Why AI matters in insurance
Insurance runs on unstructured documents: submissions, loss runs, policy wordings, broker emails, claims files, and contracts. LLMs are the first technology that can process that text at scale with useful accuracy. That is why insurance is consistently ranked among the industries with the most to gain (McKinsey, BCG; see the references behind the integration phases).
Where the value is for insurers
Highest-confidence use cases, roughly in order of value á risk. Examples lean commercial P&C, but each maps directly to other lines: swap âbroker submissionâ for âapplicationâ and âloss runâ for âclaims history.â
- Submission intake and triage: extract named insured, operations, exposures, and requested coverages from broker submissions; flag out-of-appetite risks early. Directly attacks the âquote what you see firstâ bottleneck.
- Document summarization for underwriting: condense loss runs, engineering reports, financials, and prior policies into underwriter-ready briefs.
- Policy wording and contract comparison: compare endorsements, spot deviations from standard forms, first-pass review of manuscript wordings with expert review.
- Claims file summarization: synthesize adjuster notes, medical and legal correspondence, and coverage positions for faster reserving and severity triage.
- Knowledge access (RAG): a searchable assistant over underwriting guidelines and appetite guides, such as âWhatâs our appetite for habitational in coastal Florida?â
- Actuarial and analytics acceleration: code assistance, data-quality checks, documentation drafting, and filing preparation. This is where actuaries typically start; see the technical deep dive.
- Internal drafting: broker communications, meeting summaries, and first-draft reports.
The realistic ROI picture: industry experience shows strong returns on assistive use cases: drafting, summarizing, and extracting typically save 20â50% of time on document-heavy tasks (measured carrier and vendor results). Results from fully automated use cases are mixed. The winning pattern is âAI drafts, human decides.â
What the measured returns actually cover
Time saved on document-heavy work. The gap between the two rows is the whole strategy: the evidence supports assistance, and does not yet support handing the task over.
Assistive ¡ AI drafts, a person decides
Fully automated ¡ no person in the loop
Drawn from measured carrier and vendor results. The second row is empty rather than estimated: reported outcomes for full automation vary too widely to state as a range, and that is itself the finding.
Use cases that influence underwriting, pricing, claims, or customers require formal controls; see Governance for the regulatory and risk-management requirements.