Supercharging Loss Run Analysis for Complex Submissions with Doc Chat: A Risk Analyst’s Guide for Commercial Auto, General Liability & Construction, and Property & Homeowners

Risk analysts live at the intersection of speed and precision. Your decisions hinge on how quickly and accurately you can digest loss run reports, prior carrier claims summaries, and broker submissions across multiple lines of business. The challenge: complex accounts arrive with inconsistent, multi-carrier loss runs, partial policy years, and thousands of pages of supporting exhibits. Finding true frequency and severity signals, separating shock losses from trends, and identifying anomalous patterns within tight quote timelines is often more art than science.

Nomad Data’s Doc Chat was built to eliminate that bottleneck. Doc Chat for Insurance ingests entire submission packets, normalizes loss run data from any prior carrier format, and answers plain-language questions in seconds. Ask for loss triangles by accident year, identify open claim reserve adequacy, or isolate hail-driven property losses in a specific zip code. Doc Chat delivers page-linked, defensible answers and structured outputs that drop straight into your pricing models—reducing days of manual effort to minutes.

Why Loss Run Review Breaks Under Pressure

For a risk analyst supporting underwriters on Commercial Auto, General Liability & Construction, and Property & Homeowners, loss runs are the backbone of every decision. But they are rarely delivered in a single, clean, machine-readable format. You encounter scanned PDFs, spreadsheets, portal exports, and broker-curated summaries that vary by carrier and policy year. Critical fields—claim status, paid-to-date, case reserve, total incurred, cause of loss, coverage line, deductible, date of loss, loss location, claimant type, subrogation, and salvage—are presented differently in each file. Many complex submissions include overlapping policies, wrap-ups (OCIP/CCIP), or mid-term carrier changes that create gaps or duplicates. Under time pressure, stitching together a coherent view of frequency, severity, and development is risky and time-consuming.

The downstream consequences are real: quote cycles slow, pricing confidence drops, and negotiations with brokers are harder when you cannot rapidly justify loss picks or normalize for exposure changes. Worse, small errors—like double-counting a large claim across carrier exports or missing a closed claim recovery—can materially skew loss ratios.

Line-of-Business Nuances a Risk Analyst Must Master

Commercial Auto: Frequency Noise, Severity Outliers, and Litigation Pressure

Commercial Auto loss runs typically blend third-party liability (bodily injury and property damage) with physical damage, medical payments, and sometimes UM/UIM. Frequency is often high, and severity is dominated by a few large, litigated BI claims. You also encounter DOT-reportable incidents, variable deductibles, and inconsistent coding for vehicle classes, radius of operation, and driver pools. The risk analyst’s job is to normalize paid and incurred trends, isolate nuclear verdict risk indicators, and adjust for exposure shifts—unit count, miles driven, and territory mix. Catching anomalies such as rapid paid growth with flat incurred (reserve decreases masked by subrogation or recoveries) or reserve drift on long-tail BI claims can change your risk posture.

General Liability & Construction: OCIP/CCIP, Products-Completed Ops, and Jurisdictional Landmines

For construction and general liability, loss runs may include project-specific wrap-ups, subcontractor indemnity complications, additional insured claims, and complex cause coding (falls from heights, struck-by, product defect, premises liability). New York Labor Law 240/241 claims introduce outsized severity potential. Completed operations claims may emerge years after project completion. Your analysis must separate on-site occurrence frequency from completed ops severity, understand defense and indemnity splits, and connect losses to contract terms. The difficulty increases when broker submissions span multiple TPAs and prior carriers, each with their own taxonomy and reserving philosophy.

Property & Homeowners: Cat vs. Non-Cat, Water Infiltration, and Deductible Structures

Property and homeowners loss runs require precise cat coding (named storm, hail, wildfire, freeze), geography normalization, and careful treatment of deductibles (AOP vs. wind/hail percentage deductibles). Frequency can be dominated by non-cat water or theft, while severity spikes on cat-coded claims. A high ratio of reopened claims or frequent supplemental payments may indicate chronic recovery issues, contractor disputes, or scope creep. When prior carrier summaries collapse coverage lines or under-report recoveries, your modeled loss ratio and cat load can drift off course.

How Loss Runs Are Handled Manually Today

Most risk analysts still rely on manual document review and Excel gymnastics to make sense of heterogeneous loss runs across carriers and years:

• Receive PDFs and spreadsheets within broker submissions, often with partial years, missing policy numbers, or redacted claim details.
• Re-key or OCR data from scanned loss run reports and prior carrier claims summaries into Excel or BI tools.
• Standardize field names; map cause codes and coverage lines across carriers using VLOOKUPs and custom reference tables.
• Identify and deduplicate the same claim appearing in multiple reports (carrier primary vs. TPA exports, broker summaries).
• Calculate paid, case reserve, and total incurred triangles by accident year or policy year; attempt development views with incomplete snapshots.
• Cross-check with exposure bases (vehicle schedules, payroll, receipts, TIV) if provided in the broker submissions; handle missing exposures with external research or broker follow-ups.
• Break out cat vs. non-cat in property portfolios using inconsistent flags; estimate large loss caps for pricing.
• Prepare a narrative for the underwriter on frequency and severity drivers; note litigation indicators, reopen rates, subrogation and salvage activity, and reserve adequacy concerns.

Even for seasoned analysts, this can consume 6–12 hours per complex account—and considerably more when the submission spans multiple lines or includes opaque wrap-up structures. Meanwhile, deadlines compress, and every hour spent wrangling data delays conversations that actually move the deal forward.

Doc Chat: Purpose-Built AI for Loss Run Normalization and Insight

Nomad Data’s Doc Chat was designed for exactly this problem: ingesting unstructured, inconsistent insurance documents and producing structured answers tied to source pages. It is not generic OCR; it is a suite of domain-trained agents tuned to the workflows of risk analysts and underwriters. As described in our piece Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, the system captures your team’s unwritten rules and codifies them into repeatable, auditable steps.

End-to-End Automation Built Around Loss Runs

• Ingest everything: loss run reports, prior carrier claims summaries, and broker submissions for Commercial Auto, General Liability & Construction, and Property & Homeowners—thousands of pages at once.
• Normalize fields: map paid, case reserve, total incurred, cause of loss, coverage line, claim status, deductible, and cat flags to your internal schema—automatically.
• Resolve entities: deduplicate across carriers, TPAs, and broker summaries; link the same claim number across versions and snapshots.
• Compute KPIs: accident year and policy year triangles, frequency and severity trends, open claim aging, reopen rates, salvage/subrogation recovery ratios, loss pick candidates, and large loss cap scenarios.
• Explainability: every metric links back to the page or row it came from; audit and compliance can validate in seconds.
• Real-time Q&A: ask questions like show all BI claims over 250k incurred with open reserves older than 18 months in New York or isolate all hail claims in ZIP 76137 over the last 5 years with supplemental payments, and get answers instantly.

Doc Chat ingests entire claim files and submission packets without adding headcount. It is the difference between brute-force review and truly intelligent analysis. As highlighted in our client story Reimagining Insurance Claims Management, adjusters and analysts cut review times from days to minutes while improving quality through page-level citations.

From Hours to Minutes: What the Workflow Looks Like

Step 1: Drag-and-Drop Intake

Upload the broker submission ZIP or email attachments directly into Doc Chat: loss run reports by carrier, TPA spreadsheets, prior carrier claims summaries, and supplemental exhibits. No template required. The system classifies each document by type and line of business.

Step 2: Automatic Normalization and Deduplication

Doc Chat parses tabular and free-form loss runs, resolves claim identifiers across versions, aligns accident year and policy year logic, and normalizes fields to your standards. It flags potential duplicates and prior-period restatements with rationale and source links.

Step 3: Instant KPIs and Cohort Views

With a single prompt, you can generate frequency and severity by line of business, coverage type, state, or project. For Commercial Auto, slice by vehicle class or radius; for General Liability & Construction, segment products-completed operations; for Property & Homeowners, separate cat vs. non-cat or wind/hail vs. AOP. Export the structured dataset to CSV for modeling or keep everything in Doc Chat for interactive analysis.

Step 4: Deep-Dive Interrogation

Ask Doc Chat to show reserve adequacy for open BI claims, compute lag between FNOL and first payment, or list all claims with subrogation potential above a threshold. Answers come with citations to the exact row and page in the loss run or prior carrier summary, so you can verify without manual scrolling.

What Risk Analysts Ask Doc Chat—And How It Answers

Because Doc Chat supports real-time Q&A across massive document sets, risk analysts can pursue the follow-ups that normally languish due to time constraints:

• Loss run report automation for underwriters: generate a 5-year accident-year triangle with paid, case reserve, total incurred, and claim counts for each line of business.
• AI review of complex broker submission loss runs: identify outlier years where frequency increased more than 25% while exposure (vehicles, payroll, receipts, TIV) decreased or stayed flat.
• Commercial Auto: list all litigated BI claims above 500k incurred with open reserves older than 12 months; show any reserve strengthening in the last 90 days.
• General Liability & Construction: break out completed operations vs. premises losses; isolate falls from heights in New York and flag any Labor Law 240/241 references.
• Property & Homeowners: separate cat-coded hail claims from non-cat water claims; calculate average supplemental payment per claim over the last 3 years.
• Cross-carrier quality check: surface potential duplicates across prior carrier claims summaries and broker-curated loss run reports; propose a deduped, verified claim list.

The Business Impact: Faster Quotes, Better Pricing, Lower Leakage

When loss run review compresses from 6–12 hours to minutes, everything gets better:

Time savings: Analysts reclaim days across a busy pipeline. Quotes move faster, and you gain capacity without adding staff. As noted in AI’s Untapped Goldmine, the biggest upside often comes from automating high-volume data entry and reconciliation tasks—precisely what loss run normalization requires.

Accuracy improvements: Page-level citations eliminate debates about source truth. Normalization reduces misclassification risk, and automatic deduplication prevents double counting. Machine consistency removes the late-night fatigue that causes spreadsheet errors.

Cost reduction: Less time spent wrangling data means lower acquisition and servicing costs per account. You reserve expensive actuarial or catastrophe resources for true edge cases, not formatting problems.

Pricing confidence and negotiating leverage: With an immediately defensible view of frequency and severity—by coverage, jurisdiction, and period—you can explain your loss pick, apply large loss caps consistently, and stand firm on pricing. Brokers respond to clarity and speed.

Better selection and lower loss ratios: Systematic detection of adverse patterns—reserve drift, reopen rates, chronic water losses—helps avoid bad risks. When you do quote, your price is right-sized to the true signal in the loss runs.

What Makes Nomad Data the Best Partner for Risk Analysts

Doc Chat isn’t a black box. It is the product of Nomad Data’s insurance-first approach: we train the system on your playbooks, standards, and decision logic. Then we deliver it as a white-glove solution that works the way your team already thinks.

The Nomad Process: We interview your risk analysts and underwriters to capture unwritten rules, from how you map cause codes to when you cap large losses. These rules become living presets in Doc Chat, producing consistent outputs across every submission. If you want triangles in a specific format or a loss pick summary per line, that format is enforced every time.

1–2 week implementation: You can start in days. Upload your next complex submission and watch results flow. When you are ready, we integrate with your underwriting workbench via API so normalized loss data lands where you need it. Our clients regularly go from pilot to production in a couple of weeks, not months.

Enterprise-grade security and auditability: Nomad Data is SOC 2 Type II. Every answer links to the page and row it came from, creating a defensible audit trail. Compliance, reinsurance partners, and regulators appreciate the transparency—echoing the trust benefits highlighted in our GAIG case study.

Scale and speed: Doc Chat ingests entire claim files at industrial scale—thousands of pages per minute—without hiring sprees. This is the difference between having a single risk analyst and effectively having ten.

More than summaries: As explored in Reimagining Claims Processing Through AI Transformation, Doc Chat is not limited to summarizing; it enriches and cross-checks. For loss runs, that means automatically comparing year-over-year frequency against exposure shifts and flagging where signals do not match storylines.

Examples: High-Impact Findings Doc Chat Surfaces in Minutes

Commercial Auto: Hidden Severity Drift

Doc Chat normalizes five years of loss runs from two prior carriers and a TPA spreadsheet. It flags that bodily injury claims with open reserves older than 18 months have increased 30% year-over-year, even though unit count declined. The tool links to reserve increase notes from the prior carrier claims summaries and highlights litigation language on three claims. The underwriter adjusts the price and attaches specific conditions tied to driver pool management and litigation controls.

General Liability & Construction: Wrap-Up Noise, Cleaned

A broker submission includes both a contractor’s corporate GL loss run and an OCIP wrap-up report. Doc Chat resolves duplicate claim numbers and removes double counting for wrap claims reflected in both reports. It segments products-completed operations and reveals that completed ops severity is concentrated in one project jurisdiction with known plaintiff-friendly venues. The analyst produces a targeted endorsement strategy in hours, not days.

Property & Homeowners: Cat vs. Non-Cat Truth

After normalizing cat flags across three carriers, Doc Chat shows that the majority of recent severity is not cat-related; it is chronic non-cat water losses clustered in buildings with older plumbing. The loss pick drops when large-cat expectations are removed, while underwriting adds a water mitigation requirement and pricing for the chronic water exposure. A mispriced cat narrative is replaced by a targeted, defensible non-cat strategy.

From Web Scraping to Document Intelligence: Why This Works

Most failed automation attempts treat loss runs like static tables to be scraped. But as we outline in Beyond Extraction, the real task is inference—deducing consistent, comparable meaning from inconsistent, multi-source documents. Doc Chat reads like a seasoned risk analyst, applying your taxonomy and thresholds to harmonize paid, reserve, and incurred across carriers, then computing analyses the way your team does them by hand. The output is not just data; it is decision-ready intelligence.

Key Outputs Doc Chat Delivers for Each Line of Business

Commercial Auto

• Accident-year triangles for paid, case reserve, total incurred, and counts; filterable by BI, PD, MedPay, UM/UIM.
• Litigation indicator and reserve age cohorts (e.g., open 12+, 18+, 24+ months).
• Frequency and severity by vehicle class, driver pool, and radius of operation; territory normalization.
• Reopen, subrogation, and salvage analysis with recovery ratios.

General Liability & Construction

• Segmentation of premises vs. products-completed operations; wrap-up (OCIP/CCIP) resolution.
• Cause-of-loss clustering (falls, struck-by, product defect) and jurisdiction overlays.
• Labor Law flagging (240/241) and severity concentration analysis.
• Defense and indemnity patterns; claim lifecycle and reopen metrics.

Property & Homeowners

• Cat vs. non-cat normalization; hail, wind, named storm, wildfire, freeze tags.
• Deductible structure recognition (AOP vs. % wind/hail) and net-of-deductible analytics.
• Chronic water loss detection; supplemental payments and reopen trends.
• Zip-code clustering and building-level recurrence signals.

Integrating With Your Underwriting Workflow

Start with drag-and-drop. As trust builds, Doc Chat integrates with your underwriting workbench, data lake, or pricing models via API. Normalized, deduped loss data and computed KPIs can flow directly into your rating worksheets or downstream BI dashboards. You can schedule batch processing for renewal pipelines or run Doc Chat interactively during broker meetings to test what-if scenarios.

This aligns with the adoption journey we outline in AI for Insurance: Real-World Use Cases: quick wins with document-driven automation, followed by deeper integration that compounds value across claims, underwriting, and portfolio management.

Quantifying ROI: What Risk Analysts and Underwriters Can Expect

Organizations that implement Doc Chat for loss run analysis typically see the following within the first quarter:

• Cycle time reduction: 70–90% faster turnarounds on complex submissions.
• Analyst throughput: 2–4x more accounts per analyst per week without sacrificing diligence.
• Data quality: near elimination of duplicate counting and format-related errors through normalization and page-level citations.
• Hit ratio: improved broker experience and faster quotes translate into higher bind rates on good risks.
• Loss ratio: earlier detection of adverse patterns enables better selection, terms, and pricing discipline.

These numbers mirror the broader gains reported in our articles The End of Medical File Review Bottlenecks and AI’s Untapped Goldmine: when you automate repetitive document work, speed and quality rise together.

Trust, Explainability, and Compliance

Doc Chat maintains a transparent audit trail for every computed metric. Answers cite the exact page or row from the loss run report or prior carrier claims summary. This supports internal model governance, reinsurer reviews, and regulator or auditor inquiries. Risk analysts can defend every conclusion with original-source references—no more black-box calculations or opaque macros.

Security is enterprise-grade. Nomad Data’s controls are built for sensitive insurance data. And because outputs are grounded in provided documents, hallucination risk is minimized; Doc Chat is designed to retrieve and synthesize, not invent. This is how we deliver AI you can put in front of underwriting, actuarial, and compliance with confidence.

Implementation: From Pilot to Production in 1–2 Weeks

Getting started is simple:

1. Load real submissions: Send us representative loss run reports, prior carrier claims summaries, and broker submissions across Commercial Auto, General Liability & Construction, and Property & Homeowners.
2. Configure presets: We encode your normalization rules, analysis templates, and output formats (e.g., triangles, loss pick summaries, large loss cap parameters).
3. Validate together: We compare Doc Chat outputs to your prior analyses, refine edge cases, and document governance guardrails.
4. Scale up: Turn on API export to your underwriting workbench and schedule batch processing for renewal cohorts.

Because Doc Chat is purpose-built for insurance documents, you get immediate value with minimal IT lift. As highlighted in our GAIG story, many teams adopt the tool the same day they see it—then integrate deeper over the following weeks.

How Doc Chat Elevates the Risk Analyst’s Role

Doc Chat does not replace the judgment of a risk analyst; it amplifies it. By removing the drudgery of re-keying, deduping, and reconciling inconsistent loss runs, the system frees analysts to do the highest value work: probing outliers, challenging narratives, stress-testing pricing, and designing terms that shape loss outcomes. The result is a better, faster underwriting partnership with brokers and insureds, and a more satisfying analyst role focused on investigation and decision-making rather than data cleanup.

Two High-Intent Searches Your Team Can Now Own

Nomad Data built Doc Chat to directly answer the queries risk analysts and underwriters type when the clock is ticking:

1) loss run report automation for underwriters — Doc Chat normalizes, dedupes, and computes accident-year triangles plus frequency and severity by coverage line with one prompt, exporting structured data to your pricing spreadsheet or model.

2) AI review of complex broker submission loss runs — The system ingests messy, multi-carrier packets and produces a reconciled, citation-backed view of loss dynamics with instant drill-downs by LOB, jurisdiction, project, and peril.

What You Can Ask Doc Chat Right Now

Try prompts like these on your next submission:

• Summarize 5-year loss triangles by accident year for Commercial Auto BI, PD, and UM/UIM; include claim counts and average severity.
• Flag any GL completed operations claims that reopened within 12 months of closure and show reserve changes over time.
• List Property claims tagged as hail or named storm with incurred over 100k; separate cat vs. non-cat and show deductibles applied.
• Identify potential duplicates between the broker summary and the carrier loss run; present a deduped claim list with rationale.
• Compute loss pick candidates with and without a 250k large loss cap; show impact by line of business.

The Bottom Line

Loss run analysis is too important to be slowed by formatting and reconciliation tasks. For risk analysts supporting Commercial Auto, General Liability & Construction, and Property & Homeowners, Doc Chat converts messy, multi-source loss runs into immediate, validated insight. You get speed without sacrificing diligence; consistency without losing nuance; and audit-ready analysis that strengthens every conversation with brokers, insureds, and internal stakeholders.

See how fast you can move from unstructured documents to decision-ready analysis. Explore Doc Chat for Insurance and put loss run report automation for underwriters to work on your next complex submission.