Supercharging Loss Run Analysis for Complex Submissions with Doc Chat — Commercial Auto, General Liability & Construction, Property & Homeowners

Broker Submission Specialists live in a world of deadlines, dense documentation, and high-stakes detail. The moment a complex submission lands, the clock starts: hundreds of pages of loss run reports, prior carrier claims summaries, and broker narratives must be normalized, analyzed, and packaged into a clean story that underwriters can price quickly and confidently. The challenge is intensifying—submission volumes and file sizes keep growing while market cycles compress the time available to make profitable decisions. This is exactly where Nomad Data’s Doc Chat for Insurance changes the game.

Doc Chat is a suite of purpose-built, AI-powered agents that read entire claim files end-to-end, surface the signal in the noise, and output the exact views your underwriting teams need. For Commercial Auto, General Liability & Construction, and Property & Homeowners placements, Doc Chat delivers instant, defensible insights from the very documents that historically took hours—or days—to review. If you are evaluating loss run report automation for underwriters or exploring AI review of complex broker submission loss runs, this article explains how Broker Submission Specialists can transform speed-to-quote, hit ratios, and underwriting quality without adding headcount.

The Broker Submission Specialist’s Reality: Volume, Variability, and Velocity

Loss runs are your truth set—but they rarely arrive in a tidy, uniform package. One account might include five years of mixed-format loss runs across four prior carriers, each with a different layout for paid, incurred, reserves, and subrogation. Another might include scanned PDFs where key columns span multiple pages, with supplemental ISO claim reports, FNOL forms, OSHA 300/300A logs (for construction risks), Statement of Values (SOV) files (for property), and narrative emails from the broker that introduce context but no standard structure. As a Broker Submission Specialist, you must turn all of this into a normalized view of frequency, severity, trend, and anomaly—fast.

In Commercial Auto, underwriters care about at-fault vs. not-at-fault collisions, severity of BI claims, litigation flagging, DOT-reportable events, and how losses align with exposure measures like power unit count, radius, and MVR results. For General Liability & Construction, they want premises vs. products/completed ops splits, wage class exposure, subcontractor involvement, and signs of repetitive loss causation on jobsites. In Property & Homeowners, they need peril-level signals (wind, hail, water, fire), AOP vs. cat-coded events, age of roof/updates, sprinkler/protection class, and adverse patterns across a schedule. Every line requires a different lens, but all begin with the same cornerstone: an accurate, exhaustive reading of the loss run reports and prior carrier claims summaries.

How the Manual Process Looks Today

Even at high-performing shops, manual loss run analysis is still the norm. Documents arrive via email or submission platform. You download, sort, rename, and open each file. Core steps typically include: de-duplicating multiple versions, repairing mis-scanned columns, stitching multi-page tables, and mapping carrier-specific column names into a standard model (e.g., Paid Indemnity, Paid Expense, Reserve Indemnity, Reserve Expense, Total Incurred, Recoveries/Subrogation). Then comes validation against broker-provided exposure: payroll and receipts for GL, unit count and mileage for Auto, TIV/COPE for Property. Only then can you calculate frequency, severity, loss rates (per $1M exposure), and loss ratios by policy year and line of coverage. For complex accounts, this can easily consume a full day—or several.

Additional nuance adds time. You look for claim clustering by cause of loss; flag large open reserves nearing policy limit; detect loss development over time; separate shock, cat, and attritional losses; identify recoveries that are pending or missing; and note late-reported losses or excessively long time-to-close. You also check for error patterns that affect pricing, such as mis-coded perils or missing recoveries that understate net loss. Finally, you build a summary for the underwriter: an executive view of five-year history with notable trends and recommended RQs (requests for more information). All of that must be checked for accuracy and supported with page-level references because the underwriter—and sometimes the broker and insured—will ask, “Where did this number come from?”

Why Loss Run Analysis Is Especially Nuanced by Line of Business

Commercial Auto

Auto loss runs often combine physical damage and liability, and claims may be indexed by unit, driver, or incident. Submission teams must spot signal in questions like: Are BI claims rising? Are large losses correlated with certain terminals or regions? Do we see patterns linked to driver tenure or vehicle type? Did recoveries or salvage reduce net severity? How do development patterns look for open BI claims? Clear separation of at-fault vs. not-at-fault matters, as do litigation tendencies and DOT-reportable events. Underwriters will also test your analysis against MVR summaries and telematics when available, and they’ll calibrate frequency against exposure metrics like units, mileage, and garaging density.

General Liability & Construction

GL loss runs require segmentation between premises/operations and products/completed operations. For construction, trends in struck-by incidents, falls, or subcontractor-caused losses can materially impact appetite. OSHA logs—when provided—help triangulate patterns with loss runs and claims narratives. Open claims with large expense reserves may indicate defense complexity; late-reporting indicates possible controls issues. Underwriters watch for repetitive causes on similar project types, evidence of risk transfer (certificates, additional insured endorsements), and recovery pursuit where third parties share liability.

Property & Homeowners

Property loss runs must align with the SOV and COPE details: construction type, roof age, sprinklers, alarms, and protection class. Separating attritional water damage from cat-coded wind/hail events informs the AOP vs. cat story and affects deductibles and sublimits. Underwriters care about peril trends by location, large open reserves, prior salvages, and whether mitigation steps (e.g., roof replacements) are visible in subsequent policy years. For Homeowners portfolios, the distribution by state/county and the historical cat footprint are indispensable.

Where Human-Only Review Struggles

The variability of loss run formats is the killer. A single submission can include carrier spreadsheets, scanned PDFs, tables split across pages, and loss narratives sprinkled in adjuster notes. Carrier-specific field names for the same concept (e.g., Total Incurred) force manual mapping every time. And because humans tire, the deeper into a 300-page packet you get, the more likely it is that a key reserve line, subrogation note, or cause-of-loss field is overlooked. This is precisely the complexity gap Nomad Data identified in “Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs,” where the insight you need often isn’t explicitly written in one neat field—it’s inferred across pages.

For many Broker Submission Specialists, the real bottleneck isn’t the arithmetic—it’s the reading. Long packages require hours of scrolling to locate the pages that matter. As documented in “Reimagining Insurance Claims Management,” seasoned teams lose days searching for the same facts on every submission. That cost shows up in cycle time, burn-out, and missed opportunities when market windows close before the story is ready.

How Doc Chat Automates Loss Run Analysis End-to-End

Doc Chat ingests entire submission packets—loss run reports, prior carrier claims summaries, broker narratives, SOV/COPE, OSHA logs, ISO claim reports, even email threads—and instantly normalizes them. It recognizes the dozens of ways different carriers label Paid, Reserve, and Incurred. It reconstructs tables split across pages, reads scanned PDFs, and links every KPI back to a page-level citation for auditability. Then it builds fully standardized views by policy year, line of coverage, cause of loss, and status (open/closed), so your underwriting partners can trust the picture and move to pricing faster.

Because Doc Chat is trained on your playbooks, it thinks the way your team does. For a Commercial Auto account, you can ask: “List all open BI claims with incurred > $50,000 and show paid-to-incurred ratio, litigation status, and driver tenure.” For GL & Construction, request: “Break out frequency and severity for products/completed ops vs. premises ops for the last five policy years; flag repetitive causes of loss and show subcontractor involvement if mentioned.” For Property & Homeowners, prompt: “Separate AOP vs. cat-coded events; trend water vs. wind/hail; map large open reserves to SOV locations and show any mitigation referenced in later policies.” Responses return in seconds, with clickable references to every source page.

In other words, Doc Chat doesn’t just summarize—it institutionalizes the unwritten rules your best people use. That’s the core of the Nomad process described in our post on “AI’s Untapped Goldmine: Automating Data Entry”: capturing tribal knowledge and turning it into consistent, repeatable analysis that scales with your submission volume.

What Happens to the Manual Steps You Do Today

With Doc Chat, the steps you spend the most time on are either automated or condensed into a single query-driven workflow. Instead of rekeying columns, manually stitching tables, and creating pivot tables from scratch, you drag and drop the submission files and immediately begin interrogating the results:

“Show five-year frequency/severity by cause of loss for GL; index by payroll and flag any year where loss rate exceeds 2%.”
“For Auto, compute losses per million miles; separate at-fault vs. not-at-fault if available; list open claims by age.”
“For Property, produce a peril-level triangle; pull out all water damage claims over $25,000 and show time-to-close and recovery notes.”

Doc Chat exports everything to your preferred formats (Excel/CSV, BI feeds) and integrates with submissions workbenches or core systems via API. You get the views your underwriters prefer—standard five-year summaries, per-claim listings, development snapshots, ALAE splits, loss triangles, and open claim detail—already mapped and verified.

Key Capabilities Tailored to Loss Run Review

Volume without headcount: Doc Chat reads entire packets—thousands of pages—so a job that took a day gets done in minutes. Complexity with confidence: Policy-year mapping, open/closed status, peril and cause-of-loss segmentation, and recovery detection are handled consistently. Real-time Q&A: Ask any question in plain English and get an answer with citations. Thorough and complete: The agent surfaces every reference to coverage, liability, damages, recoveries, and reserve changes so nothing important slips through.

For teams searching the web for loss run report automation for underwriters or piloting an AI review of complex broker submission loss runs, Doc Chat provides enterprise-grade reliability and auditability. It’s designed for regulated environments and built around your controls.

Business Impact: From Days to Minutes—and Better Pricing Decisions

Submission teams measure success by speed-to-quote, data quality, and win rate. Doc Chat moves all three. Submissions that previously required a full day of reading and spreadsheet wrangling now compress into a quick upload and question-driven review. Underwriters get a trustworthy view of frequency, severity, and anomalous loss patterns faster. Accounts that shouldn’t be quoted are eliminated earlier; strong accounts move forward quickly, improving broker relationships and hit ratios. And because conclusions are backed by page-level citations, review cycles with underwriting leadership, actuaries, or reinsurance partners are shorter and more productive.

The compounding effect is profound. Fewer manual touchpoints reduce loss-adjustment expense on mid-term service work and cut downstream leakage caused by missed red flags in the pre-bind stage. Teams can finally analyze the entire stack—including supplementary documents like FNOLs or adjuster notes—instead of skimming. As discussed in “Reimagining Claims Processing Through AI Transformation,” machine consistency outperforms human endurance on page 1,500 as reliably as page 1.

Feature Overview: What You Can Do on Day One

To ground the impact in practical terms, here is what Broker Submission Specialists typically accomplish in their first week using Doc Chat:

• Upload an entire complex submission: five years of loss runs from multiple prior carriers plus SOV/COPE, OSHA logs, broker narratives, and ISO reports. Begin questioning immediately with page-level citations.
• Produce a standardized five-year summary by line of coverage, policy year, and cause of loss; separate open vs. closed; compute loss rates by relevant exposure (payroll/receipts for GL, units/miles for Auto, TIV for Property).
• Generate an “exceptions” view: large open reserves > threshold, any policy year with loss rate > benchmark, repetitive causes of loss, late-reported claims, unusually long time-to-close, and claims with pending recoveries.
• Export underwriter-ready outputs: frequency/severity tables, claim listings with Paid/Reserve/Incurred/Recoveries, peril-level triangles, and a short narrative summarizing the account’s risk story.

Because Doc Chat institutionalizes your team’s best practices, results are consistent regardless of who touches the file, reducing rework and improving training outcomes for new hires.

Integration and Security: Built for Insurance Workflows

Doc Chat works the way your team already works. Start with drag-and-drop during the pilot phase. As adoption grows, integrate via API with your submission intake, data lake, or core systems (Guidewire, Duck Creek, Origami Risk, Salesforce). Routes can be configured so processed summaries automatically populate downstream templates your underwriters use—ACORD 125/126/127 supplemental summaries, appetite checklists, or custom Excel models for loss picks. Outputs can also be appended to your document management system with metadata tags for easy retrieval during audits.

Security is table stakes. Nomad Data maintains SOC 2 Type II controls, supports granular access permissions, and preserves full audit trails with page-level references for every extracted fact. Sensitive elements in medical or litigation-heavy claims are handled with the same rigor we apply in health and legal lines. The result is speed with defensibility—a combination auditors, reinsurers, and compliance teams appreciate.

Measured Outcomes: Time, Cost, Accuracy

Clients consistently report step-change improvements across three axes:

• Time savings: Days-to-minutes compression for complex loss run packets; faster speed-to-quote materially improves bind rates and broker satisfaction.
• Cost reduction: Fewer manual touches; less overtime during market crunches; reduced need for ad hoc external analytics support.
• Accuracy and consistency: Page-level citations reduce rework; consistent mapping across carriers eliminates interpretation errors; more thorough analysis catches red flags before bind.

Beyond immediate efficiencies, there’s a strategic upside: With Doc Chat, your team can finally analyze every page of every submission, not just the highlights. That means fewer missed exclusions, better selection against appetite, and stronger negotiating positions with markets—especially when prior-year loss development is mapped and explained with evidence.

Why Nomad Data and Doc Chat Are the Best Fit for Loss Run Intelligence

Nomad Data designed Doc Chat specifically for the messy reality of insurance documentation. Unlike point solutions that stop at OCR or generic summarization, Doc Chat performs the cognitive work Broker Submission Specialists actually do—reading like a domain expert, normalizing carrier idiosyncrasies, and surfacing inferences no single field ever states outright. Our differentiators map directly to your needs:

Volume: Ingest entire claim files and submission packets—thousands of pages at a time—without adding staff. Reviews move from days to minutes.
Complexity: Exclusions, endorsements, and reserve language hide inside dense, inconsistent documents; Doc Chat finds and normalizes them.
The Nomad Process: We train on your playbooks and output formats so your underwriters get familiar, desk-ready deliverables.
Real-Time Q&A: Ask questions like “Do any open losses exceed 50% of limits?” and get answers instantly—always with citations.
Thorough & Complete: No blind spots. Every reference to coverage, liability, damages, recoveries, reserves, and development is surfaced.

Just as important is the service model. Nomad delivers a white-glove engagement that captures your unwritten rules and encodes them into Doc Chat’s presets. Implementation typically takes one to two weeks from kickoff to first production use. We co-create your loss run normalization schema, set thresholds for exception reports, and align exports to your underwriting model. You’re not buying generic software—you’re gaining a partner that evolves with your needs.

How Doc Chat Supports Each Line of Business

Commercial Auto

Doc Chat flags at-fault vs. not-at-fault trends, isolates BI severity and litigation, correlates losses with unit count and mileage, and highlights open reserves nearing policy limit. It surfaces salvage and subrogation notes, links them to net incurred, and presents development paths for large claims. The underwriter gets a clear view of driver-, vehicle-, and region-level signals, backed by source citations.

General Liability & Construction

Doc Chat separates premises/operations from products/completed operations, surfaces repetitive causes (e.g., falls, struck-by), and links any subcontractor references to recovery potential. It integrates OSHA logs where available, aligning incident types with loss run patterns. Late-reporting and long time-to-close metrics are highlighted for control assessment.

Property & Homeowners

Doc Chat distinguishes AOP vs. cat-coded events, trends peril-level frequency/severity (water, wind, hail, fire), and maps losses to SOV locations. It notes mitigation references—like roof replacements—and reflects improvements in subsequent years, helping underwriters calibrate deductibles, sublimits, and credits.

From Reading to Deciding: Elevating the Broker Submission Specialist

Doc Chat doesn’t replace Broker Submission Specialists; it amplifies them. The reading and normalization work that drains time is automated. Your role shifts to directing the analysis—asking better questions, pursuing clarifications with the retail broker only where evidence suggests it matters, and packaging a crisper story for underwriters. This shift builds trust on all sides: brokers get faster feedback and fewer rework requests; underwriters see a consistent, well-supported narrative; leadership sees improved hit ratios with tighter loss selection.

Frequently Asked Questions from Submission Teams

How does Doc Chat handle scanned loss runs?
It reconstructs tables and recognizes multi-page splits, mapping carrier-specific columns to your standard schema. When text extraction is ambiguous, answers still include page-level image citations so reviewers can verify quickly.

Can we define our own KPIs and exception thresholds?
Yes. During implementation we codify your definitions of frequency, severity, open-claim age thresholds, reserve triggers, recovery expectations, and late-reporting windows. These become reusable presets.

Will it export to our underwriting templates?
Doc Chat outputs to Excel/CSV and connects via API to your workbench. Many clients auto-populate custom Excel loss pick models and appetite checklists.

Is this safe for PHI/PII and litigation-heavy claim histories?
Yes. Nomad Data is SOC 2 Type II and designed for regulated environments. Every answer includes traceability to the source page.

How quickly can we be live?
Most teams are live in 1–2 weeks. You can start by dragging and dropping real submissions on day one of the pilot.

Implementation: A Simple, Proven Path

Our onboarding starts with your real submissions—no synthetic demos. We load several recent accounts across Commercial Auto, GL & Construction, and Property & Homeowners. Your team asks the kinds of questions you normally ask under the gun. We observe, capture your playbook, and turn it into Doc Chat presets. Within days, you’ll be generating five-year standardized summaries, exceptions lists, and underwriter-ready exports with source citations. As trust builds, we wire outputs into your submission pipeline, so every complex package is transformed the same way—on time.

The Strategic Payoff: Better Risk Selection, Stronger Market Position

Submission excellence is a competitive advantage. When you deliver complete, validated loss stories faster than peers, you earn more “first looks,” reduce back-and-forth with carriers, and win more quotes at the right price. In soft markets, speed and quality differentiate. In hard markets, they’re survival. With Doc Chat, you don’t have to choose between speed and thoroughness; you deliver both every time.

Closing the Loop with Your Stakeholders

Because every insight links to the exact page in the source document, Doc Chat enables transparent collaboration with brokers, underwriters, actuaries, and reinsurers. If a carrier questions a large open reserve or a cause-of-loss categorization, you can supply the citation instantly. This shortens review cycles and builds confidence that your submission package is not only fast but defensible. It also positions your team to handle surge volumes—RFP seasons, renewals, or consolidations—without stretching staff.

A New Standard for Loss Run Review

For teams evaluating loss run report automation for underwriters or considering an AI review of complex broker submission loss runs, Nomad Data’s Doc Chat sets a new standard. It reads like your best analyst, never gets tired, and always shows its work. The result is a submission process that runs at the speed of your markets, not the speed of manual reading.

Explore how Doc Chat for Insurance can go live in your environment in one to two weeks, with a white-glove setup that mirrors your exact workflows and output requirements. For more perspective on what’s possible when reading stops being the bottleneck, see The End of Medical File Review Bottlenecks. While that article focuses on medical files, the same transformation applies to loss runs: machines do the reading; your people do the deciding.

Next Steps

If you’re ready to compress days of loss run review into minutes—and strengthen every part of your submission pipeline—schedule a working session with the Nomad team. Bring recent submissions across Commercial Auto, General Liability & Construction, and Property & Homeowners. We’ll load them, ask your questions, and show you how quickly Doc Chat turns documents into underwriting-ready intelligence—with citations on every line.