Contract · Fully remote · $55–75/h ROLE OVERVIEW Huzzle is seeking insurance professionals to test how well frontier AI systems handle real underwriting, claims, actuarial and regulatory work, and to document where they fall short. You will not be underwriting or adjusting claims. You will be finding the point at which an AI system stops being reliable at this work, and proving that point objectively. Insurance is an unusually strong domain for this: policy wordings, regulatory returns and published filings are accurate, public and checkable, which makes model errors easy to evidence and hard to argue with. Each submission has three parts: a realistic technical request, documented evidence of where the model failed it, and a scoring rubric that lets any reviewer grade a future attempt consistently. Full training and worked examples are provided. Prior AI or data-annotation experience is not required. KEY RESPONSIBILITIES Design realistic insurance tasks: policy wording and coverage comparison, exposure and aggregation analysis, claims reserving and triage,



rating and pricing analysis, reinsurance structure comparison, regulatory disclosure summarisation. Ensure each task requires live research against primary sources — published policy wordings, regulator and supervisory publications, public rate filings, solvency and financial condition disclosures, market and association statistics — plus multi-step calculation and an actual file deliverable. Typically an Excel workbook or technical memo, but also PDFs, decks and CSVs. Test each task against a frontier AI model and examine its output with the scrutiny you would apply before signing it off. Document exactly where and how it failed: coverage terms or exclusions asserted that do not appear in the cited wording, jurisdictions conflated, requirements attributed to the wrong regulatory regime, reserving or rating arithmetic that does not reconcile, confident citation of provisions that do not say what is claimed. Author

📌 Insurance Expert (India)
🏢 Huzzle.com
📍 India