Supercharging Loss Run Analysis for Complex Submissions with Doc Chat – Underwriters in Commercial Auto, General Liability & Construction, and Property & Homeowners

Every underwriter knows the moment: a complex broker submission lands on your desk with a stack of loss run reports, prior carrier claims summaries, and supporting schedules. You need to quickly determine frequency and severity trends, spot anomalies, compare results to exposure growth, and decide whether the account fits appetite—without getting trapped in a week of manual cleanup and spreadsheet wrangling. This is precisely where Nomad Data’s Doc Chat for Insurance changes the game.

Doc Chat is a suite of purpose-built, AI-powered agents that ingest entire submission files—including multi-year loss runs from multiple carriers—normalize fields, reconcile inconsistencies, and surface insights in minutes. Underwriters across Commercial Auto, General Liability & Construction, and Property & Homeowners can ask natural-language questions like, “Show all open losses >$100,000 indemnity in the last 36 months by cause and location,” and get instant answers with page-level citations. If you’re searching for loss run report automation for underwriters or evaluating an AI review of complex broker submission loss runs, you’re in the right place.

Why Loss Run Analysis Is So Hard for Underwriters

Loss runs should be straightforward, but they rarely are. Formats differ across carriers, the same column can be labeled five different ways, and critical details hide inside footnotes or embedded PDFs. The result is a time-consuming, error-prone workflow that delays quoting and distracts from risk selection. Nomad Data has written extensively about why document intelligence is different from traditional web scraping—the insight underwriters need is often implied, not explicitly written on the page. See: Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

Commercial Auto Nuances

For Commercial Auto underwriters, loss runs must be reconciled with exposure metrics like unit count, annual mileage, and driver counts. Claims may be coded differently by each prior carrier—BI/PD vs. collision/comprehensive vs. third-party subrogation—and reserves fluctuate. You need to isolate patterns such as:

• Frequency per million miles and severity by cause (rear-end, sideswipe, backing, rollovers)
• Open vs. closed loss development, reserve creep and stair-stepping
• Litigation propensity and attorney representation rates
• Driver- or route-level clustering, time-of-day spikes, and weather correlations
• Catastrophic outliers and deductibles/self-insured retentions that skew severity

Underwriters also have to cross-check accident narratives in broker submissions with prior carrier claims summaries. Mismatches in dates of loss, incurred vs. paid values, or subrogation recoveries complicate accurate trending.

General Liability & Construction Nuances

In GL & Construction, loss runs include bodily injury, property damage, and often products-completed operations losses, each influenced by subcontractor usage, contractual risk transfer, and safety controls. Common analytical pain points include:

• Mapping claims to class codes, operations, or projects and separating ongoing vs. completed operations
• Identifying construction-defect patterns that may unfold over multiple policy years
• Tracing indemnity vs. ALAE, defense cost trends, and vendor contribution
• Reconciling OCIPs/CCIPs vs. corporate programs and avoiding double-counting
• Spotting anomalous frequency on specific trades (e.g., roofing, scaffolding, concrete)

Underwriters must also compare loss histories against exposure bases—payroll, receipts, subcontracted cost—and policy terms, endorsements, or exclusions across multiple carriers and years.

Property & Homeowners Nuances

Property submissions bring weather vs. non-cat distinctions, TIV growth, and a mix of theft, water damage, and fire claims. In Homeowners, frequency clustering by address or peril is essential for appetite. In Commercial Property, underwriters are looking for:

• Loss ratios by location, peril, and construction type (frame, joisted masonry, noncombustible)
• Concentration of high-severity water or electrical fire losses indicating systemic maintenance issues
• Cat vs. non-cat attribution and the impact of deductibles, sublimits, and waiting periods
• Statement of Values (SOV) consistency with loss experience and exposure growth

Across these lines, underwriters must synthesize fragmented data into a coherent risk story—fast. That’s where Doc Chat’s purpose-built document agents shine.

How Underwriters Handle Loss Runs Manually Today

Even the best underwriting teams resort to manual steps when faced with inconsistent loss run reports and broker submissions. The typical process looks like this:

1. Receive a broker submission packet combining ACORD applications (e.g., ACORD 125/126/127/140), loss run reports from multiple prior carriers, prior carrier claims summaries, exposure schedules (driver lists, SOVs), and endorsements.
2. Copy/paste or rekey loss run fields into spreadsheets, normalize column names (Date of Loss, Report Date, Cause, Paid, Reserve, Incurred, Status), and deduplicate claim numbers.
3. Build pivot tables and charts for frequency, severity, open/closed status, and top causes by policy year; adjust for endorsements, deductibles, or SIRs manually.
4. Reconcile discrepancies among carriers (e.g., different cause codes, inconsistent reserve terminology, missing subrogation) and email the broker for clarifications.
5. Calculate frequency per exposure unit (per million miles, per $1M payroll/receipts, per 100 homes, per $1M TIV), trend losses, and test for anomalies or concentration.
6. Draft a summary, annotate findings, and prepare underwriting notes for referral or pricing.

This takes hours or days per account—longer for multi-entity rollups, layered towers, or construction projects with OCIP/CCIP considerations. It introduces risk: fatigue, hidden outliers, missed exclusions, and inconsistent results from desk to desk. Nomad Data has documented these challenges and the transformational impact of AI on similar workflows in AI’s Untapped Goldmine: Automating Data Entry and Reimagining Claims Processing Through AI Transformation.

What “Loss Run Report Automation for Underwriters” Looks Like with Doc Chat

Doc Chat automates the heavy lifting so the underwriter focuses on appraisal, appetite, and strategy—not data cleanup. Here’s a typical flow for loss run report automation for underwriters across Commercial Auto, GL & Construction, and Property & Homeowners:

• Drag-and-drop ingestion at massive scale: Upload entire submission packets—multi-year loss run reports, prior carrier claims summaries, and broker submissions. Doc Chat reads thousands of pages in minutes, including scanned PDFs via OCR.
• Normalization & schema mapping: Doc Chat standardizes field names (e.g., Loss Date, Cause, Paid Indemnity, Paid ALAE, Outstanding Reserves, Total Incurred, Claim Status), reconciles carrier-specific codes, and deduplicates claims.
• Exposure connection: The agent links claims to exposure bases such as annual mileage, fleet count, payroll, receipts, or TIV from ACORD forms, SOVs, or driver/unit schedules—enabling true frequency and severity metrics.
• Anomaly detection: It flags outliers: reserve creep, unusually high defense costs, repeat addresses or vehicles, post-renewal frequency spikes, incomplete subrogation, or mismatches between summaries and detail pages.
• Peril and cause intelligence: For Property, it separates cat vs. non-cat and clusters perils (water, theft, fire). For Auto, it segments BI/PD, collision, and comprehensive patterns. For GL & Construction, it splits ongoing vs. completed operations and spotlights high-risk trades.
• Real-time Q&A across the entire submission: Ask, “List all open GL BI claims >$250k incurred with litigation indicated,” or “Show CA losses over $50k involving rear-end collisions between 10 p.m. and 5 a.m.” Doc Chat answers instantly and links back to the exact page for verification.
• Custom summaries and exports: Generate underwriting-ready summaries that match your template, plus exportable tables (CSV/Excel) for pricing and referrals.
• Audit-ready citations: Every metric traces to source pages, supporting defensible decisions in audits or peer review.

These capabilities echo Nomad’s results in other high-volume file reviews, as outlined in The End of Medical File Review Bottlenecks and the GAIG webinar recap Reimagining Insurance Claims Management, where days of manual review were reduced to minutes.

AI Review of Complex Broker Submission Loss Runs—From Messy PDFs to Portfolio-Ready Insight

When a broker sends six years of loss runs from three different carriers—some exported to Excel, others scanned as images—Doc Chat doesn’t miss a beat. It uses AI to capture context, not just keywords, which matters because loss logic often lives between the lines: in a footnote marking an unusually high deductible that changed mid-term, or in a narrative about faulty plumbing upgrades that post-date the last major water loss. For a deep dive into why that difference matters, read Beyond Extraction.

Here’s how an AI review of complex broker submission loss runs typically unfolds:

• Document triage: Separate loss runs from ACORD apps, SOVs, safety manuals, driver lists, contracts, endorsements, and correspondence in the submission.
• Field alignment: Map “Paid” vs. “Paid Indemnity,” “Exp” vs. “Paid ALAE,” “Outstanding” vs. “Reserves,” and standardized “Total Incurred.”
• Timeline building: Organize claims by policy period and date of loss; link related claims (e.g., re-openings or supplements) and identify claim-number changes across carriers.
• Exposure normalization: Calculate frequency per exposure unit—per million miles, per $1M payroll/receipts, per 100 homes, per $1M TIV—and trend results.
• Peril/cause harmonization: Normalize cause codes and cluster similar causes across carriers for consistent ranking.
• Exception surfacing: Highlight extraordinary incurred deltas, reserve stair-stepping, recurring loss locations, or units with multiple events.
• Underwriting narrative: Produce a crisp summary that tells the risk story by line of business, calling out drivers, controls, and recommended follow-up questions for the broker or insured.

Live Questions Underwriters Can Ask Doc Chat

Underwriters in Commercial Auto, GL & Construction, and Property & Homeowners can transform their workflow by asking Doc Chat to answer questions instantly across loss runs, prior carrier claims summaries, and broker submissions:

• “List all open Commercial Auto losses over $100,000 incurred in the last 36 months. Show cause, ALAE split, and driver if available.”
• “Calculate frequency per million miles for each policy year and compare to industry benchmark; flag any year >150% of benchmark.”
• “For GL, separate ongoing vs. completed ops claims and highlight projects with repeat BI losses; identify any reserved claims with defense > indemnity.”
• “In the SOV, identify addresses showing multiple water losses in Property and estimate expected severity given current TIV and deductible.”
• “Summarize the top five causes of loss by incurred dollars across all carriers; normalize cause labels and indicate any outlier events.”
• “Show all HO losses in coastal ZIP codes with wind/hail cause and note deductibles and waiting periods applied.”

Every result includes citations back to source pages, preserving trust and enabling quick validation.

Business Impact: Time Savings, Cost Reduction, and Accuracy Improvements

Across underwriting teams, the bottleneck isn’t judgement—it’s time. Doc Chat removes the document-review backlog so underwriters can assess risk, ask better questions, and move to pricing and strategy. Nomad’s clients regularly report that tasks previously requiring hours or days now take minutes. Similar speed and quality gains are documented in GAIG’s workflow transformation and in our overview of AI for Insurance.

With loss run analysis specifically, the impact shows up in four measurable dimensions:

• Cycle time: Multi-year, multi-carrier loss normalization and analysis can compress from days to under an hour—even for construction rollups or multi-location property schedules.
• Expense: Fewer manual touchpoints, reduced overtime during renewal spikes, and less reliance on external contractors for data cleanup.
• Accuracy: Consistent extraction of paid, reserves, and ALAE; standardized cause coding; automatic anomaly surfacing (reserve creep, repeat locations, litigation flags).
• Scalability: Handle peak submission seasons without adding headcount; expand appetite exploration because document review no longer constrains throughput.

Just as importantly, underwriters spend more time on the work that matters: engaging brokers, refining coverage structure, aligning endorsements with exposure, and elevating risk selection.

How the Process Is Handled Manually Today vs. With Doc Chat

To make the contrast concrete, consider a complex GL & Construction account with five years of loss history across three carriers, plus an OCIP project:

Manual: An underwriter or analyst spends hours aligning columns, reconciling paid/reserve terminology, and mapping losses to policy years. They build pivot tables, calculate frequency per $1M payroll, and draft a summary; meanwhile, clarifying questions go to the broker, slowing the quote. Each new document version restarts portions of this process.

With Doc Chat: The underwriter drags and drops the entire submission—including ACORD 125/126, loss run PDFs, prior carrier claims summaries, subcontractor lists, and endorsements. Doc Chat creates a clean, normalized dataset, calculates exposure-adjusted frequency/severity, highlights anomalies (e.g., repeat falls-from-height on two projects), and outputs a summary aligned to the team’s template—complete with source citations. The underwriter poses a few targeted questions, confirms assumptions, and moves to pricing.

Why Nomad Data Is the Best Solution for Underwriting Teams

Nomad Data’s Doc Chat isn’t a generic summarizer. It’s a purpose-built, enterprise-grade suite of agents designed for insurance. What sets it apart for underwriters:

• Volume at underwriting speed: Ingest entire submission packets—thousands of pages—without adding headcount. Reviews move from days to minutes.
• Complexity without compromise: Doc Chat digs through messy, inconsistent loss runs to surface exclusions, endorsements, and trigger language hidden in PDFs and attachments—critical for accurate coverage design.
• The Nomad Process: We train Doc Chat on your underwriting playbooks, appetite guides, and summary templates, delivering a personalized solution that fits your desk’s workflow.
• Real-time Q&A: Ask plain-language questions, get precise answers, and click to the source page—satisfying both underwriting curiosity and compliance needs.
• Thorough & complete: No skipped pages, no fatigue. Doc Chat surfaces every reference to coverage, liability, and damages across the submission, eliminating blind spots.

Beyond technology, you’re gaining a strategic partner. Our white-glove team helps encode your best practices and unwritten rules into Doc Chat so your entire organization benefits from the expertise of your top underwriters.

Implementation: White-Glove Service in 1–2 Weeks

Underwriting teams don’t have time for year-long IT projects. Doc Chat is designed for quick wins:

• Day 0–2: Discovery sessions to capture your underwriting templates, loss run fields, appetite rules, and summary formats by line of business.
• Day 3–7: Pilot on real submissions; tune normalization, cause mapping, and exposure linkages; validate outputs against known accounts.
• Day 8–14: Roll out to a broader underwriting cohort with SSO, optional system integrations, and tailored prompts/playbooks for Commercial Auto, GL & Construction, and Property & Homeowners.

Underwriters can start with drag-and-drop uploads on day one and layer in integrations over time. This phased approach mirrors the adoption playbook highlighted in our GAIG case recap: Great American Insurance Group Accelerates Complex Claims with AI.

Security, Governance, and Auditability

Submission files include sensitive insured, claimant, and financial information. Nomad Data is SOC 2 Type 2 compliant, and Doc Chat provides defensible, page-level citations for every answer—reassuring audit, compliance, and reinsurance partners. As discussed in our piece on Automating Data Entry, enterprise-grade controls and clear provenance are table stakes. We meet them out of the box.

Underwriting Use Cases by Line of Business

Commercial Auto

Doc Chat extracts unit counts, mileage, and driver lists from ACORDs and broker submissions, links them to loss runs, and computes:

• Frequency per million miles by policy year and route
• Severity distribution by cause (rear-end, backing, rollover), vehicle type, and time of day
• Open vs. closed loss development and reserve stair-stepping
• Litigation flags and attorney representation rates

Real-world example: a fleet with flat unit count but rising frequency per million miles in late-night deliveries. Doc Chat surfaced the trend and linked it to route expansion, guiding underwriting questions and risk control recommendations.

General Liability & Construction

From ACORD 126, subcontractor schedules, and prior carrier summaries, Doc Chat maps losses to trades and project types and isolates:

• Ongoing vs. completed operations claims
• Trades with disproportionate BI frequency (e.g., roofing, scaffolding)
• Defense-heavy claims that suggest litigation posture
• Cross-year construction defect patterns

A construction insured with low overall frequency but repeat falls-from-height on two projects triggered anomaly alerts. The underwriter engaged risk engineering and adjusted coverage structure accordingly.

Property & Homeowners

Using SOVs, ACORD 140, and loss runs, Doc Chat breaks down cat vs. non-cat, flags repeat addresses, and correlates TIV growth to severity:

• Water loss clustering at specific buildings or HO communities
• Fire and electrical claims indicating systemic maintenance issues
• Wind/hail losses in coastal ZIP codes and deductible effectiveness
• Exposure-adjusted frequency per $1M TIV with trend analysis

For a multi-location schedule, Doc Chat highlighted three addresses with repeat non-cat water losses and ALAE outweighing indemnity, informing a targeted inspection plan.

From Document Review to Decision Support

Underwriters are not data clerks. They are risk experts. Doc Chat moves teams beyond document processing into decision support:

• Appetite triage: Instantly see whether loss patterns fit appetite, where exclusions or endorsements may be necessary, and whether additional controls are warranted.
• Pricing inputs: Export clean, normalized loss data to rating worksheets; segment frequency/severity and remove cat outliers when appropriate.
• Broker dialogue: Engage brokers with precise questions grounded in source-cited findings, speeding clarifications and building credibility.

As we emphasize in Reimagining Claims Processing Through AI Transformation, AI should augment, not replace, expert judgment. Underwriters remain firmly in control; Doc Chat simply ensures no important fact is missed.

Examples of Questions That Instantly Advance an Underwriting File

Across the three lines of business, these Doc Chat prompts routinely surface underwriting-critical insights:

• “Reconcile total incurred by policy year across all carriers; identify any year with >30% jump after exposure normalization.”
• “Which GL projects have repeat BI claims? Show cause categories and defense share.”
• “In Property, list addresses with 3+ losses; classify water vs. non-water and recommend next steps.”
• “For the fleet, rank terminals by frequency per million miles; highlight any with after-hours spikes.”
• “Identify all losses potentially impacted by an endorsement excluding subcontractor work; list carrier, policy year, and incurred.”
• “Create a loss summary that matches our underwriting template with frequency/severity visuals and a broker Q&A section.”

Quantifying ROI for Underwriting Teams

Insurers repeatedly see dramatic ROI once loss run analysis is automated:

• Throughput lift: One underwriter can process 3–5x more complex submissions per week, especially during renewal surges.
• Leakage reduction: Fewer missed anomalies or trends; better alignment of coverage and endorsements with actual exposure.
• Talent retention: Underwriters spend less time cleaning data and more time analyzing risk, improving engagement and reducing burnout.
• Faster, better broker response: Shorter cycles and more precise questions increase win rates on desirable accounts.

These outcomes mirror transformations seen in other document-heavy insurance workflows documented in AI for Insurance: Real-World Use Cases Driving Transformation.

Addressing Common Concerns

“Will AI hallucinate numbers?” Doc Chat grounds every answer in your documents and returns page-level citations. For structured extraction tasks like loss runs, hallucination risk is minimized by design and governed by strict validation rules.

“What about data security?” Nomad Data adheres to enterprise-grade controls and SOC 2 Type 2 standards. Your data remains your data.

“How quickly can we start?” Most underwriting teams begin live use within 1–2 weeks, with drag-and-drop onboarding and optional integrations as trust grows.

From Pilot to Standard Practice—A Pragmatic Path

We recommend a short pilot focused on your heaviest pain points: late-season renewals, multi-carrier rollups, or construction submissions with sprawling attachments. In a matter of days, you’ll see loss run normalization, anomaly detection, and underwriting summaries running end-to-end—no long IT queue required.

Ready to see loss run report automation for underwriters in action? Explore Doc Chat for Insurance and bring underwriting-grade AI to your next complex submission.