Claims, underwriting and policy documents at volume.
AI for Insurance
Insurance runs on documents and risk models. Both are areas where AI pays back quickly — provided the audit trail holds up.
What makes this sector different
Claims volume is spiky
Capacity sized for the mean collapses after a weather event.
Unstructured evidence
Photographs, PDFs, handwritten forms and call transcripts all carry claim-relevant facts.
Decisions must be defensible
A declined claim will be challenged, and the reasoning has to survive that.
Where it pays back
Claims triage and document extraction
HybridClassify and extract from claim evidence, routing edge cases to humans.
Underwriting submission review
Retrieval-augmented generationSummarise submissions and surface missing information before an underwriter opens the file.
Fraud signal detection
Classical MLScore claims against historical patterns with attributable features.
Policy question answering
Retrieval-augmented generationAnswer coverage questions from the wording itself, with the clause cited.
How the Insurance assessment differs
Insurance assessments focus on document variety and the audit trail a disputed decision will need.
Compliance we ask about first
Data sources we expect
Questions this template pushes on
- What proportion of claim evidence is scanned or handwritten?
- Which decisions can be automated, and which must stay with a human?
- How long must the reasoning behind a decision be retained?
These are suggestions surfaced alongside the questions — never pre-filled answers. The assessment still asks you everything, because a score built on assumptions you never confirmed is not a score you could act on.
Scope an AI project for insurance
The Insurance template sharpens the questions. Ten minutes gets you a feasibility score, an architecture and a costed plan.