Supercharging Loss Run Analysis for Complex Submissions in Commercial Auto, GL & Construction, and Property with Doc Chat – For Broker Submission Specialists

Broker Submission Specialists live at the center of complex risk stories. Every day, you wrangle loss run reports, prior carrier claims summaries, and sprawling broker submissions across Commercial Auto, General Liability & Construction, and Property & Homeowners. The challenge? Turning thousands of pages and countless spreadsheet tabs into a crisp narrative of frequency, severity, and trend. It’s slow, error-prone, and often the difference between a fast, confident quote and a missed market opportunity.

Nomad Data’s Doc Chat changes the game. Doc Chat for Insurance is a suite of purpose-built, AI-powered agents that ingest entire submission packets—loss run reports, prior carrier claims summaries, statement of values, schedules, endorsements, and more—and deliver instant, defensible insights. Think “loss run report automation for underwriters” that actually understands your lines of business, your playbooks, and your carrier preferences. With real-time Q&A, you can ask: “Show 5-year frequency per 100 vehicles,” “List all severity drivers over $100K incurred,” or “Flag anomalous patterns in the last carrier’s loss run,” and get answers in seconds, complete with source citations.

Why Loss Run Analysis Is Broken for Broker Submission Specialists

Loss runs should be straightforward: a historic record of claims that informs risk appetite, pricing, and terms. In reality, they’re a maze. Formats vary wildly by prior carrier, third-party administrator, and broker. Column labels change from “incurred” to “total loss,” from “paid to date” to “indemnity paid.” Causes of loss are sometimes coded, sometimes free text. For a Broker Submission Specialist tasked with packaging an account for underwriters across Commercial Auto, GL & Construction, and Property & Homeowners, every inconsistency slows you down and invites risk.

Commercial Auto loss runs often span multiple fleets, operating territories, and policy years. You’re expected to quickly distinguish frequency issues (e.g., rear-end collisions, backing incidents, parking lot fender-benders) from severity drivers (catastrophic BI, multi-vehicle events, nuclear verdict exposures). Add in FMCSA/DOT implications, driver tenure, and maintenance program notes tucked into adjuster comments—this is not just “count and sum” work.

In General Liability & Construction, the nuance multiplies. Premises-operations claims, products-completed operations losses, third-party bodily injury on jobsites, subcontractor-caused incidents, and wrap-up/OCIP or CCIP complexities all appear side-by-side. Your underwriters want to know litigation rates, claim lag (time-to-report), indemnification dynamics, and whether reserve patterns suggest emerging defect claims. A simple paid/incurred view doesn’t cut it; you need causation clarity and anomaly detection.

For Property & Homeowners, the details hide behind weather codes, CAT indicators, peril types (fire, water, theft, wind/hail), protection class, roof age, TIV growth, and sublimits or waiting periods. Frequency spikes after a hail season or water breakage in older plumbing may be buried in free-text notes or scattered across multiple files. Underwriters expect you to normalize and trend losses against exposure changes, then present a coherent story, location by location.

The result: “AI review of complex broker submission loss runs” has become a high-intent request because specialists need more than OCR—they need inference. They need a system that reads like a seasoned insurance analyst and standardizes chaos into insight.

What the Manual Process Looks Like Today

Most Broker Submission Specialists still do loss run analysis by hand—copying data into spreadsheets, building pivot tables, and scanning PDF pages for context that never survived export. It’s heroic, but it’s not scalable. Here’s what the day-to-day typically involves:

• Collecting and reconciling varied formats from prior carriers and TPAs: PDFs with scanned tables, native spreadsheets, and narrative prior carrier claims summaries.
• Normalizing columns (paid, reserve, incurred, expense, indemnity, status) and aligning claim numbering across policy years and lines of business.
• Manually categorizing cause of loss (rear-end, slip-and-fall, water damage, lightning, construction defect) and mapping inconsistent labels across files.
• Hunting through adjuster notes for litigation flags, subrogation, salvage, or large-loss drivers that aren’t captured in structured fields.
• Deriving trend views: frequency per exposure unit (per 100 vehicles, per $1M payroll/receipts, per $1M TIV), severity distributions, shock losses vs. attritional losses, and development patterns.
• Detecting anomalies and potential data quality issues: duplicated claims across carriers, mismatched loss dates, unrealized reserve development, or closed-without-payment outliers.
• Packaging findings for underwriters: executive summaries, LOB-specific highlights, and follow-up questions to the retail broker or insured.

All of this happens under time pressure. When you finally present the story, the underwriter asks for a different cut—a 3-year rolling frequency trend by state, an update to exclude subrogation recoveries, or a view net of expenses. Back to square one.

What “Good” Looks Like in Loss Run Review

Underwriters consistently ask for the same core insights, but getting there is hard without automation. For Commercial Auto, they’ll want frequency per 100 vehicles, severity over large-loss thresholds, and causal patterns like backing or nighttime incidents. For GL & Construction, they’ll want claims by operations type (premises vs. products-completed ops), subcontractor involvement, litigation rates, and lag-to-report. In Property & Homeowners, they’ll expect peril-level views (fire, water, wind/hail), CAT vs. non-CAT segmentation, and TIV-normalized loss picks. They also want defensible narratives explaining why loss experience will improve under new controls—telematics, driver training, subcontractor risk transfer, sprinkler retrofits, or roof replacements—supported by the evidence buried within the loss runs and prior carrier narratives.

“Good” is a complete, consistent story that stands up to scrutiny, with page-level citations back to the underlying documents. It’s also flexible: the ability to pivot on a dime when an underwriter requests an alternative view or an actuary needs a different normalization basis. Without automation, delivering “good” at speed is nearly impossible.

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

Doc Chat was designed for exactly this problem: complex, inconsistent documents and spreadsheets that require inference, not just extraction. It reads like a claims analyst, standardizes like a data engineer, and answers questions like your most experienced underwriter—at enterprise speed.

Ingestion at scale. Doc Chat ingests entire submission packets—loss run reports spanning five to ten policy years, prior carrier claims summaries, broker submissions, schedules of locations, statements of values, endorsements, and correspondence. It handles scanned PDFs, native Excel, mixed-format ZIPs, and email attachments with equal ease. Thousands of pages or tabs? No problem.

Normalization and mapping. The system automatically aligns heterogeneous column names and values—mapping “Total Incurred,” “Loss + ALAE,” “Indemnity Paid,” and “Expense” into your company’s standards. It unifies statuses (open/closed), cause-of-loss labels, and reserve fields across carriers. If two carriers report the same claim differently across renewal cycles, Doc Chat detects and reconciles duplicates and near-duplicates.

LOB-aware interpretation. For Commercial Auto, Doc Chat highlights collision types, BI severity patterns, DOT/FMCSA-relevant signals in notes, and garage/territory insights. For GL & Construction, it determines whether claims stem from premises-ops vs. products-completed ops, flags subcontractor-involved events, identifies wrap-up contexts, and extracts litigation indicators and lag-to-report. For Property & Homeowners, it segments CAT vs. non-CAT, breaks out peril codes (fire, water, wind/hail, theft), and correlates losses with COPE factors like roof age or protection class when available in the file set.

Real-time Q&A and interactive analysis. This isn’t static reporting. Ask Doc Chat: “Show 5-year CA frequency per 100 vehicles by state,” “List all GL claims with litigation and indemnity > $50K,” or “Which Property claims likely indicate plumbing system age issues?” You get instant answers with page-level citations and links back to source pages. You can iterate: “Now exclude closed-without-payment,” “Net out subrogation,” or “Group by cause normalized to your taxonomy.”

Explainable insight, not black boxes. For every answer, Doc Chat provides the supporting evidence—source tables, narrative excerpts from prior carrier claims summaries, and specific cells in spreadsheets. That means faster internal reviews and clean handoffs to underwriting, actuary, and claims partners.

Custom “presets” that match your playbook. We configure outputs to mirror your underwriting memos and submission templates: a standard executive summary, LOB-specific sections, frequency/severity charts, and a to-do list of clarifying questions for the retail broker. These presets are consistent across all accounts, so quality is repeatable and scalable.

Integrations and export. Push normalized outputs into your underwriting workbench, broker CRM, or data warehouse. Export spreadsheet-ready datasets for actuaries in the exact schema they expect. Plug Doc Chat into your existing intake workflow without disrupting downstream systems.

What This Looks Like Across the Big Three Lines of Business

Commercial Auto: From Fleet Chaos to Clarity

Consider a 750-unit mixed fleet operating across seven states, with ten years of loss runs from three prior carriers. Some spreadsheets are pristine; others are scanned tables embedded in PDFs. The retail broker needs a story for both admitted and E&S markets—frequency hot spots, severity outliers, and a plan to bend the curve with telematics and training.

With Doc Chat, the Broker Submission Specialist drags the entire packet into the platform. In minutes, Doc Chat produces a five-year rolling view of frequency per 100 vehicles, splits claims by collision type, highlights nighttime and urban-territory severity patterns, and identifies two repeat claimants who appeared across carrier changes. The specialist asks: “Which 10 drivers are associated with the highest severity?” and “Which terminals show the most backing incidents?” Doc Chat answers instantly, citing the exact loss run pages.

GL & Construction: Untangling Operations and Subcontractor Risk

A regional GC submits five years of GL loss runs plus narrative prior carrier claims summaries. Causes of loss are inconsistently labeled—“trip,” “fall,” “bod inj,” “prem ops”—and several claims mention subcontractor involvement but do not clearly record COI or indemnification context. The underwriter asks for litigation rate, lag-to-report distributions, and products-completed ops vs. premises-ops breakouts.

Doc Chat normalizes cause-of-loss taxonomy, tags likely subcontractor-involved incidents from narrative text, and generates a clean split of premises-ops vs. products-completed ops. It calculates average and median lag-to-report, identifies claims that escalated to litigation, and spots reserve development patterns indicating potential latent defects. With one follow-up prompt—“Exclude claims under $5K net of expense and subro”—Doc Chat recalculates the severity distribution and refreshes the executive summary.

Property & Homeowners: Peril, CAT, and COPE—Without the Busywork

An owner-operator with a multi-state habitational portfolio submits Property loss runs with sporadic peril codes, plus a statement of values (SOV) for 125 locations. The underwriter needs a clean CAT vs. non-CAT split, peril-level frequency/severity, and a TIV-normalized view. They also want location-level insights to pair with new roof schedules and planned sprinkler upgrades.

Doc Chat reconciles peril codes from multiple carriers, infers likely peril from narrative text where codes are missing, and separates CAT-coded events. It correlates losses with TIV and location characteristics, then flags buildings with clustered water-loss frequency that likely indicates aging plumbing. The Broker Submission Specialist exports a location-level dataset for the underwriter and actuary in minutes, complete with citations to the original loss runs.

Business Impact: Time, Cost, and Confidence

Doc Chat’s advantage is more than speed; it’s about delivering consistently better answers across every submission—without adding headcount or sacrificing quality. Clients routinely move from days of manual effort to minutes of automated insight. They also reduce leakage and rework because answers come with evidence, not guesswork.

• Cycle time: Reduce loss run analysis from 6–10 hours per submission to 10–20 minutes, even for packets exceeding 1,000 pages.
• Accuracy: Improve normalization accuracy and anomaly detection, reducing missed red flags (e.g., duplicated claims across carriers, hidden litigation indicators) and driving more defensible pricing.
• Capacity: Scale to handle surge volumes at month/quarter-end without overtime or temporary staff; one specialist handles 3–5x more submissions.
• Win rate: Deliver cleaner, more complete packages to underwriters faster, improving speed-to-quote and submission-to-bind conversion.
• Cost: Trim manual touchpoints and overtime; shift effort from busywork to market strategy, client communication, and negotiations.

These improvements map directly to the challenges insurers face industry-wide. As covered in our piece on AI’s Untapped Goldmine: Automating Data Entry, the seemingly mundane task of document-to-dataset conversion hides enormous ROI. And in our client story about Great American Insurance Group, Reimagining Insurance Claims Management, teams saw massive reductions in review time while improving auditability—outcomes that translate directly to loss run review and submission packaging.

Why This Isn’t Just OCR: From Extraction to Inference

Loss runs are not web pages; they are messy, multi-format artifacts of real-world operations. Traditional tools fall apart when labels and structures vary. As we argue in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, the hard part is not reading the text; it’s reasoning across inconsistent documents, applying unwritten underwriting logic, and producing the answers your team expects. Doc Chat operationalizes your playbooks, normalizes the chaos, and provides the kind of explainable inferences that manual teams struggle to deliver at scale.

In other words, “loss run report automation for underwriters” isn’t a template—it’s an intelligent system that learns how your organization defines incurred, handles expense allocations, interprets litigation clues, and treats subrogation and salvage. That’s the Doc Chat difference.

Security, Auditability, and Trust

Broker Submission Specialists deal with sensitive claim histories and identifiable details. Doc Chat is built for enterprise insurance security with SOC 2 Type 2 controls, least-privilege access, encryption at rest and in transit, and configurable data retention. Every answer includes page-level references so internal reviewers, compliance, and reinsurers can validate the analysis in seconds.

Equally important: Doc Chat never forces a decision. We keep humans in the loop. Think of the system as a high-performing junior analyst who works at machine speed and cites every source. Your expertise defines the final narrative; Doc Chat accelerates getting there.

How Nomad Data’s Doc Chat Implements Fast—Without Disrupting Your Workflow

We consistently deliver live value in 1–2 weeks. Our white-glove team learns your submission templates, loss run normalization rules, and preferred outputs. We configure presets that match your underwriting memos and build integrations with the systems you already use. Early wins come fast: drag-and-drop pilots where specialists upload loss runs and immediately get standardized outputs and Q&A. As comfort grows, we enable API feeds to underwriting workbenches and data lakes.

This “start simple, scale fast” approach mirrors what we describe in Reimagining Claims Processing Through AI Transformation: immediate productivity with no heavy IT lift, followed by light-touch integrations that fit into established workflows.

Frequently Asked Questions from Broker Submission Specialists

Does Doc Chat handle both spreadsheets and scanned PDFs?

Yes. Doc Chat ingests native Excel, CSV, and multi-tab spreadsheets, as well as scanned PDFs with embedded tables and narrative text. It reconciles them into a single, normalized view and preserves page-level traceability.

Can it detect anomalies like duplicated claims across carriers?

Yes. Doc Chat cross-references claim numbers, loss dates, amounts, and textual clues to flag likely duplicates or near-duplicates across renewal cycles and carriers—an error class that often slips through manual reviews.

How does it support different LOB nuances?

Doc Chat is LOB-aware. It applies Commercial Auto logic (frequency per 100 vehicles, collision type, BI severity), GL & Construction logic (prem-ops vs. products-completed ops, subcontractor involvement, litigation), and Property logic (peril segmentation, CAT coding, TIV normalization) using your definitions.

What about audit and compliance?

Every answer is explainable. Doc Chat links back to the exact lines, tables, and pages it used. Audit trails are time-stamped, and administrators can control retention and access policies to meet regulator and reinsurer expectations.

Can we tailor outputs to our underwriters and markets?

Absolutely. We build presets that mirror your submission packages for admitted and E&S markets—executive summaries, frequency/severity narratives, and data exports for actuaries—so every account is presented consistently.

From Document Overload to Winning Submissions

For Broker Submission Specialists, the measure of success is simple: speed, accuracy, and persuasiveness. The sooner you can distill a messy stack of loss run reports and prior carrier claims summaries into an accurate, compelling story, the more you help your markets say “yes”—and the more business your team can place. Doc Chat empowers you to do that at scale.

It’s why insurers and brokers alike are adopting AI for underwriting operations, as discussed in AI for Insurance: Real-World AI Use Cases Driving Transformation. And it’s why we built Doc Chat to be both fast and dependable: ingest entire claim files, reason through inconsistent formats, and answer questions in real time with the receipts to prove it.

A Short, Concrete Example: One Account, Three Lines

Imagine an account with all three major lines in play: a fleet-intensive distributor (Commercial Auto), premises and products exposures (GL & Construction), and a diversified property schedule (Property & Homeowners). The broker sends 12 years of mixed-format loss runs from different carriers and a 2,000-line SOV. The incumbent’s prior carrier claims summaries add helpful narrative—buried across hundreds of pages.

In a manual world, the Broker Submission Specialist would spend several days cleaning, normalizing, and reconciling, then writing the summary and creating follow-up questions. With Doc Chat, the entire dataset is analyzed in under an hour. The specialist asks a handful of questions—“What are the top five CA severity drivers?” “Which GL claims likely involve subcontractors?” “Which property locations have repeat water losses?”—and receives instant answers with citations. A submission-quality executive summary and LOB-specific data exports are produced the same day.

The kicker: when the underwriter asks for a new slice—“Exclude all CA losses under $10K and re-calc frequency per 100 vehicles by terminal”—it’s a 15-second follow-up prompt, not a complete rework.

Why Nomad Data Is the Best Choice for Loss Run Automation

Volume, complexity, and personalization define the underwriting document challenge, and Nomad Data excels at all three:

Volume. Doc Chat ingests entire claim files and submission packets—thousands of pages and tabs at a time—so you move from days to minutes. No more batching, no more cherry-picking.

Complexity. Loss runs hide critical signals in inconsistent tables and narratives. Doc Chat digs out endorsements, cause-of-loss nuance, litigation indicators, and development patterns buried in prior carrier claims summaries and broker submissions—surfacing every relevant detail.

Personalization. We train Doc Chat on your playbooks, taxonomies, and standards to deliver outputs that fit your team like a glove. Think of it as codifying your best Broker Submission Specialists and underwriters so every account benefits from their approach.

White glove service and fast implementation. Our experts configure your presets, data mappings, and integrations in 1–2 weeks. You get immediate productivity with drag-and-drop pilots and can scale to APIs when you’re ready.

Partner in AI. This isn’t one-size-fits-all software. We co-create solutions that evolve with your needs, and we stand behind them with ongoing support and measurable impact.

Getting Started

If you’re searching for “AI review of complex broker submission loss runs” or evaluating “loss run report automation for underwriters,” start with a live submission. Drag and drop real loss run reports, prior carrier claims summaries, and broker submission files into Doc Chat and see your frequency/severity insights appear in minutes—with citations back to every page. No lengthy implementation, no disruption—just better, faster submissions. Learn more or schedule a working session at Doc Chat for Insurance.