Artificial Intelligence and Insurance: Liability and Risk in Focus
Artificial intelligence is changing how industries operate, bringing both opportunities and new risks. For the insurance sector, one pressing issue is liability—how responsibility is assigned when AI systems cause harm or fail to perform as expected. AI liability policies are becoming crucial as insurers, regulators, and businesses seek clarity on managing these emerging risks.
Recent research by the WTW Research Network, in collaboration with Anat Lior, Assistant Professor at Drexel University’s Thomas R. Kline School of Law, sheds light on current practices and frameworks addressing AI risk in insurance. With AI technologies spreading across sectors, insurers are vital in building trust and enabling innovation by providing coverage that helps manage AI-related uncertainties. This research highlights gaps in existing policies, emerging industry approaches, and how regulation and insurance initiatives might work together to better handle AI risks.
The Current AI Insurance Market
Several insurers have started offering AI-specific coverage to meet the demand for AI risk protection. Munich Re launched aiSure™ in 2018, pioneering performance guarantee coverage for AI technologies. Armilla AI provides warranties based on detailed risk assessments, including training data and usage context, backed by reinsurers like Swiss Re.
Newer players like Vouch focus on start-ups with policies covering AI errors, discrimination claims, intellectual property infringement, and regulatory defense. CoverYourAI offers business interruption coverage tailored to AI-induced operational delays, using predictive models instead of historical data for underwriting. Relm Insurance introduced three AI-specific products in early 2025, while start-ups such as AiShelter and Testudo develop innovative policies and risk tools for generative AI.
Deloitte projects that AI insurance premiums could grow to $4.7 billion globally by 2032, expanding at an 80% annual rate. This fast-growing market presents both opportunities and challenges for insurers adapting to AI risk and evolving regulations.
Underwriting Practices and Risk Assessment
Insurers face challenges underwriting AI risks due to limited historical data. Some rely on predictive models, while others adapt traditional approaches until better data becomes available. Armilla AI, for example, builds proprietary datasets to quantify risk, assessing AI product specifics and application contexts.
Generative AI introduces unique concerns such as misinformation, bias, and intellectual property issues. These risks are not new but are amplified by scale and complexity. Many businesses still depend on expanded wording within existing Cyber and Technology Errors & Omissions (E&O) policies, which can create “silent coverage” and unclear claim situations.
Large insurers are cautious with AI-specific policies, often preferring to adjust existing coverages. Meanwhile, smaller firms innovate faster, focusing on niche markets and analytical tools to better price AI risks.
Regulatory Environment and Its Impact
AI regulation affects insurance in several ways. Insurance laws set the rules insurers must follow, which can either support or limit coverage innovation. Insurers also influence policyholder behavior through underwriting and claims management, promoting safer AI practices as part of broader risk control.
Legislation such as the EU AI Act has a significant effect on insurer risk appetite and product development. Strict fines and enforcement encourage insurers to set tighter coverage terms, increase premiums, or exclude certain AI risks altogether, spurring the creation of new insurance products.
The EU AI Act classifies AI applications by risk level—high, medium, or low—helping insurers define what they will cover and under what conditions.
Challenges and Industry Approaches
Data scarcity remains a major hurdle, limiting insurers’ ability to price AI risks accurately. Smaller insurers and start-ups are often more agile in developing predictive tools and carving out niche AI insurance markets.
There is a growing call for cooperation between insurers, regulators, and AI developers to ensure coverage consistency and encourage responsible AI use. Industry voices advocate for balanced regulation that protects innovation without creating unnecessary barriers.
Experimentation and collaboration will be key to evolving AI insurance products that meet market needs while managing novel risks.
Conclusion
AI insurance is a fast-growing field with significant potential. Insurers need to deepen their knowledge of AI technologies to improve risk evaluation and product design. While adapting existing policies remains practical, exploring standalone AI insurance solutions is increasingly important.
Active engagement with regulators and industry players will help develop an insurance framework that supports innovation, builds trust, and manages uncertainty effectively. Keeping pace with AI advancements will position insurers to provide valuable safety nets in an uncertain environment.
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