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

Underwriters know the feeling: a promising account arrives with a thick broker submission full of multi-year loss run reports, disjointed prior carrier claims summaries, and a sprawling broker submission narrative. You need to quickly determine frequency and severity trends, normalize losses by exposure, and spot anomalies that could change your pricing or appetite—yet the formats vary by carrier, years are missing, and duplicates creep in. The clock is ticking, brokers are calling, and your queue is growing.

Nomad Data’s Doc Chat changes the game for underwriting teams by automating end-to-end loss run review. Purpose-built, AI-powered agents ingest entire submission packets—hundreds or thousands of pages at a time—standardize the data, surface frequency/severity insights, calculate loss ratios and development, and flag red flags in minutes. Instead of wrestling with PDFs, the Underwriter can ask natural-language questions like, “Show 5-year GL frequency by cause of loss and jobsite” or “List the top 10 Commercial Auto claims over $100k with open reserves,” and get instant, source-cited answers—even across mixed formats and carriers.

Why loss run report automation for underwriters matters now

Submission sizes are ballooning, renewal timelines are shrinking, and competition is fierce. In today’s market, the advantage goes to carriers and MGAs that can analyze the loss story fast and accurately across Commercial Auto, General Liability & Construction, and Property & Homeowners. Doc Chat equips underwriting teams to accelerate triage, quote more confidently, and negotiate with data-backed clarity. If you’ve been searching for loss run report automation for underwriters or an AI review of complex broker submission loss runs, this is the practical, deployable solution built specifically for underwriting workflows.

The underwriter’s challenge: nuanced by line of business

Loss runs are not one-size-fits-all. The underwriting lens changes by line, and Doc Chat is designed to surface the line-specific insights that actually move pricing, appetite, and terms.

Commercial Auto: frequency, nuclear verdict risk, and operational signals

For fleet and non-fleet Commercial Auto, underwriters must understand accident frequency by vehicle class, driver tenure, and route type; severity drivers; open reserve adequacy; and the signals that correlate with outsized judgments. Submissions often include carrier loss run reports, DOT accident registers, MVR summaries, driver lists, IFTA logs, and sometimes telematics summaries. Many claims are coded inconsistently across carriers, impairing apples-to-apples analysis. Underwriters also need to connect loss history to exposure bases—vehicle count by class, miles driven, driver turnover, and geography—and to ask targeted questions like, “Which rear-end collisions involved drivers with less than 12 months tenure?” or “What’s the five-year average incurred per unit after capping losses at $250k?”

General Liability & Construction: severity tail, trade mix, and jobsite patterns

Construction GL risk hinges on the trade mix, subcontracted work, wrap-ups, and site conditions. Underwriters need to parse GL loss run reports alongside broker submissions describing class codes, OSHA 300/300A logs, project lists, contracts, and endorsements (e.g., CG 20 10, CG 20 37, waiver of subrogation, primary & noncontributory). Identifying loss drivers by cause (bodily injury, falls from heights, struck-by incidents, product/completed ops) and by project type or GC/subcontractor role is critical. Underwriters need to know, “What proportion of BI claims relate to ladder or scaffold work?” and “How do completed operations losses trend vs. premises operations over 7 years?” Nuanced insights inform terms and pricing for high-hazard trades and complex projects.

Property & Homeowners: attritional loss vs. cat, COPE gaps, and valuation concerns

On Property and Homeowners, the story revolves around attritional water/fire versus catastrophe wind/hail, COPE (Construction-Occupancy-Protection-Exposure) adequacy, roof age and type, and valuation. Submissions typically include Property loss runs, a Statement of Values (SOV), inspection reports, valuation reports, and broker narratives. Underwriters need to segment losses by peril, isolate cat-coded events, normalize by TIV and occupancy units, and detect anomalies like repeated non-weather water damage at the same building. Asking Doc Chat “Summarize 5-year water damage frequency per 100 units” or “List all open Property claims with reserves over $50k and the building protection class” gives instant clarity.

How the process is handled manually today

Even for elite underwriting teams, manual loss run analysis is slow, error-prone, and inconsistent—especially with multi-carrier, multi-year histories. Here’s how most teams still do it:

• Open dozens of PDFs for loss run reports, prior carrier claims summaries, and the wider broker submission; search for claim lists, incurred totals, and notes.
• Re-key claims into spreadsheets, fighting through inconsistent formats, carrier-specific field names, and missing policy years; normalize dates and currency.
• Manually dedupe duplicates from carrier changes, policy renumbering, or TPAs; reconcile open vs. closed status and reserve adequacy.
• Pivot data by line, policy year, location, cause of loss, and severity threshold; try to align with exposures (payroll, sales, vehicle count, TIV, unit count).
• Cap large losses, develop triangles by accident year, and compute loss ratios—with constant back-and-forth to verify what’s included where.
• Draft a written summary for underwriting files and leadership, with screenshots or references that can be hard to audit or replicate.

This approach consumes hours to days per submission, introduces human error and fatigue, and limits the number of accounts an Underwriter can quote confidently. Worse, it’s easy to miss the very anomalies—late-reported losses, reserve spikes, duplicate events—that should change price, terms, or appetite.

How Doc Chat automates loss run review end-to-end

Doc Chat brings together high‑volume ingestion, domain‑specific reasoning, real‑time Q&A, and a rigorous audit trail so underwriting teams can move from raw documents to pricing-ready insight in minutes:

• Ingests entire submission packets (hundreds or thousands of pages) including loss run reports, prior carrier claims summaries, broker submissions, SOVs, OSHA logs, inspection reports, and endorsement schedules.
• Normalizes inconsistent formats across carriers and TPAs: aligns dates, claim numbers, causes, and financials; standardizes open/closed status and incurred/reserve fields; detects and merges duplicates.
• Calculates underwriting-ready metrics: frequency and severity by cause/peril and policy year; loss ratios by line and accident year; large-loss caps; cat vs. non-cat segmentation; incurred per unit (vehicles, payroll, sales, TIV, doors).
• Surfaces red flags and anomalies: late reporting, reserve spikes before renewal, clusters by location/project, repeated non-weather water damage, litigation-prone claims, and potential fraud indicators.
• Answers natural-language questions with page-level citations: “Show 5-year GL losses over $50k for roofing operations,” or “List open CA claims with litigation or subrogation potential.”
• Exports structured outputs to your rating worksheets, submission checklists, and underwriting memos; integrates via API into policy admin, raters, or data warehouses.

Unlike generic summarization tools, Doc Chat is trained on your underwriting playbooks, appetite guides, and rating templates, so the outputs reflect your team’s standards and how you actually price risk.

What underwriters can ask—examples across lines

Doc Chat’s real-time Q&A transforms underwriting from hunting for facts to interpreting them. Sample prompts:

Commercial Auto: “Rank top 10 loss causes by incurred for power units only; show driver tenure where available.” “List all rear-end collisions over $100k incurred; include open/closed status and last reserve change date.”

General Liability & Construction: “Break out BI claims involving falls from height; show trade and jobsite where stated.” “Summarize completed operations losses by project type and policy year; cap single losses at $250k.”

Property & Homeowners: “Separate wind/hail cat losses from attritional; compute 5-year average incurred per $1M TIV.” “Show repeat non-weather water losses by building; include roof age and protection class where cited.”

Business impact: speed, accuracy, and stronger underwriting outcomes

Automating loss run analysis is not just about speed—it’s about making better, more consistent decisions while quoting more business with the same headcount.

Time savings: Clients routinely move from hours of manual review to minutes with Doc Chat. Ingest, normalize, and analyze multi-carrier, multi-year loss runs with instant metrics and a defensible audit trail.

Cost reduction: Reduce the time spent re-keying and reconciling data, cut reliance on external analysts for complex submissions, and enable underwriters to handle more quotes per month without overtime.

Accuracy improvements: AI does not fatigue; it reads page 1,500 as precisely as page 1. Doc Chat standardizes inconsistent carrier nomenclature, prevents missed duplicates, and backs every answer with a page-level citation for easy validation.

Better pricing and terms: With reliable frequency/severity segmentation, exposure normalization, and anomaly detection, underwriters price to risk—not to the document format. That yields healthier hit ratios, better loss ratios, and more consistent adherence to appetite.

Real-world underwriting scenarios powered by Doc Chat

Construction GL rollover with six prior carriers: The broker sends 7 years of GL loss runs spanning six carriers, plus OSHA logs and project lists. Doc Chat standardizes cause-of-loss codes, dedupes overlapping claims, and builds a completed-operations view by project type. The Underwriter instantly sees that 80% of severity stems from ladder and scaffold events on reroofing projects—informing pricing, terms (height restrictions), and risk engineering recommendations.

Commercial Auto fleet renewal with telematics addendum: A 400-unit mixed fleet provides prior loss runs, driver lists, and a telematics summary. Doc Chat computes rolling frequency by vehicle class, flags nuclear-verdict risk patterns, and correlates accidents with driver tenure. The team identifies a hotspot: new CDL drivers on regional routes with higher rear-end collision frequency. The quote reflects targeted driver qualification standards and revised deductibles.

Property schedule with repeated water damage: A middle-market real estate portfolio submits loss runs and a detailed SOV. Doc Chat separates cat wind/hail from attritional losses and spotlights three buildings with repeated non-weather water incidents. With an immediate view of incurred per 100 units and protection class, the Underwriter can apply per-location deductibles and require plumbing upgrades as conditions for binding.

From manual to modern: replacing spreadsheets with explainable AI

For decades, underwriting shops relied on manual re-keying and ad hoc spreadsheets—the opposite of scalable. Doc Chat introduces a new operating rhythm: the system ingests everything, produces a consistent analytical foundation, and supports transparent, source-cited reasoning. You keep control; the tool amplifies your expertise. That’s why leading carriers use Doc Chat’s explainability when collaborating with underwriting leadership, reinsurance, and compliance.

Trust, transparency, and auditability

Explainability is core to underwriting defensibility. Every Doc Chat insight links back to a page and line in a loss run report, prior carrier claims summary, or the broker submission. Audit and model validation teams can verify calculations and assumptions on the spot, making approvals faster and reducing back-and-forth. This also streamlines reinsurer dialogues: you can provide structured outputs plus the exact documentary support, without re-building analyses by hand.

Security and governance ready for insurance

Doc Chat is built for sensitive insurance data. Nomad Data maintains rigorous security practices and provides the compliance posture enterprise insurers demand. As described in our clients’ experience, secure, page‑level traceability builds confidence in AI-assisted workflows. For more on how carriers adopted explainable AI in complex document environments, see Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI.

Why Nomad Data’s Doc Chat is different

Most tools stop at extraction. Nomad goes beyond, turning unstructured documents into underwriting-ready intelligence. We customize Doc Chat to your playbooks and outputs—loss caps, development factors, exposure normalizations—so it works exactly the way your Underwriter desk does. Learn why document intelligence is more than scraping in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs and how enterprise-grade AI turns document processing into ROI in AI’s Untapped Goldmine: Automating Data Entry.

Implementation: white glove, fast, and tailored (1–2 weeks)

Underwriting teams cannot wait six months for value. Nomad’s white glove approach configures Doc Chat to your LOBs and underwriting standards in days, not quarters. Typical steps include:

Discovery and playbook alignment: We review your appetite, rating worksheets, and submission checklists by line (Commercial Auto, General Liability & Construction, Property & Homeowners). We encode how you want losses capped, developed, and normalized.

Rapid pilot: Drag-and-drop submissions directly into Doc Chat to build trust on familiar accounts. Underwriters test Q&A like “Show 3-year auto frequency by unit type” and validate the page-cited outputs.

Integration: Connect to your intake portals, underwriting workbench, or data warehouse via modern APIs. We map the structured output to your pricing models and memos.

Go live: Within 1–2 weeks, your team moves from manual compilation to conversational, auditable analytics across loss run reports and the full broker submission packet.

Key capabilities aligned to underwriting use cases

Doc Chat’s underwriting-centric feature set maps directly to loss run analysis tasks, whether you write fleets, high-hazard construction, or large property schedules:

Loss normalization: Consistent mapping of open/closed, paid/incurred, LAE, and reserve changes across carriers; duplicate detection across policy transitions and TPAs.

Exposure-aware analytics: Incurred per unit (vehicles, drivers), per $1M payroll or sales (GL), and per $1M TIV or per 100 units (Property & Homeowners). Line-specific metrics come out ready for rating.

Segmentation: Frequency/severity by cause-of-loss or peril, policy year, location, project/jobsite, or vehicle class; cat vs. non-cat splits on Property.

Anomaly detection: Repeated non-weather water losses, litigation-prone claims, reserve spikes near renewal, or clusters of similar accidents suggestive of operational issues.

Explainable Q&A: Ask “Show all GL claims tagged as completed ops over $50k incurred” and click the citation to verify in the source loss run or prior carrier summary.

Quantifying the ROI

Across underwriting desks, Doc Chat consistently delivers measurable gains:

Cycle time: Shift from half-day spreadsheet work to minutes of interactive analysis; quote earlier in the market cycle and negotiate from a position of clarity.

Throughput: Underwriters review more complex accounts without sacrificing rigor; leadership can push growth without proportional headcount increases.

Loss ratio: Better identification of risk drivers yields aligned pricing, targeted deductibles, and effective risk engineering recommendations—reducing adverse selection.

Employee experience: Senior underwriters spend less time on rote data wrangling and more time on judgment, broker relationships, and portfolio steering.

Defensibility for committees, reinsurers, and audits

When complex accounts go to referral, reinsurance, or audit, Doc Chat’s structured outputs and page-cited evidence shorten reviews and reduce second-guessing. The same consistency that speeds work at the desk level also raises confidence at the governance level—supporting portfolio management and reserving signals.

From data wrangling to underwriting judgment

Underwriting has always been a discipline of judgment built on facts. The problem wasn’t lack of insight; it was the cost and time required to extract it from messy documents. As highlighted in AI for Insurance: Real-World AI Use Cases Driving Transformation, the breakthrough is practical AI that reads like a domain expert, applies your rules, and gives you underwriter-ready answers—instantly.

Comparing Doc Chat to generic tools

Consumer-grade AI or basic OCR struggles with carrier-to-carrier variability and underwriting nuance. Doc Chat is built for insurance and tuned to underwriting. It handles inconsistent document structures, applies your loss development and capping rules, and remembers the things that matter to your team—like when to segment by project versus location, or how to treat LAE for specific programs. That’s why our clients see consistent outcomes where generic tools stall. For a deeper discussion of why this category demands a new skillset and approach, see Beyond Extraction.

Frequently asked underwriting questions Doc Chat answers

To make the AI review of complex broker submission loss runs concrete, here are common underwriting questions Doc Chat resolves in seconds—with citations:

Commercial Auto: “Total incurred and frequency per 100 units by policy year.” “Which accidents involved drivers under six months tenure?” “List open CA claims with pending litigation, last reserve update date, and current reserve.”

General Liability & Construction: “Proportion of BI claims from falls from height vs. slips/trips; show severity distributions.” “Completed ops losses over $100k by project type and subcontractor involvement.”

Property & Homeowners: “Attritional non-weather water loss frequency by building; average incurred per 100 units.” “Cat wind/hail events over $250k, with county and protection class.”

Getting started: from pilot to enterprise

Because Doc Chat is delivered as a secure, enterprise-grade platform, underwriting teams can begin with drag-and-drop pilots before integrating into core systems. In a typical pilot, we load historic submissions and loss runs, replicate your rating outputs, and compare Doc Chat’s answers with known results. This builds trust quickly and pinpoints the most valuable automations to scale. As chronicled in the GAIG experience, seeing accurate, page-cited answers on familiar files is the fastest path to adoption and impact.

The Nomad Process: your partner in underwriting AI

Doc Chat is not a one-size-fits-all widget. We partner with your underwriting leadership, portfolio analytics, and IT to tailor the solution to your lines, appetite, and templates. This includes custom extraction for line-specific documents—loss run reports, prior carrier claims summaries, broker submissions, OSHA logs, SOVs, inspection reports, and endorsement schedules—and outputs aligned to your memos and raters. White glove onboarding and a 1–2 week implementation timeline ensure you see value immediately, then compound it across your book. For a product overview, visit Doc Chat for Insurance.

Put loss run analysis on autopilot—keep judgment in your hands

Underwriters win when they can spend their time interpreting risk and negotiating the right terms—not reformatting PDFs. With Doc Chat, you can quote faster, price smarter, and defend your decisions with a transparent audit trail. The next time a complex submission lands with a mountain of loss run reports and attachments, let the AI assemble the facts while you apply the judgment.

Take the next step

If you’re exploring loss run report automation for underwriters or an AI review of complex broker submission loss runs, schedule a working session. Bring a tough submission. We’ll load it, analyze it together, and show you how minutes can replace days—without losing the nuance underwriting demands.