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

Underwriters are drowning in documentation. Complex broker submissions for Commercial Auto, General Liability & Construction, and Property & Homeowners routinely arrive with years of loss run reports, fragmented prior carrier claims summaries, and sprawling broker submissions that span hundreds or even thousands of pages. Within those pages hide the insights that drive pricing, retention, deductibles, sublimits, and appetite decisions: loss frequency, severity, trending, reserving practices, open vs. closed histories, litigation propensity, and anomalous patterns. The challenge isn’t a lack of data—it’s the time and precision required to extract meaning from it before quotes are due.

Nomad Data’s Doc Chat for Insurance turns that bottleneck into advantage. Purpose-built AI agents ingest entire submission files—thousands of pages at a time—normalize inconsistent formats across carriers, cross-check details, and deliver on-demand, source-cited answers to the exact questions an Underwriter needs to ask. Instead of spending days reconciling PDF loss runs and spreadsheet exports, you ask: “Show loss frequency and severity by cause for the most recent 5 policy years, flagging any claim above $100,000 including current case reserves,” and get an immediate, defensible response with page-level citations. It’s loss run report automation for underwriters, tailored to your playbook, appetite, and rating philosophy.

The Underwriter’s Loss Run Problem: Three Lines of Business, One Core Challenge

Across Commercial Auto, General Liability & Construction, and Property & Homeowners, the Underwriter must transform historical loss performance into a forward-looking view of risk. Yet loss runs are often inconsistent in structure, terminology, and completeness—even within the same account from different prior carriers. Broker cover letters occasionally provide summaries, but Underwriters still need to verify every number, cause of loss, reserve status, and corrective action narrative. And in competitive markets, you must do it faster than ever without sacrificing underwriting discipline.

Commercial Auto: Frequency-driven, severity-spiked, and format-fragmented

Commercial Auto submissions often include multi-year loss run reports by vehicle, garage location, and driver, plus driver lists, vehicle schedules (VIN, year, make, model), MVR summaries, DOT inspection histories, and sometimes telematics excerpts. The Underwriter needs quick clarity on:

• Loss frequency by exposure (e.g., delivery routes vs. long haul) and driver tenure
• Severity drivers (bodily injury, third-party property damage, nuclear verdict exposures)
• Clusters of at-fault collisions, time-of-day patterns, and weather-related frequency
• Large-loss development and adequacy of case reserves vs. ultimate loss expectations
• Safety changes implemented after high-loss periods (e.g., driver coaching, telematics intervention)

But CA loss runs frequently vary by carrier template, group losses differently (by claim number or incident), and may omit crucial context (open claim litigation status, subrogation potential). Normalizing that variability manually is error-prone and slow.

General Liability & Construction: Contract nuance meets casualty volatility

For GL & Construction, Underwriters reconcile loss run reports against contracts, COIs, subcontractor agreements, and endorsements (e.g., CG 20 10, CG 20 37, primary and noncontributory wording). The key is isolating loss patterns by job type, trade, site controls, and severity drivers (products-completed operations, premises liability, third-party bodily injury, construction defect). You need to understand:

• Frequency by trade or job class, including wrap-up vs. non-wrap exposures
• Severity spikes linked to specific project types or jurisdictions
• Defense and indemnity mixes, litigation probabilities, and tail exposures
• Additional insured implications, contractual risk transfer effectiveness
• OSHA 300/300A log references, site safety audits, and root-cause corrective actions

Loss runs often split accident dates, report dates, and close dates inconsistently; reserves for construction defects evolve over years. Manually reconciling development triangles and pinpointing trades or GCs driving the tail eats underwriting time.

Property & Homeowners: Cat signals, COPE data, and unreliable narratives

Property submissions blend multi-year loss history with Statements of Values (SOVs), COPE data, inspections, and valuations. Underwriters must correlate losses to construction, occupancy, protection, and exposure attributes; differentiate attritional vs. catastrophe; and scrutinize whether mitigation measures corrected loss causes. Core questions include:

• Per-location and per-peril frequency/severity (fire, water, hail, wind/hurricane, theft)
• Cat vs. non-cat delineation and recoveries (subrogation, salvage)
• Impact of roof age, sprinkler impairments, and water-leak detection programs
• Whether outlier locations drive a disproportionate share of paid losses and reserves
• Loss adjustment trends and any signal of underinsurance or valuation drift

Property loss runs can under-specify peril detail or aggregate by occurrence without clear peril tagging. Aligning loss history with SOVs, construction class, and cat modeling outputs becomes a manual scavenger hunt—often under tight quote timelines.

How the Process Is Handled Manually Today

Most Underwriters and risk analysts tackle loss runs and prior carrier claims summaries with a combination of spreadsheets, manual copy-paste, and painstaking note-taking across PDFs and emails. Even with great discipline, the process is slow and fragile. A typical manual workflow looks like this:

Manual loss run review steps that drain underwriting capacity:

• Collect and de-duplicate loss run reports and prior carrier claims summaries across all years, lines, and entities.
• Standardize field names (claim number, cause of loss, reserve, paid, ALAE, DCC, open/closed status) across varying carrier formats and missing headers.
• Recreate pivot tables to calculate accident-year and policy-year frequency, severity, and development by cause, location, vehicle, driver, or trade.
• Manually correlate claim narratives to assess subrogation, litigation status, and root causes—often buried in scanned PDFs and correspondence.
• Cross-check broker-provided summaries against source loss runs; reconcile discrepancies and request clarifications, frequently multiple times.
• Extract details from broader broker submissions (ACORD forms, SOVs, driver lists, vehicle schedules, COIs, endorsements) to align exposure with historical losses.
• Compile a final underwriting summary, with referenced page numbers and screenshots for auditability, then push figures into rating workbooks.

Even the best teams face inconsistencies, fatigue, and versioning risks. Underwriting leaders see throughput bottlenecks, missed opportunities, and quote declines on otherwise attractive accounts simply because the loss analysis cannot be completed in time.

AI Review of Complex Broker Submission Loss Runs: How Doc Chat Automates End-to-End

Doc Chat changes the game by ingesting the entire submission—loss run reports, prior carrier claims summaries, broker submissions, ACORDs, endorsements, SOVs, driver lists, vehicle schedules, inspection reports—and performing a consistent, rules-driven analysis aligned with your underwriting playbook. This is not generic OCR. It’s a suite of insurance-trained agents that understand exclusions, endorsements, and trigger language; compute frequency and severity; and surface anomalies with page-cited evidence you can defend to underwriters, managers, reinsurers, and auditors.

What Doc Chat delivers out of the box for loss run report automation for underwriters:

• Normalization across carriers and formats: aligns field names, dates, paid vs. reserve, ALAE/DCC, open/closed status, cause of loss, and peril categories.
• Instant summaries: policy-year and accident-year frequency/severity, loss triangles, trend lines, and loss picks by cause, peril, location, driver, or trade.
• Cross-document validation: reconciles broker summaries with underlying loss runs; flags discrepancies and missing years, entities, or locations.
• Anomaly and pattern detection: identifies outlier losses, repeated claimants, repeat locations, serial drivers, and abnormal reserve development.
• Real-time Q&A: ask “Which GL losses over $250K relate to products-completed ops?” or “List Property water losses last 5 years by building, tagging those with repeated leak incidents.”
• Audit-ready citations: every answer includes the exact page reference from the loss run or supporting document.

Doc Chat scales to entire claim files and submission packets that would overwhelm manual teams. The result: comprehensive, consistent loss analyses in minutes. For background on why this level of inference goes far beyond simple PDF scraping, see Nomad’s perspective in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

What This Looks Like in the Underwriter’s Day-to-Day

Doc Chat becomes the underwriting co-pilot at the exact moment documentation lands. You drag-and-drop the packet or route it via API. The AI instantly classifies documents, extracts key metrics, and presents a structured summary aligned to your rating models for Commercial Auto, General Liability & Construction, and Property & Homeowners. Need to audit? Every metric is clickable back to its source page.

Sample prompts Underwriters use in the app:

• “Summarize loss frequency and severity for Commercial Auto, last 5 accident years, split by at-fault vs. not-at-fault, and surface drivers with 2+ at-fault losses in any 24-month window.”
• “For GL & Construction, list all open claims over $100K with defense spend over $25K and show the project type and trade where the accident occurred.”
• “For Property, break out water vs. wind vs. fire losses, per location, with paid and reserve totals and note any repeated cause patterns and remedial actions mentioned.”
• “Compare broker’s loss summary table to underlying loss runs. Highlight mismatches and missing years.”

These are the kinds of context-rich queries that typical document tools miss. Doc Chat’s real-time Q&A lets you interrogate the file like a seasoned analyst who has already read every page. For a live carrier perspective on cutting review time from days to minutes while retaining page-level explainability, see Reimagining Insurance Claims Management: Great American Insurance Group Accelerates Complex Claims with AI.

The Business Impact: Faster Quotes, Stronger Discipline, Lower Cost

Loss run analysis is a gating step. When it drags, everything drags. With Doc Chat, underwriting teams triage and evaluate complex accounts at scale without adding headcount. The impact concentrates in four areas: speed, cost, accuracy, and empowerment.

Speed: Because Doc Chat ingests thousands of pages at a time, Underwriters move from first look to actionable loss metrics in minutes. That means faster go/no-go calls, more timely declinations when history misaligns with appetite, and the ability to engage brokers with smart questions on the same day a submission arrives. Teams that once reviewed a fraction of opportunities can now review them all.

Cost: Manual hours spent cleaning, consolidating, and recalculating loss runs shrink dramatically. According to Nomad’s experience, intelligent document processing often delivers material ROI in the first year as data entry-style tasks are automated. For more, explore AI’s Untapped Goldmine: Automating Data Entry, which details how organizations recapture significant time and budget when repetitive extraction work is automated.

Accuracy: Human accuracy degrades with page volume and fatigue; Doc Chat reads page 1 and page 1,500 with the same rigor. The platform provides consistent extraction of critical fields—paid, reserves, ALAE, cause, peril—and highlights inconsistencies that often lead to bad pricing or unnecessary referrals. See how quality improves alongside speed in Reimagining Claims Processing Through AI Transformation.

Empowerment: Underwriters and leaders gain transparency with page-level citations, standardized outputs, and a repeatable process that institutionalizes best practices across desks, offices, and new hires. The outcome is a stronger underwriting culture: faster, smarter, and more defensible.

Why Nomad Data Is the Best Solution for Underwriters

Doc Chat is purpose-built for insurance, not a generic summarizer. It ingests entire submission files—ACORDs, endorsements, loss run reports, prior carrier claims summaries, broker submissions, SOVs, driver lists, vehicle schedules, valuations—and trains on your underwriting playbook so it mirrors your appetite, escalation thresholds, and rating inputs.

What sets Nomad Data apart:

• The Nomad Process: We codify your unwritten rules into the system so outputs reflect your team’s standards and workflows.
• Real-time Q&A + auditability: Ask complex questions and get page-cited answers across massive document sets.
• Depth and completeness: The AI surfaces every relevant reference to coverage, liability, damages, and costs—eliminating blind spots.
• White-glove service: From discovery to deployment, our experts walk with your Underwriting and IT teams. Typical implementations finish in 1–2 weeks, not months.
• Security: Enterprise-grade security and governance, including SOC 2 Type 2 controls, align with carrier and MGA/wholesaler requirements.

Most carriers don’t want to build and maintain this infrastructure themselves. Doc Chat is an expert, custom-built solution that plugs into current systems and starts providing value immediately. To understand the discipline behind complex insurance document automation—and why it unlocks a step-change beyond simple extraction—read Beyond Extraction.

Use-Case Deep Dive by Line of Business

Commercial Auto

Documents: Multi-year loss run reports, prior carrier summaries, driver lists, vehicle schedules (VIN, year, make), MVR summaries, DOT inspections, telematics snapshots in broker submissions.

Doc Chat delivers: Frequency/severity by driver, vehicle class, route type; at-fault vs. not-at-fault splits; repeated claimant and repeat location patterns; large-loss development; pending litigation for open claims; reserve adequacy signals; and safety program impact analysis from narratives. The system also highlights driver clusters with repeated at-fault losses and correlates time-of-day and weather patterns where mentioned.

Underwriting impact: Faster segmentation of fleet types; confident decisions on deductibles, retentions, and collateral; clearer appetite calls on distressed fleets where loss histories are noisy; and better negotiation posture with brokers due to page-cited, data-driven insights.

General Liability & Construction

Documents: Loss run reports, prior carrier claims summaries, subcontractor agreements, COIs, endorsements (CG 20 10, CG 20 37), site safety manuals, OSHA 300/300A logs, project lists.

Doc Chat delivers: Root-cause clustering by trade and project type; products-completed ops severity patterns; defense/indemnity profiling; litigation propensity by jurisdiction; reserved but unpaid tails; and anomalies between broker summaries and carrier loss runs. It flags gaps in contractual risk transfer and connects claims to additional insured language when cited in narratives.

Underwriting impact: Better pricing for complex projects, more disciplined appetite enforcement, identification of trades driving outsized losses, and a rapid path from submission to quote—particularly when timelines are aggressive and data is inconsistent.

Property & Homeowners

Documents: Loss run reports, SOVs, valuations, inspection reports, prior carrier claim summaries, catastrophe modeling outputs, mitigation reports, broker cover letters.

Doc Chat delivers: Peril-tagged frequency/severity by location; cat vs. non-cat delineation; repeated water losses and roof-related claims; reserve development for large fires; and correlation between losses and COPE attributes. It flags outlier buildings that account for a disproportionate share of losses and highlights mitigation actions (sprinklers, roof replacements, leak detection) documented in narratives.

Underwriting impact: Clearer appetite calls on portfolios with mixed performance, tight alignment to SOV and valuation assumptions, and better substantiation for rate, deductible, and sublimit decisions in cat-exposed areas.

From First Notice to Final Summary: Where Loss Runs Meet the Rest of the File

Underwriters don’t evaluate loss runs in isolation. Doc Chat cross-references related submission documents—ACORD apps, SOVs, COIs, endorsements, inspection findings—and where available, ISO loss history or prior FNOL data included in broker submissions. The agent traces each insight to its supporting evidence and exposes gaps: missing policy years, absent locations, or incomplete schedules. When it detects inconsistencies, it drafts a broker clarification list so you can request exactly what’s needed on the first pass.

Implementation Without Disruption

Doc Chat is designed for rapid adoption and measurable outcomes. Many underwriting teams start in a no-integration mode—drag-and-drop files directly into the platform. As usage grows, IT teams connect Doc Chat via API to intake portals, underwriting workbenches, DMS, or data lakes. Because the solution is trained on your documents and underwriting standards, the outputs are immediately usable and aligned to your rating models.

Timeline: White-glove onboarding typically completes in 1–2 weeks. That includes playbook capture, output template design (e.g., frequency/severity views, loss triangles, per-LOB summaries), security review, and pilot go-live. As trust builds, organizations add automation (auto-triage, broker discrepancy letters, rating workbook pre-fill) and portfolio-wide reviews.

Governance, Security, and Explainability

Insurance organizations must uphold strict governance. Doc Chat provides page-level citations for every answer and every computed metric, enabling auditors, reinsurers, and regulators to verify conclusions quickly. The platform is built with enterprise security as a foundation, including SOC 2 Type 2 controls, role-based access, and clear data residency configurations. For perspective on how transparent, explainable AI changed the rhythm of claims document review—and why that matters equally for underwriting—see the GAIG story: Great American Insurance Group Accelerates Complex Claims with AI.

Key Performance Indicators: What Good Looks Like

Underwriting leaders typically track transformation through:

• Cycle time from submission receipt to quoted/declined for complex accounts
• Percentage of submissions fully loss-analyzed before declination
• Discrepancy rate between broker summaries and verified loss runs
• Percentage of quotes delivered with page-cited loss support
• Underwriter hours per account for loss analysis, pre- vs. post-Doc Chat
• Close rates and hit ratios conditioned on loss quality tiers

As routine extraction work disappears, teams reallocate time to risk selection, broker strategy, and portfolio steering. The net effect is more quotes on the right risks—and fewer quotes on the wrong ones.

Practical Examples: AI Review of Complex Broker Submission Loss Runs

Example 1 — Commercial Auto fleet with mixed performance: A multi-state delivery fleet submits five years of loss run reports across three carriers. Doc Chat normalizes formats, identifies repeated at-fault losses among seven drivers, correlates incidents to night shifts and winter months, and flags open BI claims with reserve inadequacy risk. The Underwriter uses the page-cited summary to recommend driver remediation and pricing aligned to exposure reality—delivering a fast, defensible quote.

Example 2 — GL & Construction specialty contractor: A structural steel contractor’s prior carrier claims summaries show scattered high-severity losses. Doc Chat clusters causes to erection work on high-rise projects, highlights jurisdictions with elevated defense costs, and reveals gaps in additional insured language for certain subs. The Underwriter structures rate, deductible, and AI endorsements accordingly and documents appetite exceptions with citations.

Example 3 — Property schedule with water-loss clusters: A mixed-use portfolio provides an SOV and multi-year loss history. Doc Chat isolates repeated water damage claims in pre-war buildings, tags those with outdated plumbing, and notes broker-stated mitigation not yet reflected in inspection reports. The Underwriter adjusts deductibles and requires specified remediation as a binding condition, referencing the exact loss pages and building attributes.

From Loss Insight to Rating Workbook

Doc Chat can output structured fields that feed rating tools directly: incurred-by-year, paid/reserve splits, ALAE, cause/peril tags, and frequency/severity aggregates by exposure unit (vehicle, location, trade). That removes error-prone rekeying and ensures that the numbers a broker sees on the quote match the numbers tied to source pages. Many teams begin with manual uploads and move to automated pre-fill in phase two.

Change Management: Keeping Underwriters in the Driver’s Seat

Doc Chat is an assistant, not a decider. Underwriters stay in control of selection, terms, and pricing. The system does the rote reading, calculating, and cross-checking, then answers questions with defensible citations. That model supports faster onboarding of new team members and reduces variability across desks. For broader context on how AI eliminates document bottlenecks while improving the quality of human decisions, see The End of Medical File Review Bottlenecks.

Frequently Asked Questions

Does Doc Chat support multi-entity and multi-carrier loss runs?
Yes. It normalizes field names, aligns date conventions, and consolidates multi-entity histories with clear labeling and auditability.

Can we enforce our underwriting formats and thresholds?
Absolutely. Doc Chat is trained on your underwriting playbook—thresholds for large losses, reserve adequacy tests, catastrophe definitions, and appetite rules are all configurable.

How does Doc Chat handle missing documents?
It flags missing policy years, absent locations, or incomplete schedules, and can generate a broker clarification list automatically.

What about security and compliance?
Doc Chat is built for enterprise security (including SOC 2 Type 2) and provides page-level explainability so regulators, reinsurers, and auditors can verify any output.

How fast can we get started?
Most underwriting teams go live in 1–2 weeks with white-glove onboarding. Many begin in a no-integration mode and add APIs later.

Why Now: Turning Documentation Volume into Underwriting Advantage

Loss runs will only get longer and more variable. Broker submissions will continue to include mixed-quality summaries, scanned documents, and inconsistent spreadsheets. The carriers and MGAs who win will be the ones who process more, faster—without compromising underwriting rigor. That’s the promise of Doc Chat: an AI partner that scales your best practices, preserves your judgment, and gives you a durable edge in competitive markets.

Next Steps

If your team is exploring loss run report automation for underwriters or evaluating options for AI review of complex broker submission loss runs, see how Doc Chat performs with your actual submission files. In just a few days, you can move from proof-of-value to production, with outputs that map directly to your rating and governance needs.

Learn more about Doc Chat for Insurance and schedule a pilot with your current pipeline of Commercial Auto, General Liability & Construction, and Property & Homeowners accounts.