AI Model
Underperformance Insurance
When contractual AI performance guarantees aren't met, the financial exposure can be devastating. We provide precision coverage for model drift, accuracy shortfalls, and technical performance gaps.
The AI / Modern Angle
As enterprises deploy ML models at scale, the gap between promised performance and real-world output creates a new category of financial risk. Models degrade over time due to data drift, concept shift, and distribution changes — often silently. A model that delivered 95% accuracy in testing may drop to 72% in production within months, triggering SLA breaches, lost revenue, and contractual penalties. Traditional insurance doesn't account for this invisible degradation curve. Our policies are specifically designed around model lifecycle risk, with 99% document processing efficiency in claims assessment.
Core Coverage Points
SLA Breach Protection
Compensation for unmet precision, recall, and accuracy benchmarks specified in your deployment contracts.
Model Drift Coverage
Coverage for revenue loss and remediation costs when models degrade due to data drift or concept shift in production.
Retraining & Remediation
Funding for emergency model retraining, dataset auditing, and pipeline reconstruction when performance thresholds are breached.
Client Liability Shield
Defense costs and settlements when downstream clients suffer losses due to your model's underperformance.
Downtime Revenue Loss
Business interruption coverage for revenue lost during model failure events, including rollback and fallback periods.
Expert Guidance,
Not Off-the-Shelf
Every AI deployment is unique — shaped by its training data, architecture, domain, and operational context. That's why we reject templated policies in favor of bespoke solutions crafted by specialists who understand the technical nuances of machine learning systems.
Our Human-in-the-Loop underwriting process pairs seasoned insurance professionals with AI risk engineers to evaluate your specific model portfolio, benchmark expectations, and operational dependencies. The result is coverage that adapts to your unique risk profile — not the other way around.
"Ocean Falls didn't just write us a policy — they embedded with our ML ops team to understand our deployment pipeline and built coverage around our actual risk surface."