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

Behnaz Amerirad, Matteo Cattaneo, Ron Kenett and Elisa Luciano
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Behnaz Amerirad: Desautels Faculty of Management, McGill University, Montréal, QC H3A 1G5, Canada
Matteo Cattaneo: Reale Group, 10122 Torino, Italy

Risks, 2023, vol. 11, issue 1, 1-17

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.

Keywords: AI in insurance; adversarial AI; insurance fraud; machine learning in insurance (ML) (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
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