Underwriting research pack
Assemble cited exposure, loss, and external risk evidence for an underwriter.
What this workflow does
Assemble cited exposure, loss, and external risk evidence for an underwriter. It separates observed facts from inference, surfaces conflicting evidence, and delivers a review-ready finding set instead of an opaque answer.
What you gain
Underwriter-reviewed research pack
What the AI agent changes
How this worked before
Traditional automation could collect records, but a person still had to compare sources, resolve conflicts, and write the conclusion.
What the AI agent changes
The agent can plan a search, inspect multiple sources, distinguish facts from inference, and return a cited report.
Agent trigger
A complete submission reaches underwriting review.
Inputs
- Process context: submission, loss runs, exposure schedule, policy, and approved research
- Approved policies, ownership, and exception rules
Agent flow
- 1
Collect source material from the approved systems and record its timestamp.
- 2
Cross-check conflicting signals and separate facts from inference.
- 3
Prepare a cited risk brief with unanswered questions with citations, unknowns, and confidence.
- 4
Deliver the approved report and preserve its evidence set.
Human decisions
After step 3
The accountable process owner approves a cited risk brief with unanswered questions.
Outcome
- Underwriter-reviewed research pack
- Evidence, exceptions, and audit trail
Guardrails
- Label inference separately from observed facts.
- Do not close the investigation while required sources are unavailable.
Risks and mitigations
Missing or stale evidence can produce a confident but incomplete finding.
Show source coverage, conflicts, timestamps, and unanswered questions.
Sources and evidence
Sources establish feasibility or impact. Not every metric comes from an identical implementation.