Adversarial AI in Insurance: Pervasiveness and Resilience

TL;DR

This paper examines the prevalence of adversarial attacks on AI systems in insurance, categorizes attack types, discusses defense strategies, and emphasizes the importance of validation and verification for resilient AI applications.

Contribution

It provides a comprehensive analysis of adversarial attacks in insurance AI, including attack examples, categorization, and defense methods, highlighting the need for robust validation and verification processes.

Findings

Adversarial attacks can deceive insurance AI systems.

Defense methods include precautionary and validation strategies.

AI system resilience is crucial for trustworthy insurance applications.

Abstract

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.