  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.
