Supercharging Loss Run Analysis for Complex Submissions with Doc Chat – Built for the Risk Analyst Across Commercial Auto, GL & Construction, and Property/Homeowners

Every Risk Analyst knows the feeling: a massive broker submission lands with sprawling loss run reports, prior carrier claims summaries, policy schedules, and emails stitched together into a single PDF. The clock starts for pricing, reserving, and selection decisions—but the volume and inconsistency of the documentation grind the process to a halt. Across Commercial Auto, General Liability & Construction, and Property & Homeowners, the pressure to detect frequency, severity, and anomalous loss patterns is higher than ever. Yet manual review still dominates.

Nomad Data’s Doc Chat changes the equation. Purpose-built for insurance, Doc Chat’s AI-powered agents ingest entire submission packages—loss run reports, prior carrier claims summaries, FNOL forms, ISO claim reports, police accident reports, claim notes, endorsement lists, and more—then deliver instant answers. Ask for five-year loss triangles, litigation rates, sudden reserve spikes, or driver-level frequency, and get source-cited results in seconds. This is loss run report automation for underwriters and risk analysts designed for real-world complexity.

Why Loss Runs Are So Hard: The Risk Analyst’s Cross-LOB Reality

Risk Analysts don’t just read rows of claims—they interpret context across lines of business, coverage structures, and time. Loss run reports vary wildly by carrier and TPA, with field names that shift across pages, policy periods that overlap, and claim statuses that are inconsistent or out-of-date. Submissions often include scanned PDFs, spreadsheets exported to PDF, and email screenshots. In the same file you might find paid/incurred totals summarized at the top, only to discover late reserves hidden in an appendix. Detecting true frequency and severity, isolating catastrophic versus attritional loss, and flagging anomalies quickly is a tall order.

Underwriting appetites demand clarity at speed. Whether it’s a fleet of 1,000 vehicles (Commercial Auto), a multi-state GC with dozens of subcontractors (GL & Construction), or a property roll-up with high TIV and mixed cat exposure (Property & Homeowners), leadership expects a clean story: What’s driving loss cost? Where are the hotspots? Can we price, exclude, sublimit, or decline? Without automation, this becomes a game of time triage and best-guess sampling—a risky basis for quoting complex accounts.

Line-of-Business Nuances the Risk Analyst Must Reconcile

Commercial Auto

Commercial Auto loss runs must be tied back to driver rosters, vehicle schedules, DOT numbers, MVR results, and police accident reports. At-fault indicators are sometimes missing or implied, bodily injury severity may be separated from med pay, and litigation indicators may only appear in adjuster notes. The Risk Analyst must reconcile mileage and radius, identify nuclear verdict risk factors, and separate catastrophic events from everyday fender-benders. Frequency trends can be masked by fleet growth; severity can be distorted by a single outlier year.

General Liability & Construction

GL & Construction demands site/project-level mapping. A fall-from-height claim at Project A should not be blended with a struck-by incident at Project B, yet many loss run reports do not consistently label location or subcontractor involvement. Wrap-ups (OCIP/CCIP) complicate who pays for what. Contractual risk transfer, COI compliance, and indemnification language live outside the loss runs and inside contract documents and endorsement schedules. The Risk Analyst must distinguish premises-only exposures from products-completed ops and align claim causation to specific operations over time.

Property & Homeowners

Property loss runs are notorious for inconsistent peril coding. Hail, wind, water intrusion, fire, theft, and vandalism may be lumped together. CAT versus non-CAT tags are sometimes missing or not aligned to PCS events. TIV growth can hide per-location severity trends. Deductible structures, salvage/subrogation, and BI/EE components are often summarized late or not at all. The Risk Analyst needs to segment losses by peril, location, construction type, protection class, and year built—data that frequently sits across multiple documents in a broker submission.

How the Process Is Handled Manually Today

Most teams still approach loss run analysis as a scavenger hunt and a spreadsheet marathon. A typical manual workflow for a Risk Analyst looks like this:

• Gather loss run reports, prior carrier claims summaries, FNOL forms, ISO claim reports, policy schedules, exposure schedules (payroll/vehicle counts/TIV), MVR summaries, and broker cover emails.
• Convert scans to text, clean OCR, and copy-paste claims into a spreadsheet. Build pivot tables by policy year, location, cause of loss, and status (open/closed).
• Reconcile paid versus incurred, track reserve changes over time, and attempt to normalize claim statuses across carriers and TPAs.
• Sample long-tail lines or large-loss years because the clock is ticking; hope the sample represents the whole.
• Handcraft a narrative for underwriters and pricing actuaries—knowing there might be hidden anomalies in unreviewed pages.

Even with careful work, manual reviews are vulnerable to fatigue and inconsistency. Details hide in footnotes, late endorsements shift attachments, and claim reopenings are easy to miss. During surge periods, analysts may rely on old templates built for different carrier formats. The result: elongated time-to-quote, uneven risk selection, and avoidable leakage in pricing and terms.

Introducing Automation: From Page-Scrolling to Precision Answers

Doc Chat was built exactly for this moment. For insurers inundated by unstructured documents—claim files, coverage forms, medical records, intake forms, applications, and demand packages—Doc Chat’s AI-powered agents automate end-to-end document review and real-time Q&A across entire submissions. It ingests thousands of pages at once and returns answers with page-level citations. That’s the foundation of scalable loss run report automation for underwriters and Risk Analysts.

If you’ve tried generic AI on insurance docs, you’ve probably seen it falter. That’s because advanced document work is not just extraction—it’s inference. As Nomad explains in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, answers often emerge from the intersection of document content and institutional rules, not from a single labeled field. Doc Chat operationalizes your playbooks and transforms ambiguous, multi-source evidence into clear, auditable output.

How Nomad Data’s Doc Chat Automates Loss Run Analysis

Doc Chat ingests entire broker submissions—loss run reports, prior carrier claims summaries, FNOL forms, ISO claim reports, police reports, litigation updates, adjuster notes, and endorsement lists—at once. It then:

• Normalizes fields across carriers and TPAs (paid, incurred, reserve, open/closed, litigation flag, cause of loss, location, claim number, policy year) even when labels differ.
• Detects duplicates and merges claim versions to show true development over time, including reopenings and reserve creep.
• Connects loss runs with exposure schedules (vehicle counts, payroll, TIV) to compute rates per unit exposure (loss per vehicle, loss per $1,000 payroll, loss per $1,000 TIV).
• Segments attritional versus catastrophic loss and highlights outliers by policy year, location, or peril.
• Produces configurable “loss story” summaries that slot directly into underwriting files and pricing memos.

Most importantly, Doc Chat supports real-time questioning across the full submission. Analysts can ask:

“List all claims over $100,000 incurred in the past five policy years, with cause of loss, location, status, and whether litigated. Note reserve changes exceeding 25% within six months.”

“Show frequency and severity trends for Commercial Auto by vehicle class and driver tenure. Include at-fault indicators and any fatalities.”

“For Property & Homeowners, break out water damage versus hail by ZIP code, indicate CAT versus non-CAT, and compute loss per $1,000 TIV.”

“For GL & Construction, identify claims tied to falls and struck-by incidents by project. Indicate subcontractor involvement and whether contractual risk transfer was documented.”

Because Doc Chat returns auditable answers with source links, it satisfies internal QA, regulatory, and reinsurance scrutiny. Speed does not come at the cost of defensibility—something highlighted in our client story with Great American Insurance Group (GAIG). See how explainable AI delivered measurable time savings in Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI.

What “AI Review of Complex Broker Submission Loss Runs” Really Means

When we say Doc Chat delivers an AI review of complex broker submission loss runs, we mean it performs the work a seasoned Risk Analyst would—at scale and with unwavering consistency—then puts you in the driver’s seat to interrogate any conclusion in real-time. It doesn’t just extract; it reasons across documents, aligns claims to exposure context, and highlights risks consistent with your underwriting appetite.

Examples of Doc Chat outputs Risk Analysts use immediately:

• Five-year loss triangles with paid, incurred, and count—flagging years with materially different settlement patterns.
• Development alerts for reserve creep, claim reopenings, and litigation conversion rate spikes.
• Location-level heat maps showing frequency versus severity and top loss drivers (peril, cause, line of coverage).
• Driver-level frequency tables (Commercial Auto), project-level BI/PD splits (GL), peril segmentation by ZIP (Property).
• Comparisons of loss cost against exposure growth (vehicles, payroll, TIV) to distinguish genuine deterioration from portfolio expansion.

Because Doc Chat is customized to your rules and thresholds, it can automatically flag what “good” and “bad” look like for your team. If your risk appetite says more than 20 losses per 100 vehicles or more than 1% of payroll as loss cost is a red flag, Doc Chat applies those benchmarks without you having to rebuild dashboards every time.

LOB-Specific Use Cases: From Frequency Patterns to Anomaly Detection

Commercial Auto: Finding the Signal in Driver and Vehicle Data

Commercial Auto loss runs come in all shapes. Doc Chat unifies them and returns answers such as:

Questions to ask Doc Chat

• “Rank loss frequency and severity by vehicle class (tractor, straight truck, light duty) and by driver tenure bands (<1 yr, 1–3 yrs, 3+ yrs).”
• “Highlight losses with fatalities or BI reserves >$250k and identify associated DOT numbers and routes.”
• “Show at-fault distribution by accident type (rear-end, intersection, lane change) and litigation rate by severity decile.”
• “Compute loss per 1,000,000 miles; trend by policy year adjusting for fleet growth.”

What Doc Chat delivers

Source-cited tables that align claims to vehicle schedules, mileage, driver tenure, and MVR flags; outlier detection for sudden BI reserve spikes; early alerts where collision-only severity jumps without exposure growth; and recommendations for terms, deductibles, or risk control measures (e.g., telematics focus, driver training, route changes).

General Liability & Construction: Location, Subcontractors, and Work-at-Height

For GL & Construction, claims are about where, how, and under which contracts work was performed. Doc Chat cross-references loss runs, project lists, COIs, and endorsement schedules to expose patterns:

Questions to ask Doc Chat

• “Break out BI vs. PD by project and cause (falls, struck-by, caught-in/between, electrical). Identify subcontractor involvement and whether indemnity or additional insured endorsements apply.”
• “Compute loss cost per $1,000 payroll by trade (carpentry, roofing, steel erection). Highlight any project exceeding company thresholds.”
• “Which projects show recurring patterns across policy years? Provide linkbacks to claim notes that mention OSHA or safety violations.”

What Doc Chat delivers

Structured outputs showing high-severity clusters at specific jobsites, persistent patterns in falls-from-height, and whether loss cost aligns with subcontracted work. It flags missing COIs or unverified additional insured status when referenced in adjuster notes or endorsement lists. Those insights roll directly into terms: higher deductibles for certain operations, project-specific exclusions, or targeted risk engineering.

Property & Homeowners: Peril Segmentation, CAT vs. Non-CAT, and TIV Context

Property submissions often carry mixed perils and evolving TIV. Doc Chat segments precisely and ties loss to exposure:

Questions to ask Doc Chat

• “Segment losses by peril (wind, hail, fire, water, theft), show CAT versus non-CAT, and compute loss per $1,000 TIV by ZIP.”
• “Identify locations with repeated non-CAT water damage and note any building age/maintenance issues in claim notes.”
• “Surface large BI/EE components and summarize salvage/subrogation outcomes.”

What Doc Chat delivers

Evidence-backed narratives that distinguish hail clusters within specific ZIPs, highlight chronic water intrusion at older properties, and quantify where deductible erosion is driving frequency. That level of clarity enables smarter deductibles, sublimits, and targeted inspections—improving price adequacy and reducing surprises at renewal.

Speed, Scale, and Consistency That Humans Alone Can’t Match

Doc Chat processes enormous volumes quickly and consistently. As described in The End of Medical File Review Bottlenecks, Doc Chat handles hundreds of thousands of pages per minute, maintaining the same attention on page 1 as page 1,500. For loss run review, that means large submissions (15,000+ pages) can be transformed into structured summaries in minutes—not weeks. The system never tires, never loses its place, and always returns source-linked answers.

This isn’t abstract. In Reimagining Claims Processing Through AI Transformation, we’ve shown real, quantifiable gains in speed and accuracy, along with the transparency required for audit and regulatory oversight. Those same gains apply to loss run analysis, where explainability and defensibility are mandatory for actuarial review, reinsurance negotiations, and internal model validation.

Business Impact for Risk Analysts and Underwriting Teams

Embedding Doc Chat into loss run analysis changes your operating model. The benefits reach across the entire underwriting pipeline and the Risk Analyst’s daily workflow:

• Time savings: Turn days of manual spreadsheet work into minutes of review. Analysts can analyze every page rather than sampling, elevating quality while accelerating time-to-quote.
• Cost reduction: Reduced reliance on overtime or overflow vendors; lower loss-adjustment expense by catching anomalies early and influencing terms before bind.
• Accuracy improvements: Consistent normalization across carriers and TPAs; fewer missed reopenings, reserve changes, or litigation indicators; stronger peril and cause coding.
• Scalability without headcount: Handle surge volumes and complex submissions during peak season without burning out your team.
• Better risk selection and pricing: Clearer segregation of attritional versus catastrophic loss, improved exposure-adjusted comparisons, and data-backed appetite decisions.

Practical KPIs teams report when they adopt loss run report automation for underwriters and Risk Analysts:

- 50–80% faster quote turnaround on complex accounts
- 20–40% improvement in pricing adequacy by removing known blind spots
- 30–60% reduction in rework due to missing data discovered late in the cycle
- 10–25% fewer underwriting contingencies at bind due to earlier document completeness checks

Why Nomad Data Is the Best Fit for Loss Run Automation

Nomad Data’s advantage is both technical and service-led:

Built for insurance complexity: Loss runs, ISO claim reports, FNOL forms, claim notes, and endorsement schedules are messy. Doc Chat is trained to read, cross-check, and reason across them. It reflects the reality of your work, not a sanitized demo set.

The Nomad Process: We train Doc Chat on your playbooks, thresholds, definitions (e.g., large loss cutoffs, litigation flags, cause-of-loss taxonomy), and preferred outputs. Your analysts get structured summaries in your format—from day one.

Explainability you can defend: Every finding links back to a page. That creates trust across underwriters, actuaries, reinsurers, compliance, and audit teams.

White-glove onboarding: We implement in 1–2 weeks, not months, with high-touch support that feels like onboarding a new team member rather than “installing software.”

Enterprise-grade security: SOC 2 Type II controls and deployment patterns that align with your data governance needs. As we outline in AI’s Untapped Goldmine: Automating Data Entry, reputable providers do not train foundation models on your data by default—privacy and control come first.

Doc Chat in Action: Common Analyst Prompts that Deliver Answers in Seconds

Use Doc Chat to power critical parts of your loss analysis narrative:

• “Summarize five-year loss history across all carriers, by line (Auto, GL, Property), with paid/incurred, open/closed counts, litigation rate, and top causes of loss. Include exposure growth (vehicles, payroll, TIV).”
• “Identify all claims reopened within 12 months of closure and quantify incremental incurred. Flag projects/locations with clustering.”
• “For the largest 10 losses, trace reserve development through prior carrier claims summaries and adjuster notes; provide a timeline and litigation milestones.”
• “Return water damage frequency by ZIP code for Property & Homeowners, indicating building age bands and recurring address patterns.”
• “Find any mention of indemnification or additional insured status in GL claim notes, COIs, or endorsement lists; link the source pages.”

These are not theoretical. They’re the routine questions a Risk Analyst asks under deadline pressure—and the exact places where a human can miss a small but material detail. Doc Chat’s consistency ensures that detail gets captured every time.

From Manual to Automated: The New Loss Run Operating Model

Here’s how teams evolve once Doc Chat is in place:

Before: Analysts copy/paste claims into spreadsheets, repair broken OCR, reconcile inconsistent labels, and manually stitch together narratives. They sample when time runs short and hope outliers don’t hide in the unreviewed pages.

After: Analysts drag-and-drop full submission files into Doc Chat, receive normalized tables and narrative summaries in minutes, then spend their time asking targeted questions, challenging anomalies, and proposing terms. No sampling. No blind spots. No waiting.

This is the practical meaning of an AI review of complex broker submission loss runs. The AI does the reading and organizing; the Risk Analyst leads the investigation.

Integration and Delivery: Meet Your Team Where They Work

Doc Chat delivers results in multiple formats to fit your workflow:

• Instant HTML/CSV/Excel exports for underwriter workups and actuarial models.
• API integration into your policy admin system, underwriting workbench, or data lake.
• Configurable dashboards and “presets” to standardize output formats by LOB or account type.

Implementation is measured in days, not quarters. Our white-glove team partners with your Risk Analysts to encode your playbooks and standards, usually going live in 1–2 weeks. If you want to start without integration, Doc Chat’s “drag-and-drop” review lets your team realize value the same day—an approach our clients used to build confidence quickly, as described in the GAIG webinar replay.

Governance, Auditability, and Model Risk Management

Loss run findings affect pricing, terms, and ultimately loss ratio. That’s why Doc Chat is designed to be fully auditable. For every answer, you get page-level citations. Oversight teams can quickly verify how a conclusion was reached, satisfying both internal model validation and external review (regulators, reinsurers). As highlighted in the GAIG case, explainability isn’t a nice-to-have—it’s core to adoption and trust.

Doc Chat also institutionalizes your best analysts’ judgment. As we discuss in Beyond Extraction, many underwriting “rules” live in heads, not handbooks. Doc Chat captures and standardizes those rules so every Risk Analyst follows a consistent process, improving results and speeding onboarding.

Addressing Common Concerns: Security, Hallucinations, and Control

Enterprise insurers require enterprise controls. Nomad Data maintains SOC 2 Type II compliance and deploys Doc Chat with strict privacy and governance standards. As noted in AI’s Untapped Goldmine, modern providers do not train foundation models on your data by default. And when the task is extracting facts within a defined corpus—like loss runs and claims summaries—large language models perform with high reliability, especially when answers are tied to citations and reinforced by deterministic post-processing.

Doc Chat’s outputs are designed to be verified. Risk Analysts stay in control, reviewing evidence and adjusting conclusions. Think of Doc Chat as a hyper-capable junior analyst who reads everything and never gets tired—while you make the final calls.

The Strategic Upside: Better Quotes, Stronger Portfolios

Loss run report automation for underwriters and Risk Analysts isn’t just an operational win; it’s strategic. Faster, clearer analysis unlocks better selection, improved price adequacy, and stronger negotiating posture with reinsurers. It reduces leakage by exposing issues early—before bind—and enables more targeted risk control.

At portfolio scale, Doc Chat helps you compare accounts on a true exposure-adjusted basis. You’ll sort submissions by data quality, frequency patterns, and emerging severity signals—allocating underwriting attention where it matters most. Over time, that discipline compounds into a healthier book and more resilient loss ratios.

Why Now: The Technology and the Team Are Ready

In the past, attempts at “automated loss run reading” failed because tools relied on rigid templates and keyword hunts. Today’s AI can read like domain experts and infer across inconsistent layouts, as documented in multiple Nomad articles, including Reimagining Claims Processing Through AI Transformation. The result is a step change: Doc Chat captures, normalizes, and reasons across the unruliest submission packages, bringing precision and speed to the center of underwriting.

You also don’t need to take on an AI science project. With Doc Chat for Insurance, you gain a partner—one that co-creates with your Risk Analysts, encodes your playbooks, and delivers results in 1–2 weeks. Our white-glove model means less time scoping and more time quoting.

Getting Started: A Practical Path for Risk Analysts

Here’s how most teams launch:

1. Pick a high-volume lane: For example, Commercial Auto fleet accounts over 200 vehicles or Property roll-ups with 100+ locations.
2. Define outputs: Agree on the narrative and tables your underwriters want every time (e.g., five-year triangles, exposure-adjusted frequency/severity, anomaly flags).
3. Upload real submissions: Loss run reports, prior carrier claims summaries, FNOL forms, ISO claim reports, endorsement lists, exposure schedules, and any broker addenda.
4. Review and refine: Validate Doc Chat’s findings with your top Risk Analysts. Adjust thresholds, taxonomy, and presets to match your standards.
5. Go live: Push exports to your underwriting workbench or data lake. Standardize the experience across LOBs and regions.

Most clients see immediate time savings on day one. Within a few weeks, you’ll have a repeatable, defensible workflow for AI review of complex broker submission loss runs that your auditors, reinsurers, and leadership can support.

Conclusion: Put Your Risk Analysts Back in the Business of Risk

Loss runs aren’t getting smaller. Submissions aren’t getting simpler. But your team can get faster, more accurate, and more confident. Doc Chat replaces hours of manual hunting with minutes of targeted analysis, all with page-level explainability. Across Commercial Auto, General Liability & Construction, and Property & Homeowners, it’s the operational backbone for loss run report automation for underwriters—designed around the Risk Analyst’s real job: turning unstructured history into clear decisions.

Ready to see it in action? Explore Doc Chat for Insurance and watch a thousand-page submission become an auditable summary in minutes. Then ask the next five questions you never had time to ask—and get the answers, fast.