LearnCyrenza

4.3

Walkthrough — Claims Analyst

45 min

Meet Priya, Commercial Property Claims Analyst

  • Reviews 40-60 new claims per month
  • Tasks: initial claim review, coverage determination, adjuster coordination, documentation
  • Pain points: repetitive data entry, inconsistent adjuster notes, finding policy language quickly

The Before State

  • Claims documentation scattered across systems (claims platform, email, shared drives)
  • Spends 30+ minutes per claim re-reading policies to determine coverage
  • Adjuster notes are unstructured, hard to extract key information
  • Frequently misses coverage nuances because policy language is dense

Knowledge Base Setup

  • Folder structure: Claims/[Year]/[ClaimID]/[InitialDocs|AdjusterNotes|CoverageMemos|Resolution]
  • File naming: ClaimID_DocType_Date
  • Context documents:
    • Policy coverage summary (by policy type: what's covered, exclusions, limits)
    • Common claim scenarios and coverage decisions
    • Adjuster communication templates
    • Red flag checklist (fraud indicators, coverage issues)

Tool Integration Choices

  • Claims management system (limited AI, mostly manual)
  • Standalone AI for policy review and coverage analysis
  • Email AI for adjuster communication
  • Manual review of photos and damage reports (AI not reliable enough yet)

Three Core Workflows

Workflow 1: Initial Claim Review and Coverage Determination

  1. New claim arrives in system
  2. Pull policy documents and claim submission
  3. Use AI to summarize claim: "Extract: date of loss, type of damage, estimated amount, claimant statement"
  4. Cross-reference with policy coverage summary document
  5. Use AI to analyze: "Based on this commercial property policy [upload], does this water damage claim appear covered? Focus on: cause of loss, policy exclusions, sub-limits"
  6. Review AI analysis, verify against actual policy language
  7. Draft initial coverage position memo with AI assistance
  8. Send to adjuster with investigation instructions

Workflow 2: Reviewing Adjuster Field Notes

  1. Adjuster uploads photos and narrative notes (often 5-10 pages, stream-of-consciousness)
  2. Use AI to extract structured information: "From these adjuster notes, create a table: damage location, severity, estimated repair cost, red flags"
  3. Review AI extraction against photos and notes
  4. Use AI to draft coverage implications: "Based on these findings, does the loss appear to be: sudden/accidental? Pre-existing? Properly maintained?"
  5. Update claim file with structured summary
  6. Flag any coverage concerns for supervisor review

Workflow 3: Communicating with Policyholder

  1. Coverage decision made (approved, denied, partially covered)
  2. Review policy language and claim facts
  3. Use AI to draft policyholder letter: "Draft a coverage explanation letter. Claim: [ID]. Decision: [approved/denied]. Reasoning: [coverage basis]. Tone: professional, empathetic, clear."
  4. Edit for regulatory compliance and company standards
  5. Add specific policy citations
  6. Review by supervisor before sending

Quality Control Checklist

  • Does the AI's coverage analysis cite the correct policy sections?
  • Have I verified the AI didn't miss any policy exclusions or endorsements?
  • Are the damage amounts from AI summaries accurate compared to photos/estimates?
  • Is this decision consistent with how we've handled similar claims?
  • Does the policyholder communication meet state insurance regulations?
  • Would I defend this decision in a coverage dispute?

The After State

  • Saves ~6 hours per week on policy review and documentation
  • Processes 20% more claims per month with same quality standards
  • Fewer coverage errors (AI catches exclusions she might have missed)
  • Better adjuster coordination (structured notes make communication clearer)
  • Still manually reviews complex or high-value claims ($100k+)

Common Mistakes for Claims Analysts

  • Trusting AI policy interpretation without verifying actual policy language
  • Using AI to make final coverage decisions (analyst judgment required)
  • Not maintaining policy coverage summaries (AI needs reference material)
  • Letting AI write legally binding coverage decisions (regulatory risk)
  • Assuming AI understands state-specific insurance regulations