Streamlining Loss Run Report Analysis for Aggregate Risk Trends in Workers Compensation, General Liability & Construction, and Commercial Auto – A Guide for the Underwriting Manager

Streamlining Loss Run Report Analysis for Aggregate Risk Trends in Workers Compensation, General Liability & Construction, and Commercial Auto – A Guide for the Underwriting Manager
Underwriting Managers face an increasingly complex challenge: brokers and insureds deliver massive, inconsistent loss run reports, claims history summaries, and loss ratio reports spanning five to ten policy years across multiple carriers and TPAs. Synthesizing these documents to find aggregate risk trends that matter for pricing, appetite, and renewal strategy is a time-consuming, manual effort—especially across Workers Compensation, General Liability & Construction, and Commercial Auto.
Doc Chat by Nomad Data eliminates this bottleneck. Built specifically for insurance document complexity, Doc Chat ingests thousands of pages of loss runs at once, normalizes inconsistent formats, and surfaces aggregate loss run trends for risk management—all in minutes. Whether you want to summarize loss runs automatically for a single account or run an AI analysis of loss run reports in insurance across your entire book, Doc Chat equips Underwriting Managers with instant, defensible insight to drive confident renewal and portfolio decisions.
Why Loss Run Mastery Matters for the Underwriting Manager
In competitive markets, superior insight into past loss performance is your advantage. Yet the documents that carry this insight—loss run reports and claims histories—are notoriously inconsistent across carriers and third-party administrators. For an Underwriting Manager, getting to an accurate, aggregate view of severity, frequency, causation, closure timing, and reserve adequacy is critical to pricing, appetite decisions, and negotiation strategy with brokers. Here’s the rub: doing it at scale, quickly, and accurately has been nearly impossible without intelligent automation.
Across Workers Compensation, General Liability & Construction, and Commercial Auto, the nuances differ, but the goal is the same: turn raw loss runs into a clean, normalized view of risk patterns that can be defended to underwriting leadership, actuaries, reinsurers, and clients at the stewardship table.
The Nuances by Line of Business: What the Underwriting Manager Must See
Loss runs are more than paid and incurred. They hide the signals Underwriting Managers need to steer both account-level and portfolio strategy. Doc Chat is trained to surface those signals in each line of business:
Workers Compensation
In Workers Compensation, frequency can be high and medical inflation persistent, while tail development and closure speed influence true ultimate severity. Underwriting Managers must understand:
- Medical vs. indemnity split, paid vs. reserve, and trends in ALAE
- Lag from Date of Loss to Report Date (reporting behavior and potential late reporting risks)
- Nature of injury (e.g., sprain/strain vs. catastrophic), ICD codes where available
- NCCI class codes and exposure mix shifts (e.g., more 5645 carpentry vs. 5606 executive supervision)
- Modified duty/return-to-work implications and claim closure rates
- Large loss development and reserve adequacy across valuation dates
General Liability & Construction
GL and construction accounts often have longer tails and severity-prone exposures. For wrap-ups (OCIPs/CCIPs) and project-based risks, Underwriting Managers look for:
- Premises and operations vs. products/completed operations splits
- Cause of loss detail (fall-from-height, struck-by, subcontractor injury, product failure)
- Litigation indicators, defense cost trends, and ALAE patterns
- Project-level concentration and location clustering
- Subcontractor mix and insurance controls (via contract references in attachments)
- Reserve movements across valuation dates for severity-prone incidents
Commercial Auto
Auto losses are frequency-driven with outsized severity risk from bodily injury and potential nuclear verdicts. Underwriting Managers need to quantify:
- Losses per million miles, frequency/severity by vehicle class (light, medium, heavy)
- Driver profile indicators (tenure, incident clusters by driver or unit, MVR references)
- Garage locations and regional loss patterns, time-of-day clustering
- Property damage vs. BI/UM, litigated vs. non-litigated trends
- Reserve and settlement velocity, subrogation/salvage recovery patterns
These nuances rarely live cleanly on one page. They’re pieced together across PDFs, scans, spreadsheets, and attachments—sometimes even referenced in FNOL forms, police reports, ISO ClaimSearch reports, or demand letters included with the loss run package. Without automation, Underwriting Managers either over-invest time on a few high-premium accounts or skim across many, risking blind spots that affect loss ratio and hit ratio.
How Loss Runs Are Handled Manually Today—and Why It’s Not Scalable
The current manual process is slow, inconsistent, and brittle:
Underwriting Managers and their teams download emailed PDFs, spreadsheets, and portal exports. They hunt for critical fields—claim number, date of loss, report date, cause of loss, paid, reserve, total incurred, ALAE, coverage part—and then re-key or copy/paste into normalizing worksheets. Valuation dates vary, per-claim reserve development is hard to reconcile, and carriers format codes differently. Loss runs for Workers Comp may list medical-only vs. lost-time differently across years; GL may bury litigation flags in notes; Auto may split claim status across multiple tabs. Even after countless hours, the team still doesn’t have portfolio-level perspective.
Common friction points include:
- Inconsistent formats across carriers/TPAs produce week-long normalization tasks.
- Missing years, stale valuation dates, or incomplete fields require broker back-and-forth.
- Duplicate claim numbers or merged claims confuse trend analysis.
- Reserve changes over time are difficult to trace in static reports, impairing severity trend recognition.
- Hand-built pivot tables risk human error, especially when the team is working under deadline.
- Sampling becomes a crutch; the team deep-dives a few big claims and infers the rest.
It’s no wonder cycle times stretch, negotiation leverage weakens, and renewal strategies become reactive rather than insight-led. As highlighted in Nomad’s perspective on advanced document intelligence—where the work is about inference, not just extraction—traditional approaches break under volume and variability. For a deeper understanding of why document scraping is fundamentally different from web scraping and why it demands new skills and systems, see Nomad’s article Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.
What “AI Analysis of Loss Run Reports in Insurance” Should Deliver
When Underwriting Managers search for AI analysis loss run reports insurance or how to summarize loss runs automatically, they’re not looking for a generic summarizer. They want an underwriting-grade system that understands coverage parts, reserve dynamics, valuation dates, and claim statuses across heterogeneous files—and that can roll it all up into defensible trend analysis. That’s exactly where Doc Chat specializes.
How Nomad Data’s Doc Chat Automates Loss Run Review End-to-End
Doc Chat is a suite of insurance-trained, AI-powered agents that read like seasoned underwriters. It’s designed to handle the messy, high-volume, high-variance reality of loss runs and their related documentation.
Ingestion at Portfolio Scale
Doc Chat ingests entire submission folders—loss run reports, claims history summaries, loss ratio reports, ACORD applications, driver lists, SOVs, prior policies, and even attachments like FNOL forms, police reports, or demand packages. Thousands of pages enter the pipeline simultaneously without extra headcount. Unlike brittle keyword tools, Doc Chat reads context, not just fields.
Normalization Across Carriers and Formats
It standardizes field names, aligns claim statuses, and reconciles valuations. For Workers Comp, it separates medical-only vs. lost-time, identifies TTD/PPD references in notes, and maps NCCI class code exposure shifts. For GL & Construction, it distinguishes premises/operations vs. products/completed ops, flags litigation indicators, and links project identifiers. For Auto, it maps unit numbers, VINs, and driver references, correlating loss frequency with fleet composition. This normalization builds an apples-to-apples base for aggregate analysis.
Aggregate Trend Surfacing and Q&A
With normalized data, Doc Chat computes trendlines Underwriting Managers rely on for pricing and appetite:
- Frequency and severity trends by policy year, coverage part, cause of loss, location, and claimant type
- Reserve movement across valuation dates, large loss emergence, and closure velocity
- ALAE mix, medical vs. indemnity split (WC), litigated vs. non-litigated ratios (GL, Auto)
- Losses per million miles (Auto) and hot spots by garage or route pattern
- Project-level clustering (GL & Construction) and subcontractor-related incidents
- Comparisons to prior carriers’ results and stewardship narrative preparation
Because Doc Chat supports real-time, plain-language Q&A, Underwriting Managers can ask: “Show me five-year frequency and severity trend by cause of loss for the general liability account; highlight any litigation-driven severity,” or “For Workers Comp, list medical-only claims that converted to indemnity and the average lag days,” or “For Commercial Auto, rank drivers and units by incident count and incurred severity.” Doc Chat returns answers with page-level citations and links back to the original reports—ensuring trust, traceability, and audit readiness.
Outputs Built for Underwriting Workflows
Doc Chat delivers structured outputs—Excel/CSV aggregates, dashboards, and underwriting briefs—aligned to your renewal timeline. It can also push structured results into policy admin or underwriting workbenches (e.g., Guidewire, Duck Creek), reducing duplicate entry. For background on how AI-grade document processing turns data entry from months into minutes, see AI’s Untapped Goldmine: Automating Data Entry.
Examples: What Underwriting Managers See in Minutes
Workers Compensation
Doc Chat compiles a five-year view of WC claims by nature and body part, medical-only vs. indemnity mix, average and median incurred by class code, and closure velocity. It flags late reporters and tracks claims converting from medical-only to indemnity. It highlights large-loss development by valuation date and checks if the loss run valuation aligns with the renewal date, prompting updates if stale.
General Liability & Construction
The system separates premises/operations from products/completed operations, surfaces subcontractor-involved incidents, and highlights litigated claims and defense cost drivers. It clusters losses by project or location, correlates scheduled exposures with loss patterns, and identifies early reserve acceleration on severity-prone claims.
Commercial Auto
Doc Chat calculates loss frequency per million miles, identifies high-incident drivers and units, detects time-of-day or route patterns, and distinguishes PD-only from BI-heavy claims. It synthesizes reserve movements over time, pinpoints large-loss emergence, and correlates garage or region with severity spikes.
How the Process Used to Feel vs. How It Works Now
Before Doc Chat: Teams spent days reconciling inconsistent loss runs; pivot tables broke, valuation dates didn’t match, and the final view was partial. Underwriting meetings focused on what wasn’t known rather than on what to do next.
With Doc Chat: Underwriting Managers open a clean, validated aggregate with source-linked proofs. Renewal strategy shifts to “why” and “what to change” versus “what happened where.” The conversation is data-driven, visual, and defensible.
Business Impact: Time, Cost, Accuracy, and Confidence
By turning loss run review from a manual reading task into a trusted, automated intelligence flow, Underwriting Managers capture tangible business value:
- Time savings: Reviews that used to take days compress to minutes—even when loss runs span ten-plus years across multiple carriers.
- Cost reduction: Less manual data prep, fewer outside analyst hours, and minimal rework driven by missing or stale valuations.
- Accuracy and completeness: Page-level citations ensure nothing important slips through; reserve movements and litigation signals are consistently captured.
- Cycle-time advantage: Faster pricing and clearer appetite decisions improve hit ratios while protecting loss ratios.
- Negotiation leverage: Underwriting reviews become insight-led; stewardship decks land with specifics and proofs.
- Portfolio visibility: Underwriting Managers can roll up trends across books, segments, regions, and brokers—not just at the account level.
For a real-world view of how high-volume insurance document analysis transforms speed and quality, see Reimagining Insurance Claims Management: Great American Insurance Group Accelerates Complex Claims with AI. While that story focuses on claims, the same Doc Chat engine and approach underlie underwriting-grade ingestion, normalization, and Q&A.
What Underwriting Managers Typically Ask Doc Chat (and Get Back Instantly)
Because Doc Chat supports natural-language Q&A grounded in your documents with source citations, Underwriting Managers use it conversationally:
- “Summarize loss runs automatically for this GL account: five-year frequency/severity by cause and location; note litigated claims.”
- “Run an AI analysis of loss run reports in insurance across my construction segment; compare premises vs. products exposure outcomes.”
- “Show aggregate loss run trends for risk management for my commercial auto book—losses per million miles, driver clusters, BI vs. PD mix.”
- “Identify WC claims that converted from medical-only to indemnity and compute average lag days by year; cite sources.”
- “List large losses with reserve increases >$50k in the past 12 months and provide reserve movement commentary.”
Each answer includes the computed metric, a short narrative in underwriting language, and links to page-level evidence in the original reports.
From Manual Reading to Strategic Underwriting: A Step-by-Step Workflow
- Drag-and-drop ingestion: Upload loss run reports, claims history summaries, loss ratio reports, and any related files (applications, driver lists, SOVs, FNOLs, ISO reports).
- Automated completeness check: Doc Chat verifies valuation date recency, coverage parts, year span, and missing fields—prompting requests to brokers if needed.
- Normalization and alignment: Field names, claim statuses, causes of loss, and coverage parts are standardized across carriers and TPAs.
- Aggregate build: Frequency/severity trends, reserve movements, closure velocity, and line-specific metrics (e.g., losses per million miles) are computed and visualized.
- Underwriting Q&A: Ask questions in plain English; receive structured answers with citations, exports, and underwriting-ready summaries.
- Export and integrate: Download Excel/CSV, or push structured fields to your underwriting workbench for rating and referral.
Ensuring Trust: Citations, Controls, and Compliance
Doc Chat is designed for auditability and defensibility. Every insight references the exact page and passage from the source document. IT and compliance teams control access, audit trails, and retention policies. Nomad Data maintains enterprise-grade security (SOC 2 Type 2) and supports integrations without compromising controls. This explainability and governance are essential when underwriting decisions must stand up to internal reviews, reinsurer diligence, and broker scrutiny. For a view into explainability at scale and transformation of document-heavy flows, see Reimagining Claims Processing Through AI Transformation.
Why Nomad Data Is the Best Partner for Underwriting Managers
Most AI tools stop at basic extraction. Nomad’s Doc Chat goes beyond, capturing the unwritten underwriting rules and nuances that live in your playbooks and leaders’ heads. We train Doc Chat on your standards and outputs—so the system speaks your underwriting language and delivers your preferred metrics and formats.
What sets Nomad apart:
- Volume without headcount: Ingest thousands of pages at once. Entire books of business analyzed in minutes.
- Complexity mastered: Coverage parts, reserve dynamics, valuation recency, litigation signals—Doc Chat handles the nuances.
- The Nomad Process: We encode your underwriting playbooks, appetite, and output templates. You get a customized, underwriting-grade solution.
- Real-time Q&A: Ask “show aggregate loss run trends for risk management” and get source-cited answers across massive document sets.
- White glove service: We partner deeply with your underwriting and IT leaders—co-creating outputs and integrating into your workflow.
- Fast implementation: Typical implementation is 1–2 weeks, with immediate value via drag-and-drop mode.
When your underwriters see the system produce accurate, traceable summaries in seconds—on their own loss runs—the trust follows. This mirrors the adoption pattern carriers observed in claims: once users experience instant, page-cited answers, manual review becomes the exception, not the norm. For parallel lessons learned, see the End of Medical File Review Bottlenecks and how explainability accelerates confidence.
Impact on Renewal Strategy and Portfolio Management
With loss run analysis automated, Underwriting Managers elevate from document prep to strategic leadership:
- Sharper renewal strategy: Arrive at broker calls with data-led positions on deductibles, coverage carve-outs, driver management, subcontractor controls, or return-to-work plans.
- Fewer surprises: Reserve movements, late reporters, and litigation clusters surface early, not mid-renewal.
- Portfolio balance: Aggregate insights reveal where appetite should tighten or widen—by industry, geography, project type, fleet composition, or broker.
- Better reinsurer conversations: Show reserve development and severity control trends with page-cited evidence to support treaty negotiations.
- Actuarial alignment: Provide clean, normalized loss histories that actuaries trust, shortening feedback loops and improving pricing accuracy.
Realistic Outcomes You Can Expect
Across carriers and MGAs, Underwriting Managers typically report:
- 60–90% reduction in time-to-insight on complex accounts
- Consistent capture of reserve movements and litigation flags that often get missed in manual triage
- Improved hit ratio from faster, more confident quotes paired with data-backed terms
- Reduced leakage via earlier identification of severity-prone patterns and stale valuations
- Higher underwriter satisfaction with more time spent in analysis and strategy versus spreadsheet cleanup
These results align with broader, documented improvements when AI handles large, unstructured insurance dossiers. For more context, see AI for Insurance: Real-World AI Use Cases Driving Transformation.
FAQ for Underwriting Managers
Does Doc Chat handle both PDFs and spreadsheets?
Yes. Doc Chat ingests mixed-format submissions—PDF loss runs, Excel claims histories, and even scanned pages. It merges them into a single, normalized dataset for analysis and Q&A.
Can it separate GL premises/operations from products/completed operations?
Yes. Doc Chat classifies claims by coverage part and cause of loss, and can flag project identifiers, subcontractor involvement, and litigation indicators.
Will Doc Chat compute losses per million miles for Commercial Auto?
Yes, when mileage or exposure data is provided (e.g., from ACORD app or submission exhibits). It aligns losses with exposure metrics to produce per-unit KPIs.
How does it handle different valuation dates across carriers?
Doc Chat normalizes valuations and highlights recency gaps, prompting updates from the broker if needed. It tracks reserve movements across valuations to surface development.
Can it export results to my underwriting workbench?
Yes. You can export Excel/CSV or integrate via API to systems like Guidewire or Duck Creek. We tailor fields and formats to your workflows.
What about governance and auditability?
Every answer comes with page-level citations and a complete audit trail. Security aligns to enterprise standards (including SOC 2 Type 2). Your data remains under your control.
Getting Started: From Proof to Production in 1–2 Weeks
Doc Chat is designed for immediate impact. Underwriting Managers can start the same day with drag-and-drop analysis of their own loss runs. As you see value, Nomad configures a tailored output (your columns, your charts, your underwriting brief) and integrates with your systems—typically in 1–2 weeks. The combination of white glove onboarding with insurance-trained agents ensures that you get a solution, not just a toolkit.
Your Edge at Renewal Starts with Better Loss Runs
In markets where speed and insight determine outcomes, Underwriting Managers who can transform loss runs into strategy win more often. Whether it’s AI analysis of loss run reports in insurance, the ability to summarize loss runs automatically, or rolling up aggregate loss run trends for risk management across your portfolio, Doc Chat gives you the clarity, speed, and confidence to lead.
See how Doc Chat for Insurance turns loss runs into underwriting insight—at enterprise scale and in minutes.