"Insuring Non-Determinism”: How Munich RE is Managing AI's Probabilistic Risks

Data Science Conversations

Peter Bärnreuther from Munich RE discusses the emerging field of AI insurance, explaining how companies can manage the inherent risks of probabilistic AI systems through specialized insurance products. The conversation covers real-world AI failures, different types of AI risks, and how insurance can help both corporations and AI vendors scale their operations safely.

Key Topics Discussed

Peter's Career Journey: Peter Bärnreuther transitioned from studying physics and economics to risk management at Accenture, then Munich RE, where he developed crypto insurance products before joining the AI risk team to create coverage for AI-related risks.

Probabilistic vs Deterministic Systems: Unlike traditional deterministic systems where errors can be traced, AI systems are probabilistic - they can be 99.5% accurate but never 100% certain, creating fundamental new risks that require insurance coverage.

AI Risk Categories: Two main types exist - traditional machine learning risks (classification errors like fraud detection) and generative AI risks (IP infringement, hallucinations, legal compliance issues), each requiring different insurance approaches.

Real-World AI Incidents: Examples include airline chatbots promising unauthorized discounts, lawyers using fake legal cases, and AI house valuation systems losing $300M+ by failing to adjust to market changes during price drops.

Insurance Product Structure: Munich RE offers two main products - one for corporations using AI internally for risk mitigation, and another for AI vendors needing trust-building to scale their business and attract enterprise clients.

Specific Use Cases: Successful implementations include solar panel fault detection (100% accuracy guarantee), credit card fraud prevention (99.9% performance guarantee), and battery health assessment for electric vehicles with compensation guarantees.

Market Challenges: Key difficulties include pricing models with limited historical data, concept drift where AI performance degrades over time, accumulation risk when multiple clients use similar foundation models, and "silent coverage" issues in existing insurance policies.

Future Market Outlook: AI insurance may either become a separate line of business (like cyber insurance) or be integrated into traditional policies, with current focus on US and European markets and strongest traction in IT security applications.