Spotting Prior Claims and Open Litigation in Submission Files Using AI (General Liability & Construction, Property & Homeowners, Specialty Lines & Marine) - A Guide for the Underwriting Manager

Underwriting Managers are under pressure to price accurately, select rigorously, and move quickly. Yet the very evidence that most influences loss ratio—prior claims, severe losses, and open litigation—often hides in sprawling broker submission packages, vague litigation summaries, inconsistent loss run reports, and attachments that arrive in different formats for every account. Manually stitching these clues together costs hours per risk and still risks missing critical details. That is precisely where Doc Chat by Nomad Data changes the underwriting equation.

Doc Chat is a suite of purpose-built insurance AI agents that read, extract, and cross-reference documents at scale. For underwriting leaders searching for AI review for open litigation in submissions or exploring prior claims detection automation underwriting strategies, Doc Chat delivers a consistent, audit-ready process that surfaces every relevant reference to prior losses and ongoing legal action—across thousands of pages—in minutes, not days.

The Underwriting Challenge: Prior Losses and Open Litigation Hide in Plain Sight

Across General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine, prior claims and active litigation materially influence appetite fit, pricing, deductibles, attachment points, and terms. But the underlying evidence is fragmented:

• Broker submission packages differ by retailer, region, and line of business, bundling ACORD forms, schedules, narratives, COIs, subcontractor rosters, safety manuals, SOVs, valuations, marine surveys, and emails.
• Loss run reports arrive from multiple carriers and TPAs with unique column structures, accident descriptions, and reserve terminology. Five-year histories may be incomplete, stale, or redacted.
• Litigation summaries sometimes provide only high-level context. Docket numbers are missing, case captions are abbreviated, and statuses (open, dismissed, settled) are ambiguous or outdated.

Underwriting Managers know that a missed products claim, a pattern of labor law injuries, a premises liability severity, or an open construction defect suit can swing results from profitable to unprofitable. The risk isn’t theoretical; it is operational. As documentation volume expands, manual processes fall behind. That is why organizations are evaluating targeted solutions for AI review for open litigation in submissions that can cut through variability and volume without adding headcount.

Nuances by Line of Business: What the Underwriting Manager Must Catch

General Liability & Construction

GL and construction submissions are complex because exposures cascade through contracts and jobsite practices. Key signals of prior claims and litigation often sit in:

• ACORD 125/126 and supplemental questionnaires (e.g., products-completed ops, project values, work at height).
• COIs for subcontractors, additional insured endorsements (e.g., CG 20 10 and CG 20 37), primary/noncontributory language, and waivers of subrogation.
• OSHA 300/300A logs, incident logs, toolbox talk records, and safety manuals that hint at frequency/severity trends.
• Loss run reports from multiple carriers/TPAs with different loss descriptions (bodily injury, products liability, NY Labor Law 240/241) and reserve histories.
• Litigation summaries that reference construction defect, contractual indemnity disputes, or plaintiff attorney demand letters.

Open litigation regarding scaffold falls, trench collapses, or products liability can dramatically affect selection, attachment, or risk transfer requirements. Yet those references may be buried inside email chains or appendices in the broker submission package.

Property & Homeowners

Property underwriters contend with scattered indicators of prior losses and open suits about coverage disputes or subrogation. Common sources include:

• ACORD 140, statement of values (SOV) spreadsheets, appraisal reports, and inspection photos.
• Loss run reports detailing fire, water, wind/hail, and theft across varying policy periods.
• Repair invoices, remediation vendor reports, and adjuster notes revealing cause-of-loss patterns.
• Homeowners add personal data sources such as CLUE Property Reports and prior carrier letters of experience.
• Litigation summaries for coverage disputes, mortgagee conflicts, or HOA-related liability that affect aggregation and recovery potential.

Prior severe water losses, roof condition disputes, or subrogation litigation can influence deductible strategy, water damage sublimits, time elements, and protective safeguard endorsements.

Specialty Lines & Marine

In Marine and Specialty, prior claims and open cases are often entwined with technical documentation:

• Hull & Machinery and P&I claims history, surveyor reports, and class/port state control inspections that flag maintenance and operational issues.
• Cargo manifests, bills of lading, stowage plans, and loss run reports for theft, wet damage, and temperature excursions.
• Marine survey reports, compliance documentation (e.g., SOLAS/ISM/MLC), and loss prevention recommendations.
• Litigation summaries for cargo damage disputes, collision liability, and charterparty disagreements that may be active in multiple jurisdictions.

Missing a pending cargo damage suit or recurring machinery failure claims can upend expected loss costs and reinsurance dialogue.

How It’s Handled Manually Today—and Why It Breaks

Even the best underwriting teams wrestle with patchwork workflows:

• Assemble and skim the broker submission package, copying snippets of potential claims into a spreadsheet.
• Scan loss run reports for the last 3–5 years; normalize claim descriptions, statuses (open/closed), and paid/reserve values manually.
• Read litigation summaries, then try to confirm details via carrier notes or public dockets (e.g., state court portals, PACER) when time permits.
• Email the broker for missing periods, carrier letters, or clarification on large losses and open litigations; wait days for a response.
• Reconcile inconsistencies between ACORD forms, narratives, and schedules, often under deadline pressure.

Manual processes introduce risk:

- Cycle time expands. The desk spends 3–8 hours per mid-market account, longer for layered construction or marine schedules.
- Human error creeps in. Fatigue means missed exclusions, overlooked open suits, or misread case status.
- Inconsistent decisions. Two underwriters reviewing the same file may reach different conclusions based on what they noticed or had time to confirm.
- Limited scalability. Seasonal surges and E&S spikes strain capacity; overtime becomes the only pressure valve.

The result: missed red flags and suboptimal pricing. Underwriting Managers see the downstream effect in loss ratio volatility and increased reinsurance friction—exactly the problems that prior claims detection automation underwriting aims to solve.

Doc Chat Automates Prior Claims and Open Litigation Discovery

Doc Chat by Nomad Data ingests entire submission files—ACORDs, schedules, SOVs, loss run reports, litigation summaries, marine surveys, OSHA logs, emails, and more—and performs structured, cross-document analysis. It is built to answer underwriting-ready questions with page-level citations:

• Normalize loss runs across carriers: The agent standardizes schema differences, aligns policy periods, and consolidates paid, reserve, and incurred values across 3–10 years of history.
• Detect open litigation: Doc Chat flags any reference to ongoing lawsuits, demand letters, case captions, docket numbers, counsel names, courts, and status language (pending, appealed, settled, dismissed) anywhere in the submission.
• Cross-check narratives vs. documents: If a broker narrative says “no known losses” but a subcontractor COI email or OSHA log hints otherwise, the system raises a discrepancy with links to the source pages.
• Summarize patterns and severity: It compiles frequency/severity by cause of loss (e.g., slip and fall, NY Labor Law, water damage, cargo wetting) and flags deterioration or clustering on specific locations, projects, or routes.
• Return source-cited answers: Every extracted item is accompanied by clickable citations to the exact page or paragraph it came from.

Unlike generic tools, Doc Chat is trained on your program rules. As described in Nomad’s perspective on the difference between extraction and inference in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, the agent doesn’t just lift text—it applies your underwriting playbook to interpret ambiguous, scattered signals and produce a consistent, underwriter-ready output.

What an Underwriting Manager Can Ask Doc Chat in Real Time

• “List all prior claims for the last 5 policy years with loss date, cause, paid, reserve, incurred, and current status. Include page citations.”
• “Are there any references to open litigation, demand letters, or active claims? Provide the case caption, docket number (if present), and court.”
• “Show me discrepancies between ACORD answers and the loss runs or OSHA logs.”
• “Summarize loss drivers for projects over $5M TCV and flag claims with products-completed ops exposure.”
• “For the SOV, surface properties with prior water damage or fire claims and create a ranked watchlist.”
• “In the marine surveys and claims history, note repeat machinery failures or cargo wet damage patterns.”

In seconds, the agent returns a structured summary, a normalized claims table, litigation callouts, and an executive narrative tuned to your appetite and guidelines. As documented in Nomad’s client stories, tasks that once consumed days of manual searching now take moments, with page-level explainability that satisfies audit and compliance teams. See how carriers accelerated complex review in Reimagining Insurance Claims Management: Great American Insurance Group Accelerates Complex Claims with AI.

Embedding AI Review for Open Litigation in Submissions into Your Workflow

Doc Chat slots into the underwriting process without requiring a core-system overhaul:

- Intake: Drag-and-drop or API-based ingestion of the submission packet (PDFs, XLSX SOVs, emails, image scans).
- Classification: The agent auto-identifies document types: ACORDs, loss runs, litigation summaries, OSHA logs, marine surveys, inspection reports, COIs, and contracts.
- Extraction & cross-check: It standardizes loss runs, identifies litigation references, and cross-checks narratives against attachments.
- Decision support: It assembles an underwriting brief: prior claims table, open litigation synopsis, loss trend analysis, and questions for the broker—each with citations.
- System integration: Export structured data to your underwriting workbench, rate/quote/bind platform, or data warehouse via APIs.

Because the agent supports real-time Q&A (“Which properties have prior water losses over $50,000?” “Any open NY Labor Law suits?”), underwriters spend more time on pricing and negotiations and less time on document triage.

Business Impact for the Underwriting Manager

Deploying Doc Chat for prior claims detection automation underwriting has immediate and compounding benefits:

• Time savings: Move from 3–8 hours of manual review per account to minutes. Nomad has demonstrated multi-thousand-page reviews reduced from weeks to minutes, as described in The End of Medical File Review Bottlenecks.
• Cost reduction: Trim overtime and third-party review spend while up-leveling the existing team’s throughput.
• Accuracy: Eliminate fatigue-related misses. Page-cited outputs and standardized claims tables reduce human error and improve defensibility.
• Consistency: Codify best practices so every desk applies the same rules—especially critical for appetite alignment and reinsurance communication.
• Speed to quote: Faster prior-loss and litigation validation means more timely indications, improved broker experience, and higher hit ratios.
• Loss ratio improvement: Better selection and sharper terms on risks with problematic litigation or deteriorating loss trends.

Beyond first-order efficiency, Doc Chat helps stabilize reserve expectations and reinsurance dialogue by delivering consistently structured insights across the portfolio—an advantage highlighted in Nomad’s overview of transformational use cases in AI for Insurance: Real-World AI Use Cases Driving Transformation.

Why Nomad Data’s Doc Chat Is the Best Fit for Underwriting Leaders

Underwriting Managers need more than a generic summarizer. They need an AI partner built for insurance documentation and tuned to their standards. Doc Chat stands out because it delivers:

• Volume: Ingest entire submission files—thousands of pages—without adding headcount. Reviews move from days to minutes.
• Complexity: Exclusions, endorsements, captions, and litigation references often hide within dense, inconsistent documents. Doc Chat digs them out and cross-links them.
• The Nomad Process: We train the agent on your underwriting playbooks—how you interpret “no known losses,” how you treat open suits, when to escalate to Legal—so outputs match your standards.
• Real-Time Q&A: Ask, “List every open case cited anywhere in this file,” and get instant answers with page citations.
• Thorough & Complete: The agent surfaces every reference to coverage, liability, damages, and litigation-related facts across the submission, eliminating blind spots.
• Your Partner in AI: You’re not buying a one-size-fits-all tool. You’re gaining a strategic partner who co-creates solutions and continuously improves them.

Security and governance are first-class considerations: Nomad maintains enterprise-grade controls, page-level explainability, and transparent audit trails—key for underwriting authorities, reinsurance partners, and regulators. Learn how transparency and source-level verification drive adoption in this GAIG webinar recap.

White-Glove Implementation in 1–2 Weeks

Doc Chat is designed to deliver value fast:

- Week 1: We collect representative submissions for your lines (GL & Construction, Property & Homeowners, Specialty & Marine), codify your underwriting rules for prior-loss validation and open-litigation handling, and stand up secure access.
- Week 2: We tune outputs (claims table fields, litigation synopsis structure, appetite flags) and integrate with your underwriting workbench if desired. Your team is live with drag-and-drop uploads and real-time Q&A on day one.
- Ongoing: White-glove support, continuous learning from your feedback, and new prompts/presets added as needs evolve.

This light-touch rollout mirrors Nomad’s track record of rapid adoption and immediate productivity. It’s why teams routinely shift from demos to daily use the same week, as shared in Reimagining Claims Processing Through AI Transformation.

Examples by Line: From “No Known Losses” to Source-Cited Evidence

General Liability & Construction

For a GC with a multi-state footprint, Doc Chat normalizes five years of loss run reports, flags three open suits (two NY Labor Law, one products-completed ops), and highlights an OSHA 300A spike at one division. It surfaces contractual risk transfer gaps (missing primary/noncontributory language on key COIs) and links each finding to the source page. The underwriting brief recommends higher deductibles, subcontractor warranty wording, and a targeted broker questionnaire—before quote.

Property & Homeowners

For a mixed commercial/residential SOV, the agent cross-references SOV locations with prior water and fire losses in the loss runs, flags open subrogation litigation on a major water loss, and produces a ranked watchlist of properties for inspection. It recommends water damage sublimits and protective safeguards based on repeat issues. For homeowners, it compares the narrative against a CLUE report to reconcile prior claims, with citations to both sources.

Specialty Lines & Marine

For a coastal cargo program, Doc Chat reads marine survey reports, class inspections, and cargo claims history to detect recurring wet damage during specific seasons and routes. It finds an open cargo damage suit referenced once in an email appendix and once in a litigation summary, connects the dots, and returns the consolidated litigation profile with docket notes. The underwriting brief recommends route-specific deductibles, condition surveys, and loss control follow-ups.

What Makes AI Review for Open Litigation in Submissions Different with Nomad

Two capabilities differentiate Doc Chat for the underwriting use case:

• Inference at scale: As Nomad explains in Beyond Extraction, the real work isn’t just finding fields—it’s interpreting scattered clues. Doc Chat reconciles narratives, loss data, and attachments to infer an accurate litigation and prior-claims picture.
• Underwriter-first outputs: Outputs are formatted for underwriting decisions: a normalized prior-claims table, an open-litigation synopsis, discrepancy alerts, and a broker question list—each with citations so you can validate instantly.

This is also why prior claims detection automation underwriting is not a generic OCR problem. It’s a codified underwriting problem where your tacit rules—what you check first, how you treat missing periods, when to escalate to counsel—become a repeatable, teachable AI process.

Governance, Security, and Auditability

Underwriting decisions must be defensible. Doc Chat maintains document-level traceability and page-cited answers, providing an auditable chain from every conclusion back to its source. Outputs can be stored alongside the submission, satisfying regulator and reinsurer expectations for transparency and consistency. Nomad’s enterprise-grade security and governance controls align with insurer requirements for PHI/PII handling and access controls.

From Manual to Managed: A Before-and-After Snapshot

Before: An underwriter receives a 400-page submission for a regional contractor. They skim the broker narrative, glance at the ACORDs, and dive into three sets of loss run reports. They copy/paste into a spreadsheet, try to reconcile reserves and closings, and email the broker for missing policy years. A single mention of an open suit hides inside a subcontractor COI email thread—and gets missed. The quote goes out with undetected exposure.

After: The underwriter drops the package into Doc Chat. Minutes later, they have a normalized claims table, an open-litigation synopsis with two flagged NY Labor Law cases, discrepancy alerts between OSHA logs and narrative statements, and a broker question list with source citations. They price confidently, adjust the deductible, and condition approval on updated risk transfer language—before the broker asks for terms.

Frequently Asked Questions from Underwriting Managers

Can Doc Chat handle mixed-format loss runs from multiple carriers?
Yes. It normalizes column names, aligns policy periods, and standardizes paid/reserve/incurred fields while preserving page-cited provenance.

How does it find open litigation if a formal docket number isn’t present?
It flags contextual references—case captions, law firm names, courts, demand letters, settlement discussions—and links you to those mentions. Where you have integrations, it can enrich with external checks via your approved data sources.

What about Specialty & Marine technical documents?
The agent reads surveys, class reports, and P&I claims narratives, then ties those insights to litigation references and prior-loss patterns across voyages or vessels.

Will this replace the underwriter?
No. Doc Chat replaces the rote reading and reconciliation work so your team can focus on judgment, negotiation, and portfolio strategy. As Nomad notes in its field experience, AI is a “capable but supervised teammate,” not an autonomous underwriter.

Where to Start: A Practical Rollout

Pick a high-volume segment—GL contractors, coastal property schedules, or marine cargo—where prior losses and litigation history frequently complicate selection. Hand Nomad 15–25 recent submission files and your underwriting checklist for prior claims and open litigation. Within 1–2 weeks, your team will be reviewing AI-generated briefs with page-level citations, asking follow-up questions in real time, and exporting structured fields into your underwriting systems.

As your team gains confidence, expand to additional lines and incorporate preset outputs for renewals, referrals, and reinsurance summaries. Because Doc Chat is tailored to your playbooks, its value compounds over time while maintaining consistency across the desk.

The Strategic Advantage for the Underwriting Manager

Market cycles reward carriers and MGAs who combine speed with rigor. With Doc Chat, your underwriting unit can promise near-real-time insight on the two hardest parts of a submission—prior claims and open litigation—and back it up with audit-ready citations. You quote faster, negotiate better, and avoid surprises.

That is how underwriting leaders turn documentation complexity into a competitive edge—and how you institutionalize best practices across General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine.

Ready to see how AI review for open litigation in submissions works in your portfolio? Explore Doc Chat for Insurance and bring your toughest submission files to a live session.