Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

Catalina Lozano-Murcia, Francisco P. Romero (), Jesus Serrano-Guerrero, Arturo Peralta and Jose A. Olivas
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Catalina Lozano-Murcia: Department Information Systems and Technologies, University of Castilla La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
Francisco P. Romero: Department Information Systems and Technologies, University of Castilla La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
Jesus Serrano-Guerrero: Department Information Systems and Technologies, University of Castilla La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
Arturo Peralta: Escuela Superior de Ingeniería y Tecnología, Universidad Internacional de La Rioja, Avda. de la Paz 93-103, 26006 Logroño, Spain
Jose A. Olivas: Department Information Systems and Technologies, University of Castilla La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain

Mathematics, 2024, vol. 12, issue 5, 1-13

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.

Keywords: machine learning; artificial intelligence; deep learning; explainable machine learning; accuracy; interpretability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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