Spotting Prior Claims and Open Litigation in Submission Files Using AI (General Liability & Construction, Property & Homeowners, Specialty Lines & Marine) — A Risk Selection Analyst’s Guide

Spotting Prior Claims and Open Litigation in Submission Files Using AI — Built for the Risk Selection Analyst

Every Risk Selection Analyst knows the grind: a 200–1,500 page broker submission package lands on your desk for General Liability & Construction, Property & Homeowners, or Specialty Lines & Marine. Somewhere inside those PDFs are the most consequential signals for underwriting—prior claims and any open litigation that could reshape the loss ratio profile, require exclusions, change retentions, or even trigger a decline. The problem is that those signals rarely appear in one place. They’re scattered, inconsistently formatted, and often buried inside exhibits, attachments, or separate loss run reports and litigation summaries that don’t align neatly with coverage years or named insureds.

Nomad Data’s Doc Chat was built for this exact challenge. Doc Chat is a suite of AI‑powered agents that ingests whole submission files—broker submission packages, loss run reports, litigation summaries, ACORD applications, SOVs, safety manuals, inspection reports, and more—and then surfaces the facts you need in minutes, not days. It standardizes how prior losses and open suits are identified, reconciles entity/name variations, and provides page‑level citations so Risk Selection Analysts can verify every answer quickly. Learn more about Doc Chat for insurance.

Why prior losses and open suits are hard to find in submissions

Across General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine, the signals you need are almost never presented as a neat list. Even when brokers include litigation summaries and loss run reports, the details can be incomplete, stale, or presented in conflicting ways. Risks come in through holding companies with multiple DBAs; claims are attached to different carriers with different date ranges; a single bodily injury loss might appear three different ways across correspondence, defense counsel letters, and spreadsheets. On top of that, pending litigation might sit in a separate addendum or a scanned exhibit.

Line‑of‑business nuances that complicate the search

General Liability & Construction: Prior claims often hide under subcontractor names or historical DBAs. Indemnity/hold‑harmless wording appears in contracts, not in a simple summary. Litigious venues or repeat plaintiff counsel may be hinted at only in suit captions. OSHA 300/300A logs and EMR worksheets speak to frequency and culture. Loss runs vary by carrier and may not align with project timelines or wrap‑up programs (OCIP/CCIP). You may need to reconcile certificate holders, additional insured endorsements, and contract exhibits to assess whether losses flowed to the insured or a subcontractor.

Property & Homeowners: Prior losses are split across per‑location loss runs, appraisal reports, or inspection notes. A water damage claim might be referenced in a contractor’s estimate, but the open subrogation or litigation could be buried in a separate file. Roof age, secondary modifiers, and COPE details live in SOVs and inspection PDFs; addressing prior fire or hail claims requires matching addresses and APNs that aren’t consistently formatted. A CLUE or ISO claim report might not be included, placing more weight on broker‑supplied histories and attachments.

Specialty Lines & Marine: Loss records can span P&I club letters, H&M loss histories, class society certificates, surveyor reports, and voyage logs. Cargo contamination claims may be referenced in survey findings or correspondence rather than in a neat spreadsheet. Open litigation can involve admiralty or international fora, with suit captions that differ from the insured’s registered name. For marine liability, prior crew injury claims or cargo disputes can be scattered across multiple operators or vessels in a fleet, complicating aggregation.

How Risk Selection Analysts handle this manually today

The manual process is thorough, but it’s slow and brittle under volume. Analysts typically:

1) Open the main broker submission package and scan for a table of contents, then hop between ACORD 125/126/140/130 (GL, Property, Cyber/Marine supplements as applicable), SOVs, COPE reports, safety manuals, contracts, and endorsements.
2) Search for “loss,” “claim,” “incident,” and “litigation,” hoping for an index or a clean loss run attachment.
3) Reconcile loss run reports across carriers, date ranges, and named insureds/DBAs/subsidiaries. Normalize claim counts, paid/OS reserves, causes of loss, and status.
4) Cross‑reference litigation mentions in emails, litigation summaries, or counsel letters; then check if any of those suits are still open or if new actions have been filed.
5) Rebuild timelines: incident date → FNOL → suit filed → defense milestones → reserve movements → settlement or judgment.
6) Draft a summary and exceptions list (e.g., missing years, missing carriers, gaps between policy terms, unexplained large OS, active plaintiff counsel, venues of concern).

Even in the best‑run shops, this can take hours per file. Surge volumes or late‑in‑the‑day submissions lead to triage shortcuts. Important context, like whether a “closed” claim was re‑opened or whether a “dismissed” suit was refiled, can be missed. And when every analyst has a different note‑taking style, re‑reviews and peer QA become expensive and inconsistent.

AI review for open litigation in submissions: what Risk Selection Analysts need

Open litigation matters because it reveals unmodeled tail risk, potential for immediate payment activity, and cultural markers like claims handling quality and plaintiff bar attention. To do this well, you need to:

Resolve entity name variations across parent/child/DBA structures and historical carriers.
Identify every mention of suits, demands, arbitrations, and liens embedded in broker submission packages and attachments.
Verify statuses (open vs. settled), venues, counsel, and alleged damages, then align them to policy periods.
Tie litigation events back to matching items in loss run reports and any litigation summaries.
Generate a defensible, page‑referenced narrative you can share with underwriting managers or committees.

Doing all of the above by hand is where cycle time balloons. This is the bottleneck Nomad Data’s Doc Chat eliminates.

How Doc Chat automates prior claims and open litigation detection

Doc Chat is engineered for end‑to‑end document intelligence on insurance files. It ingests entire submission sets—thousands of pages at once—and immediately allows you to ask focused questions like, “List all open GL bodily injury suits in the past five years for any subsidiary or DBA, with dates, venues, plaintiff counsel, and page citations.” Within seconds, you get an answer plus links to the exact pages that support each item.

What Doc Chat does under the hood

1) Massive ingestion and normalization: Drag‑and‑drop your broker submission packages, loss run reports, litigation summaries, SOVs, ACORD forms, inspection reports, contracts, endorsements, OSHA logs, and correspondence. Doc Chat reads scanned PDFs, emails, and embedded images, extracting tables and timelines while preserving the source context.

2) Entity resolution across documents: Doc Chat maps parent/child companies, DBAs, vessel/asset names, and historical carrier references so “Acme Builders LLC,” “Acme Construction,” and “Acme Group Holdings” are recognized as a single family. That means prior claims and lawsuit mentions get aggregated correctly even when naming conventions drift.

3) Loss run and litigation cross‑walk: The system matches litigation mentions back to loss run reports, reconciling claim numbers, dates of loss, causes, and status. It flags mismatches (e.g., a suit that hasn’t appeared on loss runs, or a loss run with a large open reserve but no litigation detail elsewhere).

4) Real‑time Q&A: Ask Doc Chat in plain language to surface specific facts—open suits, repeat plaintiff counsel, venue patterns, subrogation, reserves over thresholds, or recurring causes of loss. Answers arrive with page‑level citations so analysts can verify instantly. This mirrors how Great American Insurance Group described the speed and reliability gains from Nomad when their adjusters needed instant answers across thousand‑page files; see their experience in our write‑up, Reimagining Insurance Claims Management.

5) Playbook‑driven presets: We encode your underwriting guidelines and risk‑selection criteria into Doc Chat presets. Whether you need a GL loss synopsis by cause and severity, a Property prior‑loss map by location, or a Marine litigation digest by vessel—Doc Chat produces standardized outputs on every file. This is the discipline that we describe in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs: turning unwritten human rules into repeatable AI logic.

6) Gap analysis and exceptions: Doc Chat calls out missing years, carrier gaps, mismatched totals, and potential undisclosed claims. It highlights where litigation appears open but is absent from the loss runs, or where high OS reserves suggest pending action despite “closed” narrative language elsewhere.

7) Exportable, auditable deliverables: Create a page‑cited summary you can drop into your underwriting file or committee memo. Export structured fields (claim counts, loss totals, venues, counsel, reserves, statuses) to your underwriting workbench or data warehouse.

Prior claims detection automation underwriting: from tedious hunts to one‑click clarity

When analysts search for “prior claims detection automation underwriting”, they’re looking for reliability as much as speed. Doc Chat is designed to surface every prior loss reference—no matter where it hides—and reconcile it to a single, defensible view.

Here’s what that looks like in practice:

General Liability & Construction: “Summarize all bodily injury claims over $50,000 in paid+OS in the last five years, identify repeat causes (falls from height, struck‑by, third‑party over actions), and show which relate to wrap‑ups vs. practice policies.”
Property & Homeowners: “Identify all prior water, fire, and wind/hail losses by location; show board‑up dates, remediation, and any litigation or subrogation that remained open at the time of prior policy expiry.”
Specialty Lines & Marine: “List crew injuries and cargo contamination claims, map them by vessel and port, and note any litigated matters with open statuses, including counsel and forum.”

These are not generic summaries. They are page‑cited, checklist‑driven outputs aligned to your underwriting appetite, retention strategy, and pricing models. By standardizing the “hardest 10%” of document inference, Doc Chat turns inconsistent submissions into comparable, trusted facts.

Business impact: faster cycle time, better risk selection, fewer surprises

Risk Selection Analysts aren’t measured on how long they read; they’re measured on the quality and defensibility of their recommendations and the portfolio results that follow. Doc Chat delivers tangible improvements across speed, cost, and accuracy.

Speed: Entire submission files—often 500–2,000 pages across attachments—move from days of manual sifting to minutes of automation. Teams eliminate re‑reading and reduce follow‑ups because critical facts are cited and verified. Our clients regularly describe this as shaving days from quote‑to‑bind cycles, aligning with outcomes reported in our AI for Insurance use‑cases article.

Cost: Time saved from manual review becomes capacity you can redeploy to complex risks and portfolio initiatives. In document‑heavy teams, we consistently see 30–60% reduction in time spent per file, mirroring the efficiency opportunities highlighted in AI’s Untapped Goldmine: Automating Data Entry.

Accuracy and consistency: Machines don’t skip pages or tire on page 300. Doc Chat’s page‑level citations and standardized presets give underwriting managers confidence, reduce re‑work, and strengthen audit defensibility.

Portfolio quality: Better visibility into prior claims and open litigation leads to tighter eligibility screens, smarter exclusions, and more accurate pricing. That’s how you protect combined ratios—by catching surprises before they become losses.

The Risk Selection Analyst’s checklist: what to standardize with Doc Chat

Doc Chat can encode your playbook into repeatable steps each time you encounter a GL & Construction, Property & Homeowners, or Specialty & Marine submission:

For General Liability & Construction:

Aggregate loss run reports by entity/DBA and reconcile with ACORD 126, safety plans, and OSHA 300/300A logs.
Identify repeat causes and severe claims, high OS reserves, and re‑opened claims.
Surface litigation summaries, defense letters, and any active case references; record venue, plaintiff counsel, and allegations.
Cross‑walk wrap‑ups, additional insured endorsements, indemnity/hold‑harmless clauses, and subcontractor COIs to determine loss attribution.

For Property & Homeowners:

Map prior losses by location across SOVs; align roof age, protections, and secondary modifiers with frequency patterns.
Spot lingering water or fire claims with remediation gaps or pending subrogation/litigation.
Flag inspection/valuation inconsistencies and align to prior claim narratives.

For Specialty Lines & Marine:

Aggregate H&M/P&I loss records, class certificates, surveyor reports, and voyage logs.
Extract crew injury and cargo contamination histories, linking them to vessels, operators, and ports.
List any open suits, arbitrations, or liens; capture counsel, forum, and status.

What makes Doc Chat different for underwriting teams

Most tools can skim. Few can think like your top analysts. Doc Chat was built to do the latter, by capturing your real rules—the ones that live in notebooks and heads—and operationalizing them. Our process includes:

White‑glove onboarding: We interview your Risk Selection Analysts and underwriting managers to capture the nuances of your eligibility criteria, prior‑loss thresholds, litigation risk triggers, and escalation paths.

Playbook encoding and presets: We turn that expertise into Doc Chat presets that drive consistent, auditable outputs every time—varying by line of business and even program.

1–2 week implementation: Teams start with a drag‑and‑drop interface and can be live in days. Integrations to your underwriting workbench and data warehouse follow quickly, typically within one to two weeks, using modern APIs.

Security and compliance: Nomad Data is SOC 2 Type 2 certified. Every answer includes traceability to the exact page, supporting both internal QA and external audits.

Partnership and evolution: As your guidelines shift, we adjust the presets. You’re not buying generic software; you’re gaining an AI partner that evolves with your underwriting strategy. This approach—and why it outperforms one‑size‑fits‑all tools—is explored further in Beyond Extraction.

From manual to measurable: a day‑in‑the‑life transformation

Before: A Risk Selection Analyst receives a late‑day GL & Construction submission with two PDFs and seven attachments: ACORDs, loss runs, safety manual, three contract exhibits, and a litigation addendum. They skim for losses and suits, spend an hour reconciling carrier loss runs, and plan a second pass tomorrow to validate litigation statuses and note any large OS reserves.

After with Doc Chat: The analyst drops all files into Doc Chat and selects their “GL Prior Loss & Open Litigation” preset. In minutes, Doc Chat produces: (1) a page‑cited loss and litigation table (with venues, counsel, status), (2) a gap analysis for missing years and carriers, (3) repeat cause patterns and any high OS flags, and (4) a list of contract clauses (AI/HH) that could redirect losses. The analyst exports the key fields to the underwriting worksheet and includes the page‑cited narrative in the committee memo.

Tackling real‑world document messiness

Submissions are rarely clean. Doc Chat is designed for the mess:

Unstructured pages and scans: It reads scanned PDFs, images of tables in emails, and attachments saved as pictures instead of spreadsheets.

Name chaos: It resolves entities across parent companies, DBAs, subsidiaries, vessels, and project names so your prior‑loss view is comprehensive.

Version control: When there are multiple loss runs with different cutoff dates, Doc Chat notes the deltas and flags freshness concerns.

Cross‑document logic: It links events across documents—e.g., a slip‑and‑fall suit mentioned in counsel correspondence that’s missing from the loss run, but shows up as an OS reserve increase two months later in a different attachment.

Example questions Risk Selection Analysts ask Doc Chat

You can interrogate the file like you would a seasoned analyst:

“List all open GL lawsuits during the last five years and cite the page for each defendant, venue, and plaintiff counsel.”
“What prior losses exceed $100,000 paid+OS? Group by cause and show trend lines by year.”
“Which locations in the SOV have prior water or fire claims? Provide addresses and remediation completeness.”
“For marine, list crew injury claims by vessel and port, with any open litigation, forum, and counsel.”
“Do any loss run totals disagree with the broker’s summary? Show differences and pages.”

Every answer includes clickable citations to the source page, mirroring the transparent workflow that claims organizations like GAIG valued for speed and trust during their evaluation of Nomad, as described in our webinar replay.

Scaling across lines: GL & Construction, Property & Homeowners, Specialty & Marine

GL & Construction: Use presets that emphasize cause coding, OSHA signals, indemnity/contract risk transfer, wrap‑up attribution, and litigation venues known for severity. Export summarized facts to your pricing models and eligibility rules.

Property & Homeowners: Standardize location‑level loss histories and remediation status. Align inspections and valuations to prior events. Flag litigation related to contractor disputes or subrogation drag.

Specialty & Marine: Normalize P&I and H&M histories, link them to vessels and operators, and surface open suits with counsel and forum. Detect repeat contamination or handling issues by port or voyage type.

How Doc Chat plugs into your underwriting ecosystem

Start fast with drag‑and‑drop. Then integrate.

Quick start: Teams can pilot by simply uploading PDFs. No IT lift required.

Integrations: Within 1–2 weeks, connect Doc Chat outputs to your underwriting workbench, data lake, or case management system. Structured exports include prior losses, litigation details, and gap flags.

Extend with optional data: Many carriers enrich Doc Chat outputs with their internal historical policy/claim systems or authorized third‑party datasets (e.g., ISO claim reports, inspection vendors). Doc Chat’s architecture is built for secure, governed enrichment.

Measuring value in underwriting

Doc Chat delivers quantifiable impact in the underwriting operation:

Throughput: Analysts process more files per day while giving more attention to complex placements.
Quote‑to‑bind time: Faster fact‑finding drives quicker quotes and cleaner referrals to underwriting managers.
Loss ratio protection: Better detection of open litigation and undisclosed prior losses prevents adverse selection.
E&O risk reduction: Page‑level citations underpin defensible decisions and cleaner audit trails.
Talent leverage: New analysts follow the same playbook as veterans, speeding ramp‑up and standardizing quality.

Why Nomad Data’s Doc Chat is the right partner

Nomad Data is purpose‑built for the insurance document landscape. Our differentiators matter to Risk Selection Analysts:

Volume: Doc Chat ingests entire submission files—thousands of pages—without adding headcount.

Complexity: It finds exclusions, endorsements, loss references, and litigation mentions hidden in dense, inconsistent policies and attachments, enabling more accurate underwriting decisions.

The Nomad Process: We train Doc Chat on your playbooks and standards for each line of business. The result is a personalized solution rather than generic software.

Real‑time Q&A: Ask “Show all open suits” or “List all losses over $100,000 with causes” across massive submission sets and get immediate, cited answers.

Thorough & complete: Doc Chat surfaces every reference to coverage, liability, and damages so nothing important slips through the cracks.

Security & trust: SOC 2 Type 2, page‑level citations, and enterprise controls. These are table stakes for regulated operations.

If you want a deeper dive into how AI eliminates document review bottlenecks and creates consistent outputs, see The End of Medical File Review Bottlenecks—the same speed and accuracy principles apply to underwriting submissions.

Implementation in 1–2 weeks with white‑glove service

We make it easy to get started and hard to go back to manual. Our white‑glove team does the heavy lifting:

• Discovery: We interview your Risk Selection Analysts to capture playbooks by line of business and program.
• Preset configuration: We encode your prior‑loss and open‑litigation rules as reusable Doc Chat presets.
• Pilot: Analysts immediately upload real submissions and validate results against known answers.
• Integrate: In 1–2 weeks, we connect Doc Chat outputs to your underwriting workbench and reporting.

From day one, your team can ask live questions of their own files and see page‑linked answers—exactly the moment where skepticism turns into confidence during pilots.

A final word to Risk Selection Analysts

Your value is in judgment—triaging gray areas, seeing patterns across causes and venues, and recommending the right terms or declines. The least valuable use of your expertise is hunting for facts hidden in sprawling PDFs. Doc Chat moves you from manual searching to decision‑ready insights, standardizing prior‑loss and open‑litigation detection across General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine. It’s how you protect the portfolio while moving faster than the market.

Ready to see an AI review for open litigation in submissions on your next file, complete with page‑level citations and export‑ready fields? Explore Doc Chat for Insurance and transform how you find prior losses and open suits inside broker submission packages.