Carriers Send PDFs, MLAs Reformat Data
Extract loss data automatically. Shift MLAs to analysis.
Carriers transmit loss runs as PDFs—sometimes scanned, sometimes riddled with formatting noise. MLAs retype or copy-paste into structured format. A single five-year loss run takes 30-45 minutes to extract by hand. Errors compound: missing claims, transposed dates, unit mismatches (total incurred vs. paid).
MLA time gets consumed by reformatting and normalization, not analysis.
Where capacity bleeds today
The bottlenecks AI removes
Pattern Recognition Is the Skill (Humans Are Slow)
The real skill is spotting trends: increasing claim frequency, reserve creep, coverage concentration, severity volatility. But MLAs are drowning in extraction. Trends hide because they never get to analysis—they're stuck in data prep. Five-year trending lives in separate spreadsheets, never consolidated.
Trends Get Lost in One-Off Reviews
Each submission is reviewed in isolation. You see the current year claim count, but not the three-year trajectory. Is claim frequency growing? Is it seasonal spike or structural shift? You don't know because each submission's analysis starts from scratch.
AI loss run analysis insurance: structure extracted, trends surfaced instantly
AI reads the PDF loss run (scanned or native), extracts tables, OCRs text, builds structured data. It consolidates multi-year history, calculates frequency, severity, reserve trends, and flags anomalies. Underwriter sees a summary: 'Frequency up 40% YoY, reserves increased 18%, no claims this year in Coverage X (exposed).'
MLAs Shift to Exception Handling, Not Data Entry
MLAs now review AI-extracted data for edge cases, verify complex claims, and research outliers. They focus on the 5% of submissions where trend analysis surfaces questions. The 95% of routine loss runs process without MLA overhead. Your MLA team capacity expands 4-5x without hiring.
Exception-driven MLA workflows free margin across underwriting.
| Dimension | Before AI | After AI |
|---|---|---|
| Loss Run Extraction Time | 30-45 min per submission | 2-3 min (auto-parse) |
| Data Entry Accuracy | 92-95% (manual retype) | 99%+ (OCR+validation) |
| Trend Analysis Scope | Current year (1 year) | 3-5 year history (consolidated) |
| Anomaly Detection | Missed (no time for scanning) | 100% automated flagging |
| MLA Time per Submission | 40 min (extract + review) | 5 min (exception review only) |
4-5x submission volume without new hires. For 5k submissions monthly with 5 MLAs, that's $350k salary savings or pure capacity.
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.
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 much of the MLA role is spent on loss run data entry vs. analysis?
Industry data shows 70-80% of MLA time is extraction and reformatting. True analysis (trend spotting, exception handling) is only 20-30%. AI flips this ratio—MLAs spend 70% on analysis, 30% on validation.
What hidden claims patterns does AI loss run analysis typically reveal?
AI finds: frequency trends (up 3x vs. peer cohort), reserve creep (75% increase over 5 years), coverage gaps (specific coverage never claimed but exposed here), and severity outliers (single claim 5x typical cost).
Can AI loss run systems work with carrier PDFs in non-standard formats?
Yes. Moative's OCR handles scanned PDFs, native PDFs, and handwritten notes. It normalizes dates, currency, and coverage codes across carrier templates. Rare edge cases (handwriting, very old scans) flag for manual review.