2023/01/17 by Elisa Luciano, Luciano, Elisa, Matteo Cattaneo +3 · 1 citation
Computer Science · Social Sciences · #Adversarial Robustness in Machine Learning #Ethics and Social Impacts of AI #FOS: Computer and information sciences #FOS: Economics and business #General Finance (q-fin.GN) #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2301.07520
openalex publication_date 2023/01/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.