Delegated authority means MGAs are the first claims handler
Claims triage shifts from serial process to parallel with AI, not headcount
MGAs with delegated authority operate as the claims handler for carriers. When a claim is reported, it lands at the MGA first. The MGA triages, determines coverage, estimates reserve, and provides development recommendations. Only then does it go to the carrier. Speed and accuracy are the economics of delegated authority.
Delegated authority MGAs own claims cycle time. Every delay costs margin.
Where capacity bleeds today
The bottlenecks AI removes
Case triage is pattern matching — humans process it serially
Triage answers: Is this covered? What POL? What reserve? These are pattern-matching questions. Claims adjusters apply a triage logic tree and process cases one at a time. An MGA with 100 claims per week is doing 50-100 hours of triage work. That's 1.5-2.5 FTEs minimum.
Guidelines are hidden in manuals and underwriter tribal knowledge
Triage guidelines live in carrier manuals, underwriter emails, loss history, and adjuster memory. When a new adjuster joins, they learn from a senior. Guideline updates come as email memos. Lose an experienced adjuster, and triage speed drops 20-30%. New adjusters make mistakes during ramp-up.
Claim development requires analysis — reserve, recommendation, coverage decision
After triage, claims need development: estimate reserve, recommend recovery actions, assess coverage edge cases. An adjuster reads facts, applies guidelines, estimates loss, and recommends next steps. A complex claim can take 2-4 hours. Development alone consumes 1-5 FTEs per 100 claims per week.
AI applies triage rules at scale with AI delegated claims MGA, flags exceptions for human review
AI ingests every guideline, every historical claim, every triage rule. It learns patterns: covered versus excluded, reserve accuracy by claim type, escalation thresholds. When new claim arrives, AI applies triage patterns, estimates reserve, and flags exceptions. Routine cases are triaged in seconds; complex cases get an AI recommendation and human review.
AI handles 85-90% of routine triage. Adjusters focus on 10-15% of complex cases where judgment matters.
| Dimension | Before AI | After AI |
|---|---|---|
| Case triage cycle time | 30-60 min per case | 2-3 min with AI analysis plus human review flag |
| Reserve accuracy (within 10%) | 72-78% on first estimate | 94-97% with AI reserve model |
| Guideline compliance rate | 88-92% (tribal knowledge drift) | 99%+ (AI applies rules consistently) |
| Cases per adjuster per week | 15-20 (triage plus development) | 60-80 (humans focus on complex exceptions) |
| Appeal rate (coverage disputes) | 6-9% require rework | 1-2% when guidelines are applied consistently |
Triage cycle time drops 40-50%. Appeal rates fall 25-30%. Adjuster capacity per team expands 60% without hiring.
Where this sits in the $84B pool
$30.8B of MGA revenue is AI-compressible. Each bar is an activity — width is revenue share, height is operating margin. This workflow sits where the bar lands. Click any other to explore it.
Related MGA AI activities
The profit pool→
Interactive visualization of 12 MGA activities by revenue, margin, AI impact, and key players. See where the MGA automation opportunity concentrates and where it migrates.
The 24-month timeline→
Which MGA workflows to rebuild first, why the sequence is causal, and where the margin compounds. Ordered by readiness, dependency, and displacement speed.
The thesis→
Moative's position on which MGA activities gain, which lose, and who captures the difference. Not a survey of AI use cases in insurance. A position on where value lands.
Underwriting authority and risk selection→
$5.3B. The MGA core moat. AI augments underwriter throughput and selection quality without replacing specialist judgment.
Submission intake and triage→
$4.1B. 70% compressible. Document extraction, appetite matching, and go/no-go in seconds. AI submission processing cuts time-to-quote by 60–80%.
Policy issuance and coverage checking→
$2.8B. Policy generation, coverage verification, and endorsement processing automated end-to-end. Eliminates a major source of LAE.
Market access and E&S placement→
$2.7B. AI appetite matching routes submissions to the right carrier in seconds. Declination rates fall. Bind rates rise.
Loss run and risk data analysis→
$2.5B. Multi-year loss run PDFs parsed in minutes. AI turns a 45-minute analyst task into a 90-second automated output.
Portfolio data analytics and bordereaux→
$2.3B. Bordereaux automation and real-time portfolio monitoring. AI makes monthly reporting no harder than quarterly.
Program design and management→
$2B. AI-assisted program structuring, loss modeling, and carrier negotiation support. Faster program launches with better loss projections.
Renewal underwriting and retention→
$1.7B. Renewal scoring flags defection risk 90 days out. AI identifies the books most likely to non-renew before the carrier does.
Risk advisory and client analytics→
$1.5B. AI-generated risk reports and portfolio benchmarking at scale. Advisory that used to require a team now runs on a model.
Compliance and surplus lines filing→
$1.4B. Stamping, diligent search, and multi-state filing automation. AI reduces compliance overhead without adding headcount.
Distribution and producer management→
$1.1B. Producer onboarding, licensing, and performance analytics automated. AI identifies which producers are growing the right book.
Co-operate, not consult
We take position in the workflows we automate.
MGA margin sits in intake velocity, underwriting triage, and claims throughput. We run these — not map them. Our economics are equity in the margin you recover, not retainer on the analysis.
Talk to a principalThe full $84B pool
See where the MGA margin moves.
Map every activity — width is revenue share, height is operating margin. Click any bar to explore that workflow.
View the profit poolHow should MGAs prioritize delegated claims intake?
How much of the MGA claims adjuster role is pattern-based triage vs. judgment?
Roughly 60-70% of adjuster work is pattern-based triage: apply guidelines, match claim profile, estimate reserve, flag for escalation. The remaining 30-40% is judgment: complex coverage disputes, reserve adjustments for unusual circumstances, subrogation decisions. AI handles the triage; humans handle judgment.
What's the typical reserve recommendation accuracy improvement with AI analysis?
Most MGAs see reserve accuracy improve from 72-78% (within 10% of final reserve) to 94-97% when AI analyzes the claim profile against similar historical claims. AI learns reserve patterns by claim type, line of business, and loss characteristics, then applies them consistently.
How does AI ensure delegated authority guidelines are applied consistently?
AI ingests every carrier guideline, every MGA override rule, and every historical triage decision. It applies the ruleset to every new claim, then flags exceptions for human review. This eliminates the tribal knowledge problem: every guideline is enforced the same way regardless of which adjuster reviews the file.