Spotting Prior Claims and Open Litigation in Submission Files Using AI — For Underwriters Across General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine

Spotting Prior Claims and Open Litigation in Submission Files Using AI — For Underwriters Across General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine
Underwriters live and die by the quality and speed of their risk selection. Yet the toughest facts to find are often the ones that matter most: prior claims and open litigation. These details hide inside sprawling broker submission packages, scattered loss run reports, attachments, and even footnotes in litigation summaries. The result is slow quoting cycles, pricing uncertainty, and elevated risk of adverse selection. Nomad Data’s Doc Chat changes the equation by delivering an end-to-end, AI-driven review that pinpoints prior claims and open litigation across entire submission files—at portfolio scale.
Doc Chat ingests thousands of pages—from broker submission packages and ACORD applications to loss run reports, litigation summaries, ISO ClaimSearch reports, CLUE Homeowners disclosures, OSHA 300/300A logs, and legal correspondence—and returns a clear, auditable picture in minutes. Ask natural language questions like “AI review for open litigation in submissions for Acme Construction” or “Show me all bodily injury claims in loss runs since 2019” and get precise answers linked to the source pages. For General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine underwriters, this is the difference between manual hunting and instant certainty.
Why Prior Claims and Open Litigation Are So Hard to Find in Submission Files
Across lines of business, the signals underwriters need are buried in unstructured content. Broker narratives can be inconsistent, loss runs arrive from multiple carriers in different formats, and litigation summaries vary widely by jurisdiction and counsel. Under time pressure, even seasoned underwriters and risk selection analysts can miss a prior premises liability claim or overlook an active lawsuit tied to a DBA or legacy corporate name. These misses inflate loss ratios, lead to mispriced terms, and create downstream disputes at renewal.
The challenge is magnified in multi-entity risks, roll-ups, or insureds with frequent name changes. For example, a submission might reference “Acme Contracting LLC” while a loss run lists “Acme Co.” and a litigation summary cites “Acme Services, Inc.” If you don’t reconcile those variants and connect them to the same entity, you may falsely assume a clean history. Add PDFs of leases, contracts with indemnity clauses, certificates of insurance, and policy endorsement schedules, and you’re scanning thousands of pages for needles in a stack of needles.
Line-of-Business Nuances Underwriters Must Consider
General Liability & Construction
GL/Construction submissions often include ACORD 125/126, project schedules, subcontractor agreements, additional insured endorsements (e.g., CG 20 10, CG 20 37), waiver of subrogation requests, OSHA logs, safety manuals, and multi-year loss runs. Prior claims may hide in:
- Loss run reports with inconsistent reserve/paid/closed statuses by policy year
- Demand letters and attorney correspondence attached in email threads
- Litigation summaries referencing DBAs or joint ventures
- Certificates of insurance that imply additional insured obligations and potential tender exposure
- Contract indemnity provisions that expand assumed liability beyond what a summary states
Open litigation red flags include ongoing bodily injury suits, construction defect matters with long-tail exposure, and subcontractor incidents that may be tendered to the named insured. Failing to surface these can skew your base rates, deductibles, and additional insured requirements.
Property & Homeowners
Property and Homeowners underwriters see Statement of Values (SOVs), appraisals, elevation certificates, wind mitigation forms, inspection reports, and loss runs that may span weather, theft, water damage, and fire claims. Prior claims can be embedded in:
- CLUE reports and property-specific loss histories with address variants
- Inspection photos and assessors’ notes that reference unreported prior events
- Contractor invoices within broker submission packages that allude to recent undisclosed damage
- Legal filings tied to property disputes, coverage litigation, or liability arising from premises conditions
Open litigation in Property/Homeowners often shows up in county court records or in counsel summaries that don’t make it into the main submission narrative. Missing these leads to inaccurate CAT modeling assumptions, misapplied deductibles, or improper sublimits on perils like water or theft.
Specialty Lines & Marine
Marine and Specialty Lines bring their own complexity: classification society certificates, ISM Code documentation, P&I Club loss records, crew injury reports (Jones Act), hull and machinery surveys, charter party agreements, bills of lading, and voyage logs. Prior claims may be referenced in:
- P&I loss run reports with claim narratives that reference vessel renamings or ownership changes
- Port State Control deficiencies that hint at unreported incidents
- Settlement agreements for cargo or collision claims buried in legal correspondence
- Open litigation tracked under a prior fleet manager or management company
Failing to detect open litigation—such as crew injury suits or hull disputes—can upend negotiated deductibles, breach warranties, and materially affect rating and terms.
How the Manual Process Works Today—and Why It Breaks
Most underwriting teams still rely on manual review. Analysts open PDF after PDF, skim loss runs for key fields (date of loss, cause, reserves, paid, status), and attempt to reconcile entity names across years and carriers. They check litigation summaries, sometimes search court dockets, and compare broker narratives against documents. The issues:
- Volume: Submission packages regularly exceed hundreds or thousands of pages, especially for large construction accounts or complex property schedules.
- Inconsistency: Loss run formats vary by carrier; litigation summaries differ by law firm; ACORD forms arrive partially completed or scanned poorly.
- Time pressure: Quoting windows are short. Teams triage by scanning highlights, which means nuanced red flags and open suits can be missed.
- Human limitations: Fatigue and context switching degrade accuracy over long review sessions, raising E&O exposure.
Even with best efforts, it’s easy to miss a prior BI claim masked by a different entity spelling or an open premises liability suit buried in an appendix. The downstream costs are real: pricing drift, higher loss ratios, and lost broker trust when midterm issues surface.
AI Review for Open Litigation in Submissions: How Doc Chat Automates the Hunt
Nomad Data’s Doc Chat uses purpose-built, AI-powered agents to read entire submission files end to end, extract the facts underwriters need, and present them in your team’s preferred format. It’s engineered for underwriting realities: massive volume, messy data, and time-sensitive decisions.
Step-by-Step: Prior Claims Detection Automation Underwriting Teams Can Trust
- Bulk ingestion and normalization: Drag and drop broker submission packages, ACORD applications, loss run reports, litigation summaries, P&I records, SOVs, inspection reports, demand letters, and correspondence. Doc Chat processes thousands of pages per minute and normalizes poor scans for reliable parsing.
- Entity resolution across aliases: The system reconciles legal names, DBAs, prior names, FEINs, vessel names/IMO numbers, addresses, and management companies to unify identity. This eliminates the common pitfall where prior claims hide under legacy or variant names.
- Loss run structuring and dedupe: Doc Chat extracts dates of loss, cause, paid, reserves, closed/open status, litigation indicators, and notes, then deduplicates across carriers and years to build a consolidated claims timeline.
- Litigation surfacing with status tracking: From litigation summaries and legal correspondence, Doc Chat pulls parties, docket numbers, venues, allegations, counsel, and current status (filed, pending, settled, dismissed). It flags inconsistencies between “no known losses” statements and discovered suits.
- Cross-verification: Where configured, Doc Chat cross-references ISO ClaimSearch, CLUE Homeowners, or internal claims systems to validate disclosures. For Marine, it maps open claims to vessel renamings or ownership changes to avoid misses.
- Coverage context and trigger language: For GL/Construction, the system highlights additional insured endorsements and indemnity obligations that might convert third-party incidents into your insured’s exposure. For Property, it notes water damage frequency and prior CAT events by location. For Marine, it aligns claims to warranties and survey notes.
- Real-time Q&A: Ask, “List all open litigation tied to the insured in the last 5 years,” “Which claims remain open with reserves over $50k?” or “Show all BI claims in a construction setting.” Get answers with page-level citations so you can verify in seconds.
- Underwriter-ready output: Doc Chat produces a clear summary—prior claims table, open litigation roster, entity alias map, and commentary on material discrepancies. Output fields, formats, and thresholds are tailored to your appetite, rating model, and referral rules.
This “prior claims detection automation underwriting” workflow standardizes diligence across desks and time zones, ensuring every submission receives the same rigor—no matter how big the file or how tight the quote window.
What Underwriters Gain: Speed, Confidence, and Consistency
For GL & Construction, Property & Homeowners, and Specialty Lines & Marine underwriters, Doc Chat turns an uncertain, manual search into a repeatable, defensible process. Benefits include:
- Cycle-time compression: Move from hours or days of review to minutes, even when submissions top thousands of pages.
- Pricing accuracy: Incorporate the full prior claims and open litigation picture into base rates, deductibles, sublimits, and forms/endorsement strategy.
- Reduced leakage and E&O risk: Page-level citations and complete extraction reduce misses that lead to mispriced deals or disputes.
- Scalable diligence: Handle seasonal surges and complex roll-ups without adding headcount or overtime.
- Better broker experience: Faster, more informed responses increase hit ratios and trust.
These gains echo results seen by leading carriers. In complex claims environments, one carrier cut days of searching to minutes with page-cited answers, improving both speed and quality. See how Great American Insurance Group accelerated complex reviews using Nomad in this case study.
How Doc Chat Works Under the Hood—Purpose-Built for Document Complexity
Underwriting files are rarely tidy. They mix PDFs, scans, spreadsheets, emails, and attachments. Traditional OCR/keyword tools fail as formats change. Doc Chat is different. It’s engineered for inference—finding concepts scattered across a claim file and synthesizing them into structured answers. If you’ve wondered why “document scraping” is not just web scraping for PDFs, this overview from Nomad explains the gap: Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.
At the core, Doc Chat:
- Reads entire files with consistent accuracy—no fatigue, no misses on page 1,500.
- Understands synonyms, aliases, and context—recognizing that “Acme Services, Inc.”, “Acme Co.”, and “Acme Contracting LLC” may be the same insured or related entities.
- Surfaces coverage-relevant language—endorsements, exclusions, warranties, indemnity clauses—so prior claims and open suits are tied to potential coverage triggers.
- Delivers ask-anything capability—so underwriters can interrogate a file like a senior analyst would, in seconds.
Examples by Line of Business
General Liability & Construction
Scenario: A broker submission package includes ACORD 125/126, five years of loss runs from three carriers, a subcontractor schedule, OSHA logs, and a litigation summary. The narrative claims “no open BI matters.”
Doc Chat Finds: One premises liability suit still open (reserve $150k) under a DBA from two years ago; a subcontractor incident likely to be tendered via additional insured requirements; two prior minor incidents with low reserves that clustered on a single project indicating heightened jobsite risk.
Underwriter Action: Adjust base rate, apply per-project aggregate, tighten additional insured and waiver terms, require risk controls, and refer for legal review on the open suit.
Property & Homeowners
Scenario: A Homeowners submission includes a CLUE report, inspection photos, and contractor invoices. Narrative says “no prior water losses.”
Doc Chat Finds: Two water damage claims three and five years ago revealed in CLUE; inspection references a repaired roof leak not disclosed; litigation tied to a neighboring property dispute that mentions prior damage.
Underwriter Action: Increase water deductible, add sublimits on water damage, verify remediation, and factor in property dispute as a potential claims frequency indicator.
Specialty Lines & Marine
Scenario: Marine package includes P&I loss runs, hull & machinery surveys, charter party agreements, and crew injury records. Narrative cites “no open crew claims.”
Doc Chat Finds: One crew injury suit pending post-vessel rename; survey notes indicate unreported near-miss incident; charter party indemnity language expands liability beyond stated assumptions.
Underwriter Action: Reassess warranty terms, adjust deductibles, require safety remedial actions, and revisit appetite for certain voyage exposures.
Business Impact Underwriters Can Quantify
Underwriting leaders look for measurable gains from “prior claims detection automation underwriting.” Doc Chat delivers at three levels:
1) Time Savings and Throughput
Complex submissions go from multi-hour reads to minutes. Teams absorb more opportunities without overtime. Faster quotes improve broker satisfaction and win rates.
2) Cost Reduction and Loss Ratio Improvement
Detecting open litigation and undisclosed prior claims reduces mispricing, adverse selection, and downstream disputes. Avoid needless external reviews; keep expertise in-house while scaling diligence with AI.
3) Accuracy and Defensibility
Page-cited findings provide an auditable trail. Consistent extraction of dates of loss, reserves, closed/open status, and litigation posture creates a reliable foundation for pricing and underwriting notes. Compliance, audit, and internal QA benefit from standardized, transparent outputs.
These patterns mirror broader insurance document automation wins, as detailed in Nomad’s perspectives on eliminating medical file review bottlenecks and transforming claims work. For more, see The End of Medical File Review Bottlenecks and AI’s Untapped Goldmine: Automating Data Entry.
Why Nomad Data’s Doc Chat Is the Best Fit for Underwriting Teams
Underwriters evaluating tools for “AI review for open litigation in submissions” need a solution built for the realities of commercial insurance documentation. Doc Chat stands out because:
- Volume without headcount: Ingest entire submission files—thousands of pages—so full reviews move from days to minutes.
- Mastery of complexity: From GL additional insureds and indemnity clauses to Marine warranties and Property SOV nuances, Doc Chat surfaces coverage triggers and risk signals hidden in dense language.
- The Nomad Process: We train Doc Chat on your underwriting playbooks, appetite, referral rules, and output formats. You get a personalized agent aligned to your exact workflows.
- Real-time Q&A: Ask plain-language questions across massive document sets and get instant, cited answers.
- Thorough and complete: No more blind spots. Every reference to prior claims, open suits, reserves, and coverage touchpoints is captured and linked.
- Security and governance: Nomad maintains enterprise-grade security, including SOC 2 Type 2. Page-level explainability supports audit, compliance, reinsurer reviews, and internal oversight.
- White-glove service and rapid value: Typical implementation runs 1–2 weeks. Our team co-creates with you, ensuring high adoption and immediate ROI.
The results are in line with other carriers’ experiences adopting Nomad technology for complex reviews. See how instant answers with page citations transformed cycle times and QA in GAIG’s story.
From Manual to Managed: A Typical Underwriting Workflow with Doc Chat
- Intake: Broker submission arrives with ACORDs, SOV, loss runs, litigation summaries, inspection reports, and contracts. Documents are uploaded or automatically ingested via integration.
- Automated completeness check: Doc Chat confirms required artifacts (e.g., 5-year loss runs, OSHA logs for construction, CLUE for homeowners, P&I for marine). Missing items trigger a templated broker request.
- Prior claims and litigation summary: The system produces a consolidated table of all known claims and flags any open suits with dockets, parties, and status—each with source citations.
- Coverage signal extraction: Endorsements, exclusions, warranties, indemnities, AI requirements, and subcontractor relationships are summarized for fast review.
- Targeted Q&A: The underwriter asks follow-up questions (e.g., “List all open GL suits with reserve > $100k,” “Which properties have three or more water claims?”, “Map crew injury claims to vessel renames”).
- Underwriter memo and referral pack: Doc Chat generates a memo in your template with tables, commentary, and links to the source pages—ready for underwriting committee or facultative placements.
- Decision support: With complete visibility, the underwriter calibrates pricing, terms, endorsements, and conditions with confidence—and does it days faster.
Addressing Common Concerns
“Will AI hallucinate or miss critical facts?” For bounded document sets like broker submissions, loss runs, and litigation summaries, Doc Chat is designed to extract what’s there and answer with citations. You can click through to verify every conclusion.
“Does this replace underwriters?” No. It removes rote reading and reconciliation so underwriters can apply judgment, negotiate terms, and manage broker relationships. As Nomad notes in its broader claims automation work, AI becomes a capable, supervised teammate—never the final decision-maker. See Reimagining Claims Processing Through AI Transformation.
“How fast can we get value?” Most teams are live in 1–2 weeks. We start with drag-and-drop reviews and then integrate with underwriting workbenches and DMS via APIs for touchless intake.
KPIs to Track Post-Implementation
- Submission review time: Average hours spent from intake to quote readiness.
- Detection rate: Number of previously undisclosed prior claims and open litigation found per 100 submissions.
- Pricing accuracy: Variance between quoted and indicated rate after full information; reduction in midterm adjustments.
- Loss ratio improvement: Year-over-year change on cohorts with Doc Chat-enabled reviews.
- Hit ratio and broker NPS: Faster, better-informed responses typically lift both.
Getting Started: A Practical Pilot for Underwriting Leaders
We recommend a focused pilot targeting the highest-impact use cases—complex GL/Construction accounts with multi-carrier loss runs, Property schedules with scattered loss history, and Marine fleets with ownership changes. In two weeks, you can:
- Stand up a secure workspace and import 25–50 historical submissions.
- Define an output template for prior claims and open litigation.
- Codify appetite thresholds and referral rules (e.g., open suits > $100k reserve).
- Run parallel reviews against recent quotes to benchmark time saved and detection gains.
- Roll learnings into a scaled deployment integrated with your intake workflows.
Within this framework, Doc Chat consistently demonstrates reductions in review time from hours to minutes while increasing the detection of material prior claims and open litigation. That combination of speed and completeness is the foundation of better underwriting.
The Competitive Edge: Institutionalizing Underwriting Best Practices
Every organization has seasoned underwriters who “just know” where problems hide in submissions. With Doc Chat, you can encode those unwritten rules into prompts, checks, and output formats that every underwriter follows—day one. This standardization improves training, creates defensible decisions, and preserves institutional knowledge. As Nomad explains, the value isn’t in generic summarization—it’s in teaching machines to think like your best people and applying that standard consistently across every file. See Beyond Extraction for the philosophy behind this approach.
Conclusion: Make Prior Claims and Open Litigation Review a Strength, Not a Bottleneck
For underwriters in General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine, the difference between a good book and a great one often hinges on what you find—or miss—in submission files. Prior claims and open litigation shape pricing, terms, and appetite. With Nomad Data’s Doc Chat, you turn an error-prone, manual process into a fast, accurate, and auditable capability that scales with your portfolio. It’s “AI review for open litigation in submissions” built for the real world of underwriting, powered by your playbooks, and live in as little as 1–2 weeks.
If you’re ready to transform “prior claims detection automation underwriting” from an aspiration into daily practice, let’s start with a pilot and prove the impact on your next quoting cycle.