Spotting Prior Claims and Open Litigation in Submission Files Using AI — Underwriting Manager’s Playbook for General Liability & Construction, Property & Homeowners, and Specialty & Marine

Underwriting Managers face an escalating paperwork problem. Broker submission packages arrive as sprawling PDFs, email threads, spreadsheets, and supplements that can stretch into the hundreds or even thousands of pages. Buried in those pages are the most decisive signals for risk selection and pricing: prior claims and any open or pending litigation. The challenge is clear: you need a reliable, fast, and defensible way to surface these signals during pre-bind — before adverse selection creeps into your book. That is exactly where Doc Chat by Nomad Data delivers immediate value.

Doc Chat is a suite of purpose-built, AI-powered document agents designed for insurance. It ingests entire submission files, normalizes inconsistent formats, and answers underwriting-grade questions in seconds (with page-level citations). Instead of having your team painstakingly hunt for loss run anomalies, missing years, or references to ongoing lawsuits, you can ask: “List all prior claims by date of loss and current status,” or “Identify any open litigation referenced anywhere in this submission,” and receive fully sourced answers. This article explains how Underwriting Managers across General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine can use AI to run an “AI review for open litigation in submissions” and deploy “prior claims detection automation underwriting” at scale.

The Underwriting Manager’s Problem by Line of Business

General Liability & Construction: GL and construction risks are litigation-prone. Subcontractor networks, additional insured obligations, wrap-ups (OCIP/CCIP), completed operations, and site conditions create complex exposures. Prior claims hide in multi-carrier loss runs and small-text footnotes in litigation summaries. OSHA 300/300A logs, certificates of insurance (COIs), ACORD 125/126 applications, and safety manuals often reference incidents that never reach a neat, consolidated list.

Property & Homeowners: Water damage, fire, theft, and weather claims may be spread across different carriers and time periods. The Statement/Schedule of Values (SOV), COPE reports, inspection photos, ACORD 140, prior carrier dec pages, and mortgagee requirements can contain conflicting details. A single earlier water loss, if still litigated with a contractor or HOA, can materially change your appetite and pricing for a coastal homeowner’s risk or a commercial habitational schedule.

Specialty Lines & Marine: From cargo to hull to marine liability, prior claims and open litigation are often dispersed across P&I Club correspondence, surveyor reports, charter party agreements, port state control findings, cargo manifests, and carrier emails. Specialized terminology and varying documentation standards make it easy to miss a still-active recovery action or a pending subrogation dispute when the submission is stitched together from multiple files.

Across all three lines, the Underwriting Manager must referee consistency: aligning loss run totals to ACORD applications, confirming claim counts against broker narratives, and matching entity name variations (LLCs, DBAs, former names) across documents. Missing an open suit or undercounting prior claims is not a clerical error — it is a pricing, appetite, and E&O risk.

Why Prior Claims and Open Litigation Are Hard to Find

Submission packages are not standardized. A broker emails a 300-page PDF that combines ACORD forms, SOVs, COPE detail, loss run reports from three prior carriers, litigation summaries, demand letters, and endorsements from expiring policies. Prior claims might be referenced indirectly — in a safety meeting minute, in a single line on a loss run attachment, or in a legal memo appended to a renewal questionnaire. Some submissions include external references: court docket numbers without links, claim numbers without carriers, or abbreviations that differ by market.

Even when loss runs are present, they can be incomplete or inconsistent in time span, reserve status, or coding. Litigation may be open but not explicitly labeled as such. A sentence like “Defense counsel anticipates resolution next quarter” can be your only clue that the matter is still active. This is exactly the use case that inspires many Underwriting Managers to seek an AI review for open litigation in submissions: the signal exists, but it is scattered, inconsistent, and time-consuming to assemble.

How the Process Is Handled Manually Today

Most underwriting teams rely on analysts or assistants to triage documents and build spreadsheets by hand. This is slow, costly, and error-prone — especially during peak renewal seasons or when large schedules arrive. A typical manual workflow looks like this:

Open the broker submission package and identify included documents (ACORD 125/126/140, SOV/COPE, loss run reports, litigation summaries, questionnaires, safety materials, dec pages, endorsements).
Skim ACORD applications and broker narratives for stated claim counts and litigation references.
Manually read loss run reports, retype claim numbers, dates of loss, causes, paid/OS, and status into a spreadsheet; reconcile multiple carriers and policy periods.
Cross-check entity names (legal vs. DBA) to avoid double-counting or missing claims tied to alternates.
Search litigation summaries and correspondence for active suits, legal counsel names, and settlement posture; note any docket numbers for later verification.
Compare totals back to ACORD responses; flag discrepancies, request missing years or carriers, and await broker corrections.
Re-run the reconciliation when replacements arrive.

This process can consume hours per submission, sometimes days for complex schedules. It strains capacity, introduces variance in quality between reviewers, and still misses subtle references that trigger different appetite, pricing, or terms.

The Cost of Missing Prior Claims or Open Litigation

Pricing accuracy suffers when loss frequency or severity is understated. Risk selection degrades when ongoing litigation signals a pattern of operational issues. Coverage terms may be misaligned when your team misses a loss pattern that calls for sublimits, higher deductibles, or revised exclusions. Finally, your organization incurs operational drag: endless back-and-forth for missing data, rework when discrepancies are discovered late, and E&O exposure when an oversight surfaces post-bind. The downstream impact includes elevated loss ratios, margin compression, and competitive disadvantage.

Introducing Doc Chat by Nomad Data for Underwriting Managers

Doc Chat by Nomad Data removes this friction by automating the heavy lift of document triage, extraction, and cross-checking. Built specifically for insurance documents, Doc Chat ingests entire submission files — not just forms but the messy, unstructured attachments that carry the real risk signals. Then it answers targeted questions, generates structured outputs, and cites the exact pages it used to form its conclusions.

Unlike generic tools, Doc Chat is trained on your underwriting playbooks, document types, and standards. It normalizes patterns across ACORD forms, loss run reports, litigation summaries, endorsements, SOVs, COPE, OSHA logs, and expiring dec pages. You get a custom, standard output every time — a consistent experience that accelerates decisions and reduces errors.

How Doc Chat Automates Prior Claims Detection and Open Litigation Discovery

With Doc Chat, Underwriting Managers can operationalize prior claims detection automation underwriting and an AI review for open litigation in submissions with a set of specialized capabilities:

Full-file ingestion at scale: Doc Chat can process entire submission packets, including ACORD 125/126/140, SOV/COPE, loss run reports (multi-carrier), litigation summaries, questionnaires, photos, and correspondence. It has been demonstrated processing volumes at enormous speed, with Nomad reporting throughput of approximately 250,000 pages per minute in complex medical contexts — a clear signal of scalable infrastructure for large document sets. See “The End of Medical File Review Bottlenecks.”
Entity and alias reconciliation: Detects legal names, DBAs, former names, and affiliated entities across documents, avoiding omissions or double-counting.
Loss run normalization: Extracts claim numbers, dates of loss, cause of loss, paid/OS, reserve movements, and status; aligns timeframes across carriers; marks gaps and missing years; and creates a consistent view by policy period.
Litigation signal surfacing: Identifies explicit and implied references to filings, dockets, defense counsel, settlement discussions, and any “active,” “pending,” or “anticipated” litigation language embedded anywhere in the submission.
Cross-document corroboration: Compares claim counts on ACORD forms to loss run totals and litigation mentions in broker narratives or legal memos, highlighting discrepancies with citations.
Structured export: Outputs a clean spreadsheet or JSON of claims and litigation signals that can be dropped into your rating model or underwriting workbench.
Real-time Q&A with citations: Ask “List all open claims and open litigation with dates and current status” and receive a fully sourced answer with clickable page references for audit and peer review.

What This Looks Like in General Liability & Construction

Doc Chat reads your ACORD 125/126, contractor questionnaires, OSHA 300/300A logs, subcontractor agreements, and multi-carrier loss runs. It reconciles claim totals and flags whether completed operations or products claims persist. It highlights references like “still in discovery” or “settlement pending” within litigation summaries or defense counsel emails. It then produces a ready-to-use table showing frequency, severity, open/closed status, and any legal actions tied to each event — with page citations into the broker submission package.

What This Looks Like in Property & Homeowners

Doc Chat aligns SOV/COPE data with ACORD 140, inspection reports, and loss runs to spotlight water or fire losses that signal maintenance issues or systemic risk. It notes if a property loss has spilled into litigation with contractors or HOAs and whether the suit remains active. If ACORD responses list “5 claims last 5 years” but carrier loss runs show a sixth event (or missing years), Doc Chat flags the variance and points to the exact page where the discrepancy appears.

What This Looks Like in Specialty Lines & Marine

Marine submissions can include P&I Club correspondence, surveyor reports, cargo manifests, and port state control histories. Doc Chat extracts prior claims from varied terminologies, connects references across disparate documents, and identifies any current recoveries or legal actions underway. It delivers a consolidated claims and litigation view, so underwriters can adjust appetite, terms, or pricing with confidence.

Beyond Extraction: Consistent Inference Across Messy Documents

Finding prior claims and open litigation is not just “reading a field on a form.” It requires inference across inconsistent, multi-source documents. Nomad Data has written about this distinction in “Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.” Doc Chat does more than OCR or key-value pull: it applies your underwriting logic, follows your playbook, and assembles conclusions that were never explicitly typed in a single place. That is the difference between generic IDP and underwriting-ready automation.

How It Works When You Ask Questions

Doc Chat’s real-time Q&A is purpose-built for insurance. As described in Nomad’s claims-focused write-up “Reimagining Claims Processing Through AI Transformation,” users can pose plain-language questions and receive instant answers with source citations. For underwriting, this translates directly into question-driven triage: “Are there any open suits mentioned?” “Summarize prior GL claims by cause for the last five years.” “Which locations on the SOV have repeat water losses?” Answers arrive with link-backs to the exact pages, so reviewers and auditors can verify in seconds.

Manual vs. Automated: What Changes on Day One

With Doc Chat, your review shifts from scrolling to strategic investigation. Rather than assembling a picture manually, your team starts with an accurate, cited summary. From there, they ask follow-ups to clarify discrepancies, request missing years of loss runs, or challenge ambiguous statements in a broker narrative. Underwriters spend their time making judgment calls — not copying numbers into spreadsheets.

Nomad’s blog “AI’s Untapped Goldmine: Automating Data Entry” explains why even advanced insurance tasks ultimately hinge on reliable extraction and normalization. Doc Chat standardizes output formats, pushes structured data into your underwriting workbench, and ensures consistent quality across your staff and partners.

Business Impact: Time, Cost, and Accuracy

Time Savings: Underwriting analysts can spend hours unpacking a single submission. Doc Chat compresses that effort to minutes. Nomad has documented extreme throughput — on the order of hundreds of thousands of pages per minute in other insurance contexts — demonstrating that even the largest submission packets are no longer bottlenecks. Faster reviews mean earlier declinations on mismatch risks, quicker indications on attractive ones, and reduced cycle times for brokers and insureds.

Cost Reduction: When your team no longer re-enters loss runs by hand or chases ambiguities across emails, you reduce overtime, avoid surge staffing during renewal peaks, and redeploy talent to higher-value underwriting tasks. You also lower rework costs caused by late-stage discovery of missing years or misaligned claim counts.

Accuracy Improvements: Humans get fatigued; AI does not. Doc Chat applies the same rigor to page 1 and page 1,000, and every answer is source-cited for rapid validation. Fewer missed open suits, fewer overlooked claims, and fewer inconsistencies slipping through to bind reduce loss ratios and E&O exposure.

Consistency and Auditability: Doc Chat enforces your underwriting playbook and creates an auditable trail of every extraction and conclusion. That consistency supports internal QA, reinsurer reviews, and regulatory scrutiny.

Why Nomad Data Is the Best Partner

The Nomad Process: We train Doc Chat on your playbooks, your document types, and your standards. That means your rules for what constitutes an “open” matter, how to treat reserve movements, how to reconcile entity aliases, and how to flag missing years are encoded into the agent. As Nomad notes in “AI for Insurance: Real-World AI Use Cases Driving Transformation,” this is not one-size-fits-all — it is tailored automation that mirrors your workflow.

White-Glove Service, Fast Results: Implementation typically takes 1–2 weeks, not months. Your team can start with drag-and-drop pilots and then integrate via modern APIs into your underwriting platform when you are ready. Nomad runs a white-glove process: interviewing your underwriting staff, encoding their unwritten rules, and validating outputs on your real submissions.

Enterprise-Grade Security: Nomad maintains SOC 2 Type II controls and delivers page-level transparency for every answer. Insurance data is handled with the governance rigor carriers and MGAs expect.

Scale Without Headcount: Doc Chat ingests massive submission volumes without adding bodies. When the market surges or renewal season spikes, your throughput scales instantly. This aligns with Nomad’s documented benefits across insurance use cases, including the GAIG experience in “Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI.”

How Doc Chat Fits Into Your Underwriting Workflow

Pre-Clearance and Triage: Immediately upon receipt, Doc Chat inventories the submission, confirms presence of ACORD forms and loss runs, and flags missing years or carriers. It highlights any mentions of open litigation and lists every prior claim with status.

Risk Selection and Appetite Check: Using your rules, Doc Chat flags thresholds (e.g., frequency over five years, severity thresholds, repeat causes by location). It suggests where higher deductibles, sublimits, or exclusions may be warranted.

Referral and Peer Review: Because every finding is cited, Underwriting Managers can quickly peer-review or route edge cases to counsel. Discrepancies between ACORD answers and loss runs are packaged with citations for broker follow-up.

Bind and Handover: When you are ready, Doc Chat exports a clean, structured record of claims and litigation status into your rating model or underwriting system. The audit trail travels with the account for future renewals.

Capabilities That Matter for GL & Construction, Property & Homeowners, and Specialty & Marine

GL & Construction: Detect references to completed operations suits, subcontractor disputes, and additional insured tender details across contracts and broker narratives. Align OSHA logs, ACORDs, and loss runs to spot frequency issues. Confirm whether an indemnity claim still has active defense counsel engagement.

Property & Homeowners: Link SOV locations to claim histories and litigation mentions involving HOAs, vendors, or contractors. Identify repeat water losses, note whether mitigation disputes are still active, and verify that stated claim counts match carrier-provided loss runs.

Specialty & Marine: Surface cargo, hull, or liability claims across survey reports and P&I correspondence. Identify open recoveries or legal actions, even if referenced obliquely, and tie them back to the insured or affiliated entities via name reconciliation.

Explainability and Trust: Page-Level Citations

Underwriting is a defensible decision-making discipline; you must know why the system concluded what it did. Doc Chat’s answers always cite their source pages. When it flags an open suit or an extra claim not listed on ACORD 125, you can click straight to the paragraph that justifies the finding. This explainability builds trust with your analysts, audit, legal, and reinsurer partners.

Results You Can Measure

Clients see dramatic cycle-time reductions, higher throughput per underwriter, and fewer late-stage surprises. The ability to read everything — and keep it all consistent — is a game changer. As Nomad’s experience shows across claims and medical contexts, machines do not tire or lose focus; they review page 1,500 with the same rigor as page 1. The outcome for underwriting: fewer missed red flags, cleaner pricing decisions, and an improved submission-to-bind ratio on risks that truly fit your appetite.

Common Questions from Underwriting Managers

Does Doc Chat replace underwriters?

No. Doc Chat removes the rote work of document review and data entry, so underwriters can focus on judgment: appetite, terms, and pricing. Think of Doc Chat as a highly capable analyst that assembles the facts consistently and quickly.

Can Doc Chat check external databases?

Doc Chat can integrate with third-party data sources as permitted by your organization, enriching detection and validation. Many carriers begin with documents-in-hand (ACORD, loss runs, litigation summaries, dec pages) and later add external sources for verification and enrichment.

How does Doc Chat handle inconsistent formats?

That’s the point. It reads across formats, links references, reconciles name variations, and infers conclusions when information is scattered. This is why Nomad emphasizes that document understanding is about inference, not just extraction.

What about security and compliance?

Nomad operates with enterprise-grade controls, including SOC 2 Type II. Every answer is traceable back to the page, supporting internal audits and regulatory inquiries.

Implementation: White-Glove, Fast, and Low Disruption

Nomad’s standard engagement begins with real submission files and your underwriting playbooks. The team interviews your top performers to capture unwritten rules and edge cases, then encodes those into Doc Chat. Most customers are live in 1–2 weeks. Early use starts with drag-and-drop, and then IT can enable API integration to your underwriting workbench, content management system, or data lake. As your needs evolve, Doc Chat evolves with you — new prompts, new document types, and expanded enrichment options.

Putting “AI Review for Open Litigation in Submissions” Into Practice

To operationalize this quickly, pick a cohort of in-bound submissions with the following artifacts: ACORD 125/126/140, loss run reports for the last five years across all carriers, any litigation summaries or legal correspondence, the SOV/COPE set (Property), and relevant supplements (OSHA logs for Construction; P&I records for Marine). Configure Doc Chat to output:

1) A consolidated table: claim number, date of loss, line of business, cause, paid/OS/reserve, open/closed, litigation tie (Y/N), and citations.
2) A litigation digest: matter name, forum, counsel, status, last action, and citations.
3) A discrepancy report: differences between ACORD responses and loss run/litigation counts, with citations for broker follow-up.

From there, your analysts validate a sample, tune thresholds, and roll the process to the rest of the queue. Within a renewal cycle, you will see shorter turnaround times, cleaner broker interactions, and improved pricing accuracy.

Governance, Audit, and Future-Proofing

Underwriting organizations that standardize document intelligence gain a durable advantage. Doc Chat institutionalizes your best reviewers’ techniques into a repeatable, teachable process — ensuring new hires follow the same steps and that expertise does not walk out the door. It also creates an auditable substrate of every decision input, making reinsurance reviews and regulatory audits far less painful.

Key Takeaways for Underwriting Managers

- Prior claims and open litigation are the strongest early signals of fit, price, and terms — but they are scattered across submission artifacts.
- Manual review is slow, inconsistent, and costly.
- Doc Chat automates detection, normalization, and reconciliation with page-level citations.
- You get faster cycle times, lower costs, better accuracy, and a defensible audit trail.
- Implementation is white-glove and typically takes 1–2 weeks.

Next Steps

If you are ready to deploy prior claims detection automation underwriting and a rigorous AI review for open litigation in submissions, see how quickly you can get started with Doc Chat for Insurance. Bring two or three live submissions, and within days you’ll see structured results, discrepancy flags, and fully cited answers that your team can trust. For deeper background on Nomad’s approach to complex documents and high-volume insurance workflows, explore: Beyond Extraction, The End of Medical File Review Bottlenecks, and AI for Insurance: Real-World Use Cases.

The underwriting organizations that win the next decade will not be those who read faster — but those who never have to read the same detail twice. With Doc Chat, your team reads once, reasons once, and makes better decisions every time.