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Why “AI Is Too Risky to Insure” Is Becoming a Global Concern

AI is too risky to insure, according to major underwriters who say current AI systems create unpredictable, black-box risks that traditional insurance models cannot cover. Companies adopting generative AI tools are now facing real-world liabilities—false claims, misinformation, and AI-enabled fraud—that insurers fear could trigger thousands of simultaneous losses. As regulators review insurers’ requests to exclude AI-related liabilities, businesses are left wondering whether their AI tools are becoming financial ticking time bombs.

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Are Insurers Right That “AI Is Too Risky to Insure”?

Insurers argue that AI is too risky to insure because outputs are often opaque, uncontrollable, and capable of causing large-scale, widespread harm. Cases like a chatbot inventing a discount, Google AI Overview generating false allegations, and deepfake executives stealing millions highlight how unpredictable AI failures have become. For insurers, the real fear isn’t one lawsuit—it’s systemic, simultaneous claims that break actuarial models.

What Happens If AI-Related Liabilities Are Excluded From Policies?

If regulators approve exclusions, businesses may be left exposed to AI-generated errors, misinformation, or fraud with no financial safety net. Companies relying heavily on automation could face expensive lawsuits and reputation damage without insurer protection. This shift could also force organizations to rethink AI adoption, invest in stronger oversight, or create internal risk-management frameworks to fill the insurance gap.

How Can Businesses Prepare If “AI Is Too Risky to Insure”?

Experts recommend boosting AI governance, auditing model outputs, and creating clear rules around chatbot interactions. Companies should also monitor third-party AI tools, document decisions, and invest in fraud-prevention systems to reduce exposure. Until the industry agrees on standards for insuring AI, proactive risk management will be essential for businesses that can’t afford unexpected AI failures.