Adversarial AI in Insurance: Pervasiveness and Resilience
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Abstract (arXiv)
The rapid and dynamic pace of Artificial Intelligence (AI) and Machine Learning (ML) is revolutionizing the insurance sector. AI offers significant, very much welcome advantages to insurance companies, and is fundamental to their customer-centricity strategy. It also poses challenges, in the project and implementation phase. Among those, we study Adversarial Attacks, which consist of the creation of modified input data to deceive an AI system and produce false outputs. We provide examples of attacks on insurance AI applications, categorize them, and argue on defence methods and precautionary systems, considering that they can involve few-shot and zero-shot multilabelling. A related topic, with growing interest, is the validation and verification of systems incorporating AI and ML components. These topics are discussed in various sections of this paper.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 34 references that are in the map. First posted on arXiv 17 Jan 2023.
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9 of 34 references.
References · 9
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
- 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 234
- 2017Robust Physical-World Attacks on Deep Learning Modelscited by 70
- 2018Adversarial Attacks Against Medical Deep Learning Systemscited by 61
- 2021A Survey On Universal Adversarial Attackcited by 59
- 2020Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factorscited by 39
- 2018Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classificationcited by 34
- 2020Multi-label Zero-shot Classification by Learning to Transfer from External Knowledgecited by 6
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