Spotting Prior Claims and Open Litigation in Submission Files Using AI for Underwriters

Underwriters in General Liability and Construction, Property and Homeowners, and Specialty Lines and Marine face a persistent and costly challenge: critical details about prior claims and open litigation are scattered across sprawling broker submission packages, loss run reports, and litigation summaries. These data points often hide in footers, email threads, endorsements, attachments, or inconsistent broker narratives. Missing them skews risk selection, pricing, and referral decisions. Nomad Data’s Doc Chat addresses this head-on by ingesting entire submission files and instantly surfacing prior losses and active litigation with page-level citations, freeing underwriters to make faster, more defensible decisions.

Doc Chat by Nomad Data is a suite of purpose-built, AI-powered agents that reads like your best underwriting assistant. It ingests thousands of pages across submission packets, extracts key fields, performs entity resolution across DBAs and subsidiaries, and answers questions in natural language. With Doc Chat, an underwriter can ask: Flag any open lawsuits, list prior GL and Property claims by policy year with total incurred and status, or reconcile the loss runs against ACORD statements of no known losses. The result: fewer blind spots, higher throughput, tighter underwriting.

AI review for open litigation in submissions: why this matters now

The surge in documentation volume has made manual review untenable. Construction firms submit binders including ACORD 125, ACORD 126, subcontractor agreements, certificates of insurance, additional insured endorsements, and OSHA 300 and 300A logs. Property and Homeowners packages include ACORD 125 and 140, statements of values, inspections, appraisals, catastrophe modeling outputs, and prior carrier loss runs. Specialty Lines and Marine submissions may include ACORD 152, bills of lading, charter parties, class surveys, port state control records, and P&I or inland marine loss histories. Within these materials, references to lawsuits, notices of claim, demand letters, and attorney correspondence can be easy to miss. An AI review for open litigation in submissions ensures that nothing important slips through the cracks.

Nomad Data’s approach is designed for this complexity. As highlighted in the company’s perspective on the difference between web and document scraping, the value is not just extracting what sits plainly on a page; it is inferring what matters by connecting breadcrumbs spread across a document set. See Nomad’s take in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs here.

The nuances of prior claims and litigation across key lines of business

General Liability and Construction

GL and construction risks often involve complex contracting structures with multiple insured entities, subcontractors, and additional insured endorsements. Prior claims may reside under a parent company’s name while open litigation appears under a DBA or joint venture. Underwriters must reconcile subcontractor indemnity agreements, OCIP or CCIP documentation, and certificates of insurance against claims and suits naming the insured as an additional insured. Prior claims include bodily injury, completed operations, product liability, and premises occurrences; open litigation may involve OSHA investigations, third-party injury suits, construction defect allegations, or contractual disputes. Any gap between what the broker asserts and what loss runs or litigation summaries reveal can materially affect risk selection, terms, deductibles, and pricing.

Property and Homeowners

For property risks, prior claims and suits affect catastrophe modeling assumptions, deductibles, sublimits, and inspection priorities. Loss histories may cover water damage, fire, theft, wind or hail, and catastrophe events. Open litigation may refer to coverage disputes, appraisal clauses, or assignment of benefits cases. Underwriters must triangulate loss run reports, ACORD 140 statements, inspection reports, appraisals, and COPE data with broker narratives. Household names and individual insureds complicate matching when addresses or named insured formats vary. Seasonal homes, short-term rentals, or mixed-use properties can lead to scattered references in emails or addenda. Missing a single open lawsuit can misstate loss propensity and inflate loss ratio risk.

Specialty Lines and Marine

Specialty and marine underwriting frequently spans diverse document types: ACORD 152 (inland marine), cargo manifests, surveyor reports, bills of lading, charter parties, and class or compliance certificates. Losses may involve cargo contamination, delay, heavy equipment transit, hull damage, or warehouse legal liability. Open litigation might live in obscure references to port disputes, limitation of liability actions, or civil cases in foreign jurisdictions. Underwriters must unify names across vessel operator, owner, beneficial owner, and charterer, and interpret claims that arise under differing legal regimes. A prior claim or active suit hidden in a survey footnote or email appendix materially alters terms and conditions, coverage triggers, and pricing.

How this review is handled manually today

Most underwriters still rely on painstaking manual triage of broker submission packages. They open PDFs, scroll through attachments, and perform Ctrl-F searches on phrases like claim, loss, suit, litigation, demand, summons, or docket. They reconcile loss run reports by policy year, hunting for incurred, paid, and outstanding reserves, cause of loss, date of loss, and status. They cross-compare broker litigation summaries with demand letters, counsel correspondence, and settlement notes. Underwriters may check internal data sources, email chains, and occasionally public dockets, then copy key facts into an underwriting worksheet or rating model.

This manual workflow consumes hours per submission file and still leaves gaps. Document structures vary widely; ACORD forms are not always complete; loss runs can be missing years or carriers; litigation details may be referenced once in a footnote, redacted, or embedded as a scanned image. When volumes spike, the odds of missing a prior claim or an active suit increase. Even expert reviewers struggle to maintain consistency across hundreds of pages. As Nomad Data’s clients describe in their experience with complex claims files, speed and accuracy degrade under sheer volume; see the discussion of Great American Insurance Group’s results in this webinar recap here.

Why manual review misses critical signals

Most submission files suffer from inconsistency and scale. Information needed for prior claims detection and litigation screening is rarely presented as a single field. Instead, it emerges from cross-referencing loss run reports, broker narratives, litigation summaries, and attachments. Subsidiary names, DBAs, and joint ventures obscure entity matching. Dates of loss and reopening information may be inconsistent. Adjuster notes and counsel email threads use nonstandard language. Some attachments are scans that defeat basic search. All this creates the perfect trap for human error. As Nomad Data argues, the real job is to infer meaning across documents, not to scrape obvious fields; see their deep dive on document inference here.

How Doc Chat automates prior claims detection and open litigation screening

Nomad Data’s Doc Chat transforms this task from days to minutes. Built for insurance workflows, Doc Chat ingests complete submission files — broker submission packages, litigation summaries, loss run reports, ACORD forms, statements of values, OSHA logs, inspection reports, subcontractor agreements, charter parties, cargo surveys, and more — then performs a deep review with page-level traceability. Underwriters can ask questions in plain language and receive answers instantly along with citations to the exact pages. Learn more about Doc Chat for insurance on the product page here.

End-to-end ingestion and normalization

Doc Chat accepts mixed formats, including native PDFs, scanned images, spreadsheets, and email exports. It normalizes text with OCR, resolves low-quality scans, and tags document types. The system extracts key underwriting fields from ACORD 125, 126, 140, and 152; harvests cause of loss, date of loss, paid, incurred, and status from loss runs; and identifies references to suits, demand letters, summons, and settlement status within broker narratives and counsel correspondence. It cross-links references so that a claim ID mentioned in an email can be reconciled with the same claim on a loss run.

Entity resolution and synonym handling

To avoid missed hits, Doc Chat performs fuzzy entity resolution across named insureds, DBAs, parent companies, subsidiaries, construction joint ventures, and vessel ownership structures. It recognizes address-normalized matches, common misspellings, and corporate suffix variants. For marine risks, it maps vessel names to operators or beneficial owners; for construction risks, it maps joint ventures to parent contractors and cross-references subcontractor certificates and endorsements.

AI review for open litigation in submissions

The system flags litigation signals that hide in free text or footers, including references to complaint, petition, statement of claim, docket, index no., case no., summons and complaint, suit filed, notice of claim, arbitration, mediation, and settlement-in-principle. It extracts parties, venue, filing date, counsel, matter type, and current status when present, then compiles an Open Litigation Summary with links back to source pages so the underwriter can verify instantly.

Prior claims detection automation underwriting

Doc Chat builds a Prior Claims Summary that aligns to underwriting playbooks by line of business. It groups claims by coverage part and policy year, captures cause of loss, location, paid and incurred amounts, and whether the loss is open or closed. It compares this against broker attestations and highlights discrepancies such as statements of no known losses contradicted by loss runs or email threads. It can generate a roll-up view at the account level and a drill-down to each claim with citations.

Real-time Q&A and audit-ready outputs

Underwriters interact via simple prompts: Show open suits; list BI or construction defect claims with total incurred over a threshold; reconcile prior carrier names; or provide a timeline of all litigation mentions. Every answer includes page-level citations, which speeds peer review, underwriting audits, and regulator questions. These citations also build trust across underwriting, compliance, and reinsurance partners, echoing the page-citation approach discussed in Nomad’s claims webinar recap here.

Optional connections to your systems and licensed data

Doc Chat can integrate into intake portals, underwriting workbenches, and rating tools via APIs. Where permitted and licensed by the carrier, it can also interface with internal systems or third-party datasets for verification. The goal is to centralize prior claims and litigation insight inside the underwriting workflow without forcing underwriters to swivel across systems.

What this automation looks like in practice

Here is a typical underwriter flow with Doc Chat on a General Liability and Construction submission package:

1. Drag-and-drop the broker submission PDF bundle and loss run spreadsheets.
2. Doc Chat classifies documents, runs OCR, and extracts key fields from ACORD 125 and 126, subcontractor schedules, certificates, and endorsements.
3. It builds a Prior Claims Summary grouped by policy year and cause of loss, with paid, incurred, and open status.
4. It scans for litigation signals and compiles an Open Litigation Summary with parties, venue, case posture, and citations.
5. Underwriter asks: Compare broker statement of no known losses to the loss runs; highlight any claim exceeding 50,000 incurred; list all suits referencing construction defect or contractual indemnity.
6. Doc Chat returns answers with links to the source pages for verification and an exportable table for the underwriting worksheet.

For Property and Homeowners, Doc Chat aligns loss histories to locations, matching addresses across ACORD 140, SOV, and inspections. It flags repeated water damage or fire losses, identifies appraisal-related disputes and litigation, and correlates loss frequency and severity with COPE details. For Specialty and Marine, it maps claims to cargo type or equipment category, extracts survey findings, and detects litigation tied to charter party obligations or port disputes, even when referenced sparsely.

Potential business impact: speed, cost, and accuracy

Underwriting teams adopting Doc Chat report dramatic cycle-time reductions. Submission reviews that previously required hours per file can be completed in minutes, with evidence-backed summaries suitable for referral, pricing, and capacity decisions. Because Doc Chat reads every page with consistent focus, it maintains accuracy at scale. This mirrors the performance shift described in Nomad’s write-ups on medical file review and claims transformation, where summaries that once took days or weeks are generated in minutes; see The End of Medical File Review Bottlenecks here and Reimagining Claims Processing Through AI Transformation here.

Beyond time savings, the cost impact is significant. Underwriters and underwriting assistants spend less time on document hunting and more time on risk selection, pricing, negotiation, and broker communication. Improved accuracy means fewer pricing errors, more appropriate deductibles and terms, and fewer post-bind surprises. With consistent extraction and audit trails, carriers tighten underwriting governance and reduce rework and leakage. In short, prior claims detection automation underwriting shifts teams from manual processing to informed decision-making.

Why Nomad Data’s Doc Chat is the best-fit solution

Nomad Data purpose-built Doc Chat for insurance document complexity. The solution ingests entire claim or submission files without adding headcount and delivers instant answers across massive document sets. It surfaces every reference to coverage, liability, damages, and litigation so underwriters can trust that no signal is left behind. Just as important, Nomad’s implementation is fast and white glove: most teams are live in one to two weeks with workflows tailored to their submissions and underwriting playbooks. Learn more about Doc Chat’s insurance capabilities here.

Security and governance are built in. Nomad operates with enterprise-grade security, including SOC 2 Type 2, and provides page-level traceability for audit and compliance. Teams get transparency into where each fact came from, echoing the explainability standards highlighted by Great American Insurance Group’s experience here. For organizations that need more than generic summarization, Nomad’s customization and service model ensure Doc Chat follows your rules, not a one-size-fits-all template.

Underwriting-ready outputs you can export and defend

Doc Chat produces structured outputs that drop directly into underwriting worksheets, workbenches, and rating tools. Typical deliverables include:

• Prior Claims Summary by policy year, coverage part, cause of loss, date of loss, paid, incurred, and open/closed status.
• Open Litigation Summary with parties, venue, cause, filing date, status, counsel, and page-level citations.
• Reconciliation report comparing broker statements to loss runs and litigation mentions, with discrepancy flags.
• Entity resolution map connecting named insureds, DBAs, subsidiaries, and joint ventures for account-level clarity.
• Line-of-business checklists that confirm required documents are present: ACORD 125/126/140/152, loss runs, SOV, OSHA logs, subcontractor agreements, inspections, surveys, and appraisals.

Because every field includes citation links back to the submission file, these outputs are not just fast; they are defensible during audit, peer review, or regulatory inquiries.

Sample prompts underwriters use on Day One

Doc Chat’s real-time Q&A turns submission review into a set of questions rather than a hunting expedition. Common prompts include:

• List all prior GL and Property claims by policy year with paid, incurred, and open/closed status. Include page citations.
• Flag any open litigation or open claim matter. Provide case posture, venue, and first mention, with citations.
• Compare broker attestation of no known losses to the loss run reports. Highlight discrepancies.
• Identify any construction defect or completed operations claims in the last 10 years. Summarize severity and resolution.
• For SOV locations with repeat water losses, list address, date of loss, cause, and total incurred.
• Map named insureds, DBAs, and subsidiaries. Note where claims are tied to a different entity name.
• For marine cargo, list all cargo damage or delay claims by commodity and route, plus any related suits.

Applying Doc Chat across GL and Construction, Property and Homeowners, and Specialty and Marine

GL and Construction underwriting

Doc Chat checks subcontractor agreements for indemnity, ties additional insured endorsements to certificates, and aligns loss runs to coverage parts. It spots construction defect allegations buried in correspondence and correlates OSHA citations with injury claims. It flags repeated premises incidents and highlights reserve growth in outstanding claims. For OCIP or CCIP, it verifies that covered projects and terms align with prior losses and known lawsuits.

Property and Homeowners underwriting

Doc Chat resolves addresses across ACORD 140, SOV, and inspection reports; flags repeat water damage or fire; and notes litigation involving coverage disputes or appraisal. It correlates building characteristics with loss histories, enabling better deductibles, sublimits, and inspection requirements. For high-value homes, it surfaces appraisals, prior renovations, and any suits tied to contractor disputes or water intrusion.

Specialty and Marine underwriting

Doc Chat extracts insights from survey reports, bills of lading, charter parties, and class documents, pulling out claims connected to specific cargo types, equipment classes, routes, or port operations. It flags references to limitation proceedings or port state control detentions that signal legal or operational risk and assembles litigation mentions that could reappear as recurring loss types.

Linking to your underwriting playbook and standards

The Nomad process begins with your playbooks. Doc Chat is trained on your underwriting guidelines, document checklists, and referral rules so that outputs align with your standards. Rather than generic summaries, you receive underwriting-ready analyses that follow your checklist: loss history thresholds, repeat-loss triggers, litigation red flags, documentation completeness, and referral prompts. That alignment creates consistent outcomes across desks and shorter onboarding for new underwriters.

Change management and implementation in 1 to 2 weeks

Nomad Data offers a white glove onboarding experience. Teams typically start with a drag-and-drop workflow to build trust quickly, then integrate with underwriting systems via modern APIs. Because Doc Chat requires no data science on your side, IT involvement is light, and integrations usually complete within one to two weeks. This mirrors the easy start and rapid integration approach described in Nomad’s claims transformation article here.

Governance, security, and auditability

Insurers must defend underwriting decisions every day. Doc Chat supports that with page-level citations, immutable logs, and configurable outputs that mirror your templates. Nomad’s enterprise security posture, including SOC 2 Type 2 controls, helps carriers adopt AI with confidence. The approach to explainability and governance reflects lessons from real-world carrier deployments, highlighted by Great American Insurance Group’s adoption story here.

Measuring ROI: throughput gains and quality lifts

Carriers evaluating Doc Chat for underwriters typically measure:

1. Cycle time reductions: average hours saved per submission file for GL and Construction, Property and Homeowners, and Specialty and Marine.
2. Quality improvements: reduction in missed prior claims or open litigation, fewer rating reworks, and cleaner referrals.
3. Capacity lift: additional submissions processed per underwriter per week without adding headcount.
4. Defensibility: faster responses to audit, reinsurance, and regulatory queries via page-level citations.

These levers compound. With fewer missed loss and litigation signals, pricing and terms improve. With faster throughput, brokers receive quicker responses and cleaner conditional quotes. With better explainability, internal reviews and reinsurance discussions run smoothly. The efficiency and quality gains mirror the broader performance improvements Nomad documents across document-intensive insurance workflows, including data entry automation at enterprise scale here.

Addressing common concerns about AI in underwriting

Three questions arise frequently:

Will we still control decisions? Yes. Doc Chat provides evidence-backed answers and structured summaries; humans make the decisions. Think of Doc Chat as a highly capable junior assistant with perfect recall and page citations.

What about accuracy and hallucinations? When grounded in your documents and asked to extract specific facts from them, modern AI performs consistently. Page-level citations ensure every field can be verified before binding.

How secure is the data? Nomad’s SOC 2 Type 2 posture, data handling controls, and customer-first governance allow carriers to adopt AI with confidence. The system is designed to meet internal audit and regulator expectations regarding traceability and defensibility.

From manual hunt to confident underwriting: a before-and-after snapshot

Before Doc Chat, an underwriter might spend two to three hours scanning a 300-page GL and Construction submission, another hour reconciling loss runs, and still miss a construction defect suit referenced once in a subcontractor email attachment. After Doc Chat, the underwriter receives a structured Prior Claims Summary and Open Litigation Summary with citations, plus a discrepancy report comparing broker attestation to loss runs. A few targeted questions complete the review and the file is ready for pricing or referral, often within minutes.

Take the next step

The underwriting edge today comes from surfacing the details that change decisions. Prior claims and open litigation are two of the most important signals in a submission file, and they are the easiest to overlook without the right tooling. Nomad Data’s Doc Chat gives your underwriters a faster, more accurate way to find them, explain them, and act on them. See how Doc Chat works for insurance teams here, and explore additional perspectives on large-scale document review and AI-driven insurance workflows in Nomad’s blog library, including Beyond Extraction here and The End of Medical File Review Bottlenecks here.

Incorporating the language your audience searches

If you are evaluating tools for AI review for open litigation in submissions or prioritizing prior claims detection automation underwriting, Doc Chat was built to solve precisely those two problems across General Liability and Construction, Property and Homeowners, and Specialty and Marine. It standardizes how your team discovers, summarizes, and documents the signals that matter most, with the speed and consistency modern underwriting demands.