Field brief
Claims exception handling / evidence packet assembly
Start with claims exception handling / evidence packet assembly. The subflow is viable because the work is evidence-heavy, customer-impacting, and measurable, while AI can be bounded to file summarization, missing-evidence detection, guideline comparison, and draft preparation.
Why this is a first viable workflow
- Exception claims concentrate visible pain without requiring automation of the entire claims lifecycle.
- The workflow depends on assembling evidence from known sources: claim file, policy, communications, vendor reports, and handling guidelines.
- AI can prepare summaries, missing-evidence checks, and follow-up drafts while the adjuster owns coverage, settlement, denial, and escalation decisions.
- Success can be measured through exception cycle time, touch count, missing-evidence rate, communication quality, and evidence completeness.
Value leakage map
Fragmented claim file evidence
Manual exception triage
Repeated claimant, broker, vendor, or provider follow-up
Slow coverage or liability routing
Inconsistent handling guideline comparison
Weak communication traceability
Incomplete regulatory or audit evidence
Workflow anatomy
- Claim is flagged as an exception because information is missing, coverage is unclear, liability is disputed, severity is unusual, or escalation is required.
- Adjuster assembles claim evidence across policy data, claim notes, communications, vendor reports, and prior related activity.
- Exception is routed for coverage, authority, vendor, SIU, legal, or supervisor review when thresholds are met.
- Adjuster requests missing evidence, drafts customer or broker updates, and documents the rationale for next action.
- Decision, approval, communication, and supporting evidence are retained in the claim file.
Required context
Claim notice
Policy and coverage details
Adjuster notes
Photos, estimates, invoices, or medical records
Vendor reports
Claimant and broker communications
Coverage position history
Claims handling guidelines
Authority rules
Prior related claims
Regulatory correspondence
AI-fit matrix
| AI role | Fit | Human control | Model guidance |
|---|---|---|---|
| Summarize claim file | High | Adjuster verifies | economical |
| Detect missing evidence | High | Adjuster confirms | economical |
| Compare to handling guidelines | Medium/High | Adjuster decides | reasoning |
| Draft claimant, broker, or vendor follow-up | High | Adjuster approves | reasoning |
| Recommend settlement amount | Low/Medium | Human-owned | — |
| Coverage denial or SIU referral decision | Not appropriate | Human-owned | — |
Governance profile
confidential
Sensitive business data. Confirm data handling and access controls are in place before running the pilot.
- Summarize claim file and exception context into a structured packet
- Detect missing evidence items against the required claim file list
- Compare claim details to handling guidelines and flag exceptions
- Draft claimant, broker, or vendor follow-up for adjuster review and approval
- Prepare exception escalation packet for authority review
- Claim file evidence referenced in each AI summary
- Communication drafts and adjuster approval record
- Handling guideline comparison output and exceptions noted
- Coverage and authority escalation decisions
- Final decision and communication evidence for market conduct review
Pilot KPIs
Establish baselines before the pilot starts. These metrics determine whether the workflow actually improved.
| Metric | Baseline method | Unit |
|---|---|---|
| Exception cycle time | Average days from exception flag to resolution, last 30 exceptions | days |
| Touch count | Average number of adjuster actions per exception case | touches |
| Missing evidence rate | Percentage of exceptions with incomplete claim file on intake | % |
| Claim file completeness | Percentage of closed exceptions with required evidence on file | % |
Human authority and governance
- AI does not decide coverage, liability, settlement, denial, SIU referral, or regulatory response.
- Claims adjuster verifies summaries, evidence gaps, guideline comparisons, and communication drafts.
- Authority rules determine which human owns escalation, approval, settlement, or denial.
- The workflow should retain source evidence, drafts, approvals, communications, and decision rationale for audit or market conduct review.
Evidence to inspect
- Recent exception claims with timestamps
- Claim notes and activity history
- Policy, coverage, and endorsement references
- Photos, estimates, invoices, medical records, or vendor reports
- Customer, broker, vendor, or provider communications
- Coverage escalation and authority approvals
- Complaint or regulatory correspondence tied to claim handling
Field calibration questions
- Which claim exception types create the most cycle-time drag today?
- Where do adjusters leave the claim system to assemble evidence?
- Which missing evidence patterns trigger repeated follow-up?
- Who owns escalation when coverage, liability, severity, or authority is unclear?
- Which communications need review before they go to the claimant, broker, provider, or vendor?
- What claim file evidence would compliance, legal, or market conduct review need later?
Measurable outcomes
Exception cycle time
Touch count
Missing evidence rate
Customer update quality
Escalation accuracy
Claim file evidence completeness
30/60/90 adoption path
- Select one exception type
- Inspect recent exception claim files
- Map evidence and communication sources
- Benchmark touch count and cycle time
- Prototype exception packet assembly
- Pilot with one claims team
- Add missing evidence detection
- Add follow-up draft preparation
- Define review and authority controls
- Measure cycle-time and rework impact
- Expand to adjacent exception types
- Integrate with claims workflow system
- Formalize communication and audit evidence
- Reuse the pattern for complaint handling or subrogation review
After 90 days
After claims exception is proven, the pattern extends to the full claims lifecycle, complaint handling, subrogation review, and regulatory correspondence — each shares the evidence-assembly and communication-drafting core.
Source-backed signals
Industry outlook framing AI, data quality, governance, operating model change, and measurable value levers for insurers.
Regulatory context for AI use in underwriting, pricing, claims, fraud, governance, consumer impact, and examination evidence.
Adopted model bulletin emphasizing AI governance, risk management, compliance with insurance laws, and documentation available for regulatory review.
Regulatory source lens for claims handling standards, unfair claims settlement practices, record retention, and market conduct evidence expectations.
Claims-specific perspective on using AI and generative AI to improve claim handling, customer communication, and adjuster productivity.
Insurance GenAI source noting claims processing, private data, regulatory compliance, and source traceability as important design constraints.