Insights
Short, cite-ready essays on what actually ships in regulated insurance AI — not hype decks. Each note links to full case studies with metrics.
Last updated: July 10, 2026
Brokers submit messy PDFs; underwriters need field-level trust. Production IDP pipelines must return coordinates to source pages, human-in-the-loop for edge cases, and accuracy measured against gold labels — not demo F1 scores on clean samples. At Insly we held launch until 99.4% field match on 800 sampled fields across 4,200 submissions.
Read the underwriting case study →Fast ML-augmented rating only works when actuaries own bounds: shadow mode, circuit breakers, and explainable mappings from model outputs to approved grids. Speed without governance increases referral noise; with governance, auto-binding can jump from 22% to 68% while loss ratios stay bounded.
Read the pricing case study →AI assistants need live facts, not stale training data. A hosted MCP server with annotated tools lets Claude or Cursor fetch Estonian electricity prices, company filings, or parliament votes in one step — no API keys, no copy-paste. Open source the server; operate the endpoint for reliability.
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