Spotting Prior Claims and Open Litigation in Submission Files Using AI for General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine - A Guide for Risk Selection Analysts

Spotting Prior Claims and Open Litigation in Submission Files Using AI – What Risk Selection Analysts Need Now

Every Risk Selection Analyst knows the feeling: a new broker submission lands with hundreds of pages across PDFs, spreadsheets, and emails. Somewhere in those broker submission packages, litigation summaries, and loss run reports lies the truth about prior claims, open cases, reserves, and disputes. But under deadline pressure, manual review risks missing items that materially change the risk profile. That is the core challenge this article tackles.

Nomad Data's Doc Chat was built to solve precisely this problem. It is a suite of purpose-built, AI-powered agents that can ingest entire submission files, extract and cross-check details about prior claims and active litigation, and answer precise questions in seconds. With Doc Chat, a Risk Selection Analyst can run an AI review for open litigation in submissions and find every reference to past losses, lawsuits, or coverage disputes with page-level citations and real-time Q&A. Learn more about Doc Chat for Insurance here: Doc Chat by Nomad Data.

Why Prior Claims and Litigation Matter So Much in Risk Selection

In risk selection, history is the best predictor of future loss. For General Liability & Construction, repeated third-party injury claims or contractual indemnity disputes foreshadow future frequency and severity. For Property & Homeowners, unmitigated water losses or prior fire claims may signal systemic issues. For Specialty Lines & Marine, prior cargo thefts, hull damage, or contract disputes can change warranties, deductibles, and even appetite. Accurately identifying all prior claims and ongoing litigation is not optional; it is the foundation of sound pricing, coverage terms, and appetite decisions.

The complicating factor is volume and variability. Today’s submission packages are sprawling: ACORD applications, COIs, OSHA logs, SOV spreadsheets, engineering reports, contracts, endorsements, appraisals, hull and cargo surveys, P&I letters, FNOL forms, ISO claim reports, and more. Details about prior incidents often appear in side notes, footers, or scanned correspondence. Open litigation might be referenced in a single sentence in a litigation summary, a claim note inside a loss run, or in an email inside the submission. Without automation, critical facts are easily missed.

The Submission Reality by Line of Business

General Liability & Construction

GL & Construction submissions typically include ACORD 125/126, subcontractor agreements, additional insured endorsements, hold harmless clauses, jobsite safety manuals, OSHA 300/300A logs, incident reports, and multi-year loss runs. Prior claims can hide in contractor agreements, incident logs, or secondary schedules appended to loss run PDFs. Open litigation might surface as a pending docket number referenced once in a broker email or a defense counsel name on a claim note. Discrepancies are common: a loss run indicates a bodily injury claim still open, while a broker narrative says closed. Which is true?

Property & Homeowners

Property submissions mix SOV spreadsheets, roof and plumbing reports, cat modeling exports, inspection photos, valuations, replacement cost estimates, and multi-carrier loss runs. Prior water or fire incidents might be noted in inspection comments but not reflected in the loss run totals due to sublimits or a different policy number. Open litigation can arise from denied claims, public adjuster disputes, or repair contractor lawsuits. A single reference in a correspondence attachment may be the only mention that a suit remains active.

Specialty Lines & Marine

Specialty and Marine submissions can include hull surveys, class certificates, P&I Club letters, cargo manifests, bills of lading, charterparty agreements, crew statements, voyage logs, and repair invoices. Prior claims may be referred to by hull number or voyage ID rather than account name. Litigation could be described in a port state control report or a charterparty addendum with non-standard naming. Loss runs might not align cleanly to vessel identifiers across fleets, creating reconciliation headaches that hide real exposure.

How Risk Selection Analysts Handle It Manually Today

Even the best teams follow a largely manual approach, which is meticulous but slow and brittle under pressure:

Typical manual steps include:

• Collecting and indexing all documents: broker submission packages, litigation summaries, loss run reports, ACORD apps, SOVs, contracts, endorsements, surveys, photos, emails.
• Reading each document line by line to locate names, policy numbers, claim numbers, dates of loss, reserves, litigation notes, and venues.
• Reconciling loss run totals with narrative summaries and email representations from brokers or insureds.
• Searching for court or docket references by hand, sometimes leaving the submission to check public portals or internal systems.
• Copying details into spreadsheets for comparison and trend analysis across carriers, lines of business, or time periods.

This process can take hours to days per account. It is also vulnerable to human fatigue: after the third 300-page PDF of the day, even an expert can overlook an embedded reference to a pending suit or a reopened claim. Peaks in submission volume make consistency even harder to maintain, creating unnecessary risk selection uncertainty.

The Nuances That Make Detection Hard

In reality, prior claims and open litigation rarely live in neat, structured fields. They are inferred from context and scattered across files. Three tricky patterns appear again and again:

1) Mismatched identifiers — A GL claim may be filed under a project entity while the loss run aggregates under the parent company. A marine claim may be tracked by hull number, while the broker narrative uses vessel name aliases. A property claim might list an old address prior to a location renumbering in the SOV.

2) Implicit status cues — Words like pending, defense counsel assigned, mediation scheduled, or reserve adjusted often indicate open litigation, but they live inside adjuster notes or email threads, not a single field.

3) Cross-document breadcrumb trails — The litigation summary references a docket, the loss run shows a reserve increase, the ACORD remarks mention a denied claim now in appraisal, and a separate letter indicates a notice of suit. Humans must connect these dots; manual errors are common.

AI Review for Open Litigation in Submissions: What Doc Chat Changes

Doc Chat approaches risk selection like a seasoned analyst who never tires. It ingests the full submission and applies your playbooks to identify, extract, and reconcile litigation and prior claim signals across every page and file type. Then, it lets you ask natural-language questions and receive instant, page-linked answers you can verify at a click.

Key ways Doc Chat transforms AI review for open litigation in submissions:

• Whole-file ingestion at scale — Doc Chat reads thousands of pages across PDFs, Word docs, emails, spreadsheets, and images. It captures loss development, venue, counsel, reserves, demand amounts, and status language wherever they appear.
• Cross-document entity resolution — The system ties together claimants, project entities, vessel identifiers, policy numbers, and addresses even when formats differ, enabling precise matching of loss events and litigation mentions.
• Contextual inference — It recognizes that mediation scheduled next quarter plus a reserve increase likely means a file is still open, even if the status field says pending closure.
• Page-linked explainability — Every answer includes a link back to the exact sentence and page. That creates a defensible trail for underwriting committees and auditors.
• Real-time Q&A — Ask: List all lawsuits still open with reserves over 100k or Identify any property losses tied to water damage in the past 36 months and receive immediate, citation-backed responses.

For more on why document AI must go beyond simple extraction to inference, see Nomad Data's perspective: Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

Prior Claims Detection Automation Underwriting: From Days to Minutes

Doc Chat delivers prior claims detection automation underwriting in a way that mirrors how top Risk Selection Analysts think, then scales it to every submission:

1) Normalize and reconcile loss runs — The agent extracts carrier, policy period, claim number, claimant, cause of loss, paid, reserve, and status across different formats. It aligns these to your account, project, vessel, or property identifiers and flags mismatches.

2) Surface hidden references — The system finds prior incidents and disputes in OSHA logs, contractor correspondence, SOV footnotes, hull and cargo surveys, and appraisal reports, then ties them back to the loss history.

3) Detect litigation signals — It pulls out docket numbers, venues, counsel names, hearing dates, and settlement language. It spots phrases suggesting open status or reopen risk: mediation set, trial date pending, tender rejected, notice of appeal filed.

4) Build a defensible summary — Doc Chat produces a concise account of all prior claims and any open litigation, complete with page-linked citations for underwriting committees and file notes. You can export structured fields directly to underwriting systems.

To see how large insurers use page-level citations to accelerate complex reviews, watch the GAIG case study summary: Great American Insurance Group accelerates complex claims with AI.

What the Process Looks Like in Practice

Step 1: Ingest and classify the entire submission

Drag and drop all materials: broker submission packages, litigation summaries, loss run reports, ACORD forms, SOVs, OSHA logs, contracts, endorsements, inspections, hull surveys, cargo manifests, photos, and email threads. Doc Chat classifies and indexes everything instantly.

Step 2: Ask targeted questions

Doc Chat is built for real-time Q&A. Examples a Risk Selection Analyst across GL & Construction, Property & Homeowners, and Specialty Lines & Marine might ask include:

• List all open or pending lawsuits, with claim numbers, venues, reserves, and last activity date.
• Summarize prior claims by cause of loss and trend paid plus reserve across the last 5 policy periods.
• Identify any bodily injury claims tied to subcontractor work with additional insured complications or tender disputes.
• Extract all water-related property losses by address and note any mitigation steps referenced in inspections.
• Cross-check vessel hull survey notes with any claims or repairs referenced in loss runs; flag discrepancies and dates.
• Are there any references to appraisal, arbitration, or denied claims under dispute?
• Do the OSHA 300/300A logs show recordables that do not appear in the GL loss runs?

Step 3: Validate with citations and export

Each answer includes page-linked citations. With one click, you can verify the sentence in context. Then export structured results into your underwriting system or a spreadsheet for committee review. If something looks concerning, ask a follow-up question and Doc Chat updates the summary on the fly.

Document Types Doc Chat Reads Without Blinking

Risk Selection Analysts juggle a broad document landscape. Doc Chat was designed for the diversity and noise of real-world submissions:

Core submission materials — Broker submission packages, ACORD 125/126/140, statements of values, litigation summaries, loss run reports, COIs, ISO claim reports, FNOL forms, OSHA 300/300A logs, risk control and engineering reports, valuations, inspection photos, appraisals, policy forms, endorsements, and binders.

Construction and contractual artifacts — Subcontractor agreements, master service agreements, additional insured endorsements, indemnity and hold harmless language, project schedules, incident reports, safety manuals, site-specific JHAs.

Marine and specialty — Hull and cargo surveys, class certificates, P&I letters, charterparty agreements and addenda, bills of lading, voyage logs, port state control reports, repair invoices, crew statements, manifests, and customs documentation.

Doc Chat resolves entity aliases, normalizes identifiers, and ties references together to ensure that small clues in one document become detectable signals when mapped across the whole file.

Business Impact: Faster, Cheaper, More Accurate Risk Selection

Automating the hunt for prior claims and open litigation produces measurable improvements:

Time savings — End-to-end review of a complex submission moves from days to minutes. Large claim files that previously required a dedicated reviewer can be processed instantly, enabling same-day underwriting decisions and higher throughput during peak season.

Cost reduction — By removing hours of manual reading and spreadsheet reconciliation, teams curb overtime and avoid needlessly scaling staff to handle surge volumes. Reinspections or after-the-fact corrections decrease as accuracy rises.

Accuracy and leakage — Doc Chat reads every page with identical rigor, catching open litigation references, reopened claims, and misaligned loss runs that human reviewers might miss late in the day. That reduces claims leakage and pricing drift, supporting better combined ratios.

Consistency and defensibility — Page-level citations create a clean audit trail. Committees can review both the conclusion and the underlying evidence quickly, standardizing risk selection decisions across desks and locations.

For a deeper dive into the step-change in throughput and consistency modern document AI delivers, explore: Reimagining Claims Processing Through AI Transformation and AI's Untapped Goldmine: Automating Data Entry.

Examples Across Lines of Business

GL & Construction: Subcontractor injury with tender dispute

A submission includes ACORD forms, a 5-year loss run, OSHA logs, and a master subcontractor agreement. Doc Chat surfaces a single note in the loss run indicating tender rejected by upstream carrier and a separate email indicating mediation scheduled next quarter, with reserves recently increased. The broker narrative had marked the claim as closed. With evidence linked to pages, the Risk Selection Analyst can recalibrate terms, request clarification, or decline.

Property & Homeowners: Silent water losses in inspection notes

A multi-location SOV and inspection set mentions repetitive kitchen leaks in two buildings. The loss run aggregates only one water claim. Doc Chat cross-references inspection comments to note three additional incidents recorded as repairs rather than claims. It flags possible underreporting and recommends follow-up questions plus an endorsement adjusting water damage deductibles.

Specialty Lines & Marine: Hull damage and unresolved litigation

A marine account shows a hull claim paid last year. A separate hull survey contains a footnote referencing a second incident tied to a voyage ID; a charterparty addendum references ongoing litigation with a port authority. Doc Chat connects the voyage ID to the hull number and identifies a pending suit referenced in an email that did not make it into the summary. That materially changes appetite and pricing discussions.

Why Nomad Data and Doc Chat Are the Best Fit for Risk Selection Analysts

Most tools stop at generic summarization. Doc Chat goes further because it was designed to operate like a team of underwriting analysts working at machine speed:

Volume without headcount — Ingest entire submission files, thousands of pages at a time, and return answers in minutes. This removes backlogs entirely.

Complexity made simple — Dense and inconsistent policies, endorsements, contracts, and surveys are where exclusions, warranties, and litigation status hide. Doc Chat digs them out and ties them together.

Trained on your playbooks — We encode your underwriting criteria, appetite, and red-flag definitions, so the agent mirrors your Risk Selection Analyst workflow and standards across GL & Construction, Property & Homeowners, and Specialty Lines & Marine.

Real-time Q&A with citations — Ask any question and get a defensible answer with a link back to the source page. This supports underwriting committees, compliance, and reinsurers.

White glove partnership — You are not buying off-the-shelf software. Nomad co-creates the solution with you, integrating your rules and documents. Our team provides ongoing support so Doc Chat evolves with your book and appetite.

Implementation in 1–2 weeks — Start with drag-and-drop uploads. As usage grows, we integrate with your document stores and underwriting systems using modern APIs. Most teams are fully up and running within two weeks.

For more on the end of document review bottlenecks and why page-linked explainability changes the game, see: The End of Medical File Review Bottlenecks.

Security, Auditability, and Trust

Insurance documentation is sensitive. Nomad Data is built for enterprise-grade confidentiality and governance. We support page-level explainability, time-stamped audit trails, and secure deployment. Nomad maintains SOC 2 Type 2 certification, giving IT and compliance teams confidence that the platform meets stringent controls for data handling and processing. Answers are always linked to source pages so underwriters, managers, and auditors can verify the context instantly.

From Pilot to Scale: A 1–2 Week Playbook

Nomad’s approach is intentionally simple and fast, designed around your Risk Selection Analysts and their line-of-business nuances.

Week 1: Prove value on live submissions

We start by loading real broker packages for GL & Construction, Property & Homeowners, and Specialty Lines & Marine. Analysts ask day-to-day questions: Where are open lawsuits? Which water losses are missing from the loss run? Are there unresolved tender disputes in the GL submissions? Within minutes, the team sees accurate answers with links to source pages.

Week 2: Tune to your playbooks and integrate

We encode appetite triggers, refer/decline criteria, and red flags into Doc Chat presets. Then we integrate with your DMS or intake workflows to eliminate upload friction. Analysts continue to perform AI review for open litigation in submissions and prior claims detection automation underwriting daily, but within a fully embedded process.

Because Doc Chat is built for insurance, teams routinely go from proof-of-value to everyday use in under two weeks. The experience mirrors what Great American Insurance Group saw when adopting AI for complex claims: page-linked, verifiable answers in seconds that build trust and speed. See the story: Reimagining Insurance Claims Management.

Operationalizing Detection: What Good Looks Like

Risk Selection Analysts using Doc Chat consistently adopt a high-velocity, defensible workflow:

Submission triage — Immediately run a preset that extracts all prior claims and open litigation; flag discrepancies between broker narrative and loss runs or litigation summaries.

Focused scrutiny — Deep-dive only into files with red flags: pending suits over your reserve threshold, frequency spikes, repeat water or BI claims, tender rejections, arbitration or appraisal mentions.

Standardized summaries — Produce a Doc Chat-generated prior loss and litigation summary for every file, with citations. Export structured fields to your underwriting system.

Committee-ready documentation — Use the citation links during peer review to confirm facts quickly and set terms with confidence. That audit trail is invaluable with reinsurers and regulators.

Answers to Common Questions

Will Doc Chat hallucinate details? In extraction scenarios tied to a defined submission, modern AI systems like Doc Chat rarely invent facts. Outputs reference the provided documents and always include page citations for verification.

What if my brokers use wildly different formats? That is the norm. Doc Chat was built for inconsistent, messy submissions and infers structure from context, not templates. The platform thrives on variability.

Can it look beyond the submission? Many clients connect Doc Chat to internal systems or third-party sources for enrichment and verification. Where appropriate, Doc Chat can cross-check internal claim histories or ISO claim reports to confirm completeness and status.

Is it going to replace our analysts? No. Doc Chat takes on the rote reading and reconciliation work so Risk Selection Analysts can spend time on judgment, negotiation, and portfolio strategy.

The Competitive Edge for Risk Selection Analysts

The best Risk Selection Analysts do two things better than anyone else: they see the whole picture of prior claims and litigation, and they act on it faster than competitors. Doc Chat gives you that edge consistently across GL & Construction, Property & Homeowners, and Specialty Lines & Marine. When every submission receives complete, citation-backed review in minutes, you can price precisely, set terms confidently, and walk away from misrepresented risks before they become losses.

The firms adopting Doc Chat are not simply reading faster. They are standardizing excellence: every submission, every time, with no blind spots. That is how you reduce leakage, protect combined ratio, and expand profitable growth.

Get Started

If you are ready to operationalize AI review for open litigation in submissions and embed prior claims detection automation underwriting into your daily risk selection workflow, explore Doc Chat for Insurance. In 1–2 weeks, your team can move from manual hunts to minute-by-minute certainty, with white glove support from a partner that builds solutions around your documents, rules, and goals.