Castilla La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
    Current address: School of Computer Sciene, University of Castilla la Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain.
    These authors contributed equally to this work.)

  • Arturo Peralta

    (Escuela Superior de Ingeniería y Tecnología, Universidad Internacional de La Rioja, Avda. de la Paz 93-103, 26006 Logroño, Spain
    Ciencia y Tecnología, Escuela Superior de Ingeniería, Universidad Internacional de Valencia, Calle Pintor Sorolla, 21, 46002 Valencia, Spain
    These authors contributed equally to this work.)

  • Jose A. Olivas

    (Department Information Systems and Technologies, University of Castilla La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
    Current address: School of Computer Sciene, University of Castilla la Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain.
    These authors contributed equally to this work.)

Abstract

Explainable artificial intelligence (XAI) is a group of techniques and evaluations that allows users to understand artificial intelligence knowledge and increase the reliability of the results produced using artificial intelligence. XAI can assist actuaries in achieving better estimations and decisions. This study reviews the current literature to summarize XAI in common actuarial problems. We proposed a research process based on understanding the type of AI used in actuarial practice in the financial industry and insurance pricing and then researched XAI implementation. This study systematically reviews the literature on the need for implementation options and the current use of explanatory artificial intelligence (XAI) techniques for actuarial problems. The study begins with a contextual introduction outlining the use of artificial intelligence techniques and their potential limitations, followed by the definition of the search equations used in the research process, the analysis of the results, and the identification of the main potential fields for exploitation in actuarial problems, as well as pointers for potential future work in this area.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:635-:d:1343128
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    References listed on IDEAS

    as
    1. Kevin Kuo, 2018. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Papers 1804.09253, arXiv.org, revised Sep 2019.
    2. Kristian Buchardt & Christian Furrer & Oliver Lunding Sandqvist, 2022. "Transaction time models in multi-state life insurance," Papers 2209.06902, arXiv.org, revised Feb 2023.
    3. Kristian Buchardt & Christian Furrer & Oliver Lunding Sandqvist, 2023. "Transaction time models in multi-state life insurance," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2023(10), pages 974-999, November.
    4. Sebastian Baran & Przemys{l}aw Rola, 2022. "Prediction of motor insurance claims occurrence as an imbalanced machine learning problem," Papers 2204.06109, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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