Adversarial AI in Insurance: Pervasiveness and Resilience
It addresses security challenges for insurance companies using AI, but is incremental as it reviews existing topics without new empirical findings.
The paper tackles the problem of adversarial attacks on AI systems in the insurance sector, providing examples, categorizations, and defense methods, but does not report specific numerical results.
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.