Spotting Prior Claims and Open Litigation in Submission Files Using AI – Underwriter

Underwriters across General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine routinely face a risky blind spot: critical facts about prior claims and ongoing litigation are often buried deep within broker submission packages, loss run reports, and scattered litigation summaries. Missing these signals can derail risk selection, misprice accounts, and trigger costly surprises post-bind.

Nomad Data’s Doc Chat eliminates that blind spot. Purpose‑built AI agents rapidly ingest entire submission files—ACORD applications, SOV spreadsheets, loss runs, litigation summaries, contracts, certificates, emails, and more—then answer pointed underwriting questions in seconds. Ask, “List all open suits against the named insured, with docket numbers and venues” or “Summarize prior water losses at 14 Elm Street and whether any are still litigated,” and Doc Chat returns precise answers with page‑level citations back to the original documents. What once took hours of manual hunting now takes minutes, with explainable results underwriter teams can trust.

Why this problem persists: the messy reality of submission files

Submission files are not standardized. In General Liability & Construction, a single new business submission can contain ACORD 125/126 applications, subcontractor agreements, wrap-up (OCIP/CCIP) details, COIs, indemnity provisions, OSHA 300 logs, EMR worksheets, loss run reports from multiple carriers, litigation summaries prepared by counsel, and even deposition excerpts. In Property & Homeowners, you might receive ACORD 140/Property supplements, Statement of Values (SOV) spreadsheets, COPE data, inspection reports, appraisal PDFs, catastrophe modeling outputs, and historical loss runs referencing prior water, fire, or wind events that may still be under dispute. Specialty Lines & Marine submissions often feature charter parties, bills of lading, P&I club correspondence, surveyor reports, and prior cargo claim documentation, sometimes accompanied by open litigation with federal or international jurisdictions.

Across lines, signals of open litigation and prior claims rarely live in one consistent spot. They’re embedded in: footnotes on a loss run, a coverage memo inside a broker submission package, a one‑page litigation summary drafted two renewals ago, or an email attachment with PDF images from a court docket. Naming conventions vary; policyholder and DBA names differ; locations appear in inconsistent formats; and some brokers will summarize “no current litigation” without reconciling against long-tail suits lurking in public records or older attachments. Human reviewers simply cannot read every page with the same attention—especially across rush submissions or mid‑term endorsements.

How underwriters handle it manually today

Most underwriting teams follow a similar manual process when scouring broker submission packages, loss run reports, and litigation summaries for prior claims and open suits:

Document intake and consolidation. Files arrive via email or portal: ACORD forms, SOVs, supplemental questionnaires, loss runs, litigation summaries, contracts, inspection reports, ISO ClaimSearch references, CLUE reports, public court dockets (PACER or state portals), and ad hoc broker narratives. Analysts merge or bookmark the PDFs, then start skimming.

Keyword searching and note taking. Reviewers search for “litigation,” “suit,” “claim,” “loss,” “subrogation,” “AOB,” “bodily injury,” “Jones Act,” “cargo damage,” “sinkhole,” “water damage,” “hail,” “mold,” and similar terms. They copy/paste fragments into notes, trying to reconcile inconsistent names, addresses, VINs or hull IDs, voyage numbers, policy periods, and claim numbers spanning multiple carriers.

Cross‑checking and escalation. When the broker’s litigation summary doesn’t match loss runs—or when a loss run lists an open claim without description—underwriters send follow‑up questions. They may check public court portals or ISO/CLUE data (if available) to validate whether cases are truly closed. This volley of emails can take days and often occurs up against binding deadlines.

Constraints and risks. Under time pressure, teams triage by page count and perceived severity, meaning not every page gets the same scrutiny. Inconsistent broker formats, scanned images that hinder text search, and unstructured emails increase the risk of missed signals. The result: coverage misalignment, mispriced accounts, surprise ALAE from ongoing suits, and downstream rework for underwriting, claims, and legal.

AI review for open litigation in submissions: how Doc Chat changes the workflow

Doc Chat is designed for exactly this class of underwriting problem: high‑volume, high‑variability document sets where critical details are scattered and inconsistent. Instead of skimming and keyword hunting, underwriters work question‑first. Load the entire set of broker submission packages, loss run reports, litigation summaries, ACORD forms, contracts, OSHA logs, and correspondence. Then ask targeted questions like:

• “List all open litigation with docket numbers, courts, plaintiffs/defendants, incident dates, and current status. Cite the page each detail was found.”
• “Identify all prior BI/PD claims in the loss runs exceeding $25,000 indemnity, indicating open/closed and reserve amounts.”
• “For the SOV addresses, show any prior Property losses by address, peril, and whether any are in dispute or litigation.”
• “Summarize Specialty/Marine cargo claims by commodity, voyage, bill of lading number, damage type, and any referenced arbitrations.”
• “Highlight discrepancies between the broker’s litigation summary and the loss runs (e.g., open/closed status mismatches).”

Doc Chat’s answers return with precise citations back to source pages, making validation simple. Underwriters can drill down with natural‑language follow‑ups: “Which OSHA logs correlate to the slip‑and‑fall suit?” or “Show all references to ‘AOB litigation’ across the packet and indicate whether assignments are still active.” This real‑time Q&A across massive document sets removes the drudgery of manual page‑turning while preserving defensibility.

Line-of-business nuances that make prior claims and open suits hard to spot

General Liability & Construction. Prior claims may be tied to subcontractor incidents, wrap‑up projects, product/completed ops exposures, or premises liability at multiple locations. Litigation references can hide in indemnity agreements, contracts, or COIs that shift risk upstream/downstream. Some suits remain “open” informally despite a broker narrative suggesting closure. OSHA 300/300A forms may imply incidents with litigation potential not yet captured in loss runs.

Property & Homeowners. Water, wind, fire, and theft losses may recur at the same address under different carriers or DBAs. Assignment of Benefits (AOB) disputes linger post‑CAT seasons; sinkhole or earth movement cases can remain active for years; contractor disputes over scope often proceed on separate legal tracks. SOV typos or re‑addressing can mask prior losses, and scanned inspection reports often contain the only mention of contentious repairs or denied claims that later turned litigious.

Specialty Lines & Marine. Cargo damage, laytime/demurrage issues, general average, and Jones Act crew claims traverse a patchwork of jurisdictions and arbitration venues. Litigation and arbitration might be referenced in P&I club correspondence, surveyor reports, or charter party side letters. The same voyage can spawn multiple claim files with different counterparties, complicating aggregation and open/closed status reconciliation.

What Doc Chat automates for underwriters

Doc Chat is not just OCR or generic summarization; it’s a suite of underwriting‑tuned, AI‑powered agents capable of reading like domain experts and following your playbook. For prior claims and open litigation detection, Doc Chat automates:

1) End‑to‑end document ingestion. Entire broker submission packages, loss run reports, litigation summaries, ACORD 125/126/140 forms, SOV/COPE spreadsheets, inspection surveys, contracts, COIs, OSHA logs, P&I correspondence, bills of lading, charter parties, and email exports are ingested at once—even when scanned or inconsistently formatted.

2) Normalization & entity resolution. Doc Chat recognizes that ABC Builders, ABC Builders LLC, and ABC Builders, Inc. are the same Entity; it reconciles DBA names, alternate addresses, vessel names/hull IDs, voyage numbers, and varying claim numbering conventions across carriers. This is critical when aggregating losses and litigation across time.

3) Cross‑document inference. If a loss run lists an open claim in 2022 but the litigation summary asserts “no open litigation,” Doc Chat flags the discrepancy and cites both sources. It maps events across documents—linking OSHA incidents to premises BI suits, or plumbing invoices in correspondence to a contested water loss that appears later in Property loss runs.

4) Real‑time Q&A. Underwriters ask focused questions—“AI review for open litigation in submissions” is a native Doc Chat pattern. The system returns structured answers instantly with page‑level citations, enabling rapid validation and confident underwriting notes.

5) Custom extraction & scoring. Nomad trains Doc Chat on your underwriting rules. For example, “flag open BI suits >$50,000 reserves within the last 36 months for GL; highlight any AOB or roof litigation within 5 years for Property; surface Jones Act or cargo contamination claims within 60 months for Marine.” The result is a standardized, playbook‑driven output that underwriters can drop into their workbench.

What you gain: speed, accuracy, and consistency at underwriting scale

Nomad’s clients routinely see submission reviews move from hours to minutes. With Doc Chat reviewing every page with identical attention, accuracy improves as volume increases, reversing the typical human fatigue curve. Underwriters get consistent, repeatable extraction of litigation status, loss details, reserves, venues, and timelines—cutting rework and reducing E&O exposure.

Business impact includes:

Faster risk selection. Quickly disqualify accounts outside appetite (e.g., habitational risks with clustered water losses and ongoing AOB suits) or prioritize good risks with clean, fully reconciled histories.

Better pricing and terms. Precisely quantify prior loss and open litigation signals to calibrate deductibles, sublimits, co‑insurance, or exclusions. For Construction, identify where contractual risk transfer is weak; for Marine, surface voyage‑specific patterns (commodity type, port pairings) that influence rate.

Lower LAE and cycle time. Fewer back‑and‑forth broker requests. Underwriters start with a clean list of discrepancies and missing documents, accelerating time to quote and binding decisions.

Reduced leakage. By finding every material reference to prior claims and open suits, Doc Chat minimizes surprises that lead to leakage and post‑bind disputes.

prior claims detection automation underwriting: a practical, playbook-first approach

Generic AI struggles when the rules live only in human heads. Nomad addresses this by encoding your underwriting logic into Doc Chat. As described in our piece, Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, the real work is cross‑document inference guided by institutional expertise. We interview your senior underwriters, extract the unwritten rules, and translate them into machine‑executable steps. The outcome: a solution that behaves like your best reviewer—only faster and more consistent.

Concrete examples by line of business

General Liability & Construction: A contractor submission includes ACORD 125/126, loss runs from two prior carriers, subcontractor hold‑harmless language, and OSHA logs. Doc Chat extracts:

• Two bodily injury suits still marked “open” on one loss run, but the broker’s litigation summary lists them as closed. Doc Chat flags the mismatch and provides exact page citations.

• A premises BI loss at a satellite yard referenced only in an old inspection report attached to an email—tying it to an NOC code found in the ACORD. The incident resulted in a claim reopened last month after a demand letter, discovered in correspondence.

Property & Homeowners: A multi‑location SOV shows several habitational addresses with recent water loss history. Doc Chat links:

• A 2021 water loss at 14 Elm Street with ongoing AOB litigation that appears only in a scanned letter within the submission package; the loss run shows “open claim,” but the broker’s summary omits it.

• Two hail losses at 18 Oak Avenue and 22 Oak Avenue with disputed scope—Doc Chat recognizes related contractor invoices that indicate continued dispute risk despite a “closed” notation in one carrier’s system.

Specialty Lines & Marine: A cargo account provides bills of lading, surveyor reports, P&I correspondence, and charter party agreements:

• Doc Chat consolidates three separate cargo contamination claims that reference different voyage numbers but the same shipment window and product batch. It flags a related arbitration still open in London, cited in a surveyor’s appendix.

• A Jones Act crew injury referenced only in P&I correspondence is linked to a U.S. district court docket number found in a scanned PDF footer.

Built for volume, complexity, and compliance

Doc Chat handles entire claim files and submission packets—thousands of pages—without added headcount. As we’ve written in The End of Medical File Review Bottlenecks, Doc Chat reads every page with unflagging attention, producing standardized outputs in seconds. For insurers worried about explainability, Doc Chat provides page‑level citations for every answer, a capability highlighted in the Great American Insurance Group webinar recap—where page‑linked results accelerated trust, review, and oversight.

Security and governance are table stakes. Nomad maintains enterprise‑grade controls and SOC 2 Type 2 practices, described in AI’s Untapped Goldmine: Automating Data Entry. Answers are traceable, audit‑ready, and defensible for regulators, reinsurers, and internal QA.

Where Doc Chat plugs in: from drag‑and‑drop to underwriting workbench

Getting started is simple. Teams can begin with drag‑and‑drop uploads into Doc Chat to validate ROI quickly. As adoption grows, Nomad integrates with your underwriting platforms (policy admin, intake portals, rating engines) via modern APIs. The solution can automatically:

• Check submission completeness (loss runs for each prior carrier, litigation summaries updated within last 90 days, required ACORDs and supplements present).
• Run a “prior claims detection automation underwriting” preset that extracts open suits, disputed claims, reserves, venues, and timelines by line of business.
• Produce standardized underwriting notes with full citations, ready to store in your workbench.

This phased approach mirrors what we’ve seen in claims organizations adopting Doc Chat, as outlined in Reimagining Claims Processing Through AI Transformation: quick wins first, then deeper integration for lasting impact.

How Doc Chat answers the two core underwriting questions

1) What prior claims matter most for this account? Doc Chat aligns with your risk appetite to surface only material, recent, or pattern‑forming losses (e.g., water losses within 36 months for Property habitational; BI with litigation for GL; contamination/delay for Marine cargo). It pulls amounts, reserves, dates, locations, and narratives, then unifies them across carriers and PDFs.

2) Where is there open litigation risk right now? The “AI review for open litigation in submissions” workflow crawls the packet for explicit suits, arbitrations, and demand letters. It also flags proxy evidence of open matters—e.g., reserve activity suggesting ongoing dispute, correspondence mentioning court‑ordered mediation, or references to counsel still engaged. You get a clear list of matters with citations and a confidence assessment based on document consistency.

From exceptions to outcomes: what underwriters do with the results

Because Doc Chat cites every finding, underwriters can immediately verify and decide:

• Decline or re‑rate where open litigation and prior loss patterns exceed appetite.

• Adjust terms (deductibles, sublimits, exclusions) when recurring causes or disputed losses drive tail risk.

• Request targeted info from brokers (e.g., updated loss runs from Carrier B, current docket status for Smith v. Insured, revised SOV for additional addresses).

The difference is focus. Underwriters spend their time making decisions, not turning pages.

Quantifying ROI: time, cost, and accuracy

Across carriers and MGAs, underwriting teams report material improvements when using Doc Chat for prior claims and litigation detection:

Time savings. A complex submission review that used to take 2–4 hours of human effort often drops to under 15 minutes, even when files exceed a thousand pages.

Cost reduction. By compressing review windows and reducing back‑and‑forth broker requests, teams quote more accounts per underwriter without increasing headcount—lowering expense ratios.

Accuracy and defensibility. Page‑level citations and standardized outputs reduce E&O exposure and improve internal QA pass rates. Underwriting managers gain portfolio‑level visibility into why decisions were made.

Loss ratio improvement. Better identification of open suits and disputed losses means cleaner risk selection and fewer post‑bind “surprises,” mitigating leakage and adverse development.

Why Nomad Data is the best partner for underwriting AI

Doc Chat stands out for five reasons especially relevant to underwriters:

1) Volume. Doc Chat ingests entire submission files—thousands of pages—without breaking a sweat. Reviews move from days to minutes.

2) Complexity. Exclusions, endorsements, litigation mentions, and loss details hide in dense, inconsistent documents. Doc Chat digs them out and cross‑checks them, enabling more accurate underwriting decisions.

3) The Nomad Process. We train Doc Chat on your underwriting playbooks, appetite, and document types. The result is a solution tailored to GL & Construction, Property & Homeowners, and Specialty & Marine nuances—not a one‑size‑fits‑all tool.

4) Real‑time Q&A. Ask “Summarize all open litigation by venue” or “Show prior claims >$25k reserves by location and peril” and get instant answers—even across massive packets.

5) Thoroughness. Doc Chat surfaces every reference to coverage, liability, or damages. For underwriting, that translates to finding every prior claim and open matter that could materially affect pricing or appetite.

White‑glove onboarding in 1–2 weeks

Nomad delivers a white‑glove implementation that typically completes in 1–2 weeks. We collect sample submission files (by LOB), interview your senior underwriters, and encode your rules into Doc Chat presets. You start with drag‑and‑drop workflows for immediate wins, then proceed to API integration with your underwriting workbench. Training sessions focus on practical prompts, validation via citations, and exception handling—ensuring rapid adoption and measurable impact in the first month.

Raising confidence: explainability, audit, and security

Underwriters and managers need to verify every conclusion. That’s why Doc Chat provides page‑linked citations for each answer it returns. Oversight can click straight to the source PDF page, confirm context, and finalize underwriting notes with confidence. This “transparent trail” is central to trust, as highlighted by the Great American Insurance Group experience linked above.

Security is woven throughout. Nomad adheres to modern compliance practices, safeguards sensitive information, and provides document‑level traceability for audits and regulatory scrutiny. Your data stays protected, and your teams gain a defensible AI partner built for enterprise insurance.

From manual review to machine‑assisted excellence

Underwriting used to equate to “read everything.” Today, the better standard is “ask the best questions and validate quickly.” Doc Chat makes that shift painless. One underwriter can now review more accounts, at greater depth, with fewer misses. The technology absorbs the reading; the underwriter applies judgment, negotiates terms, and selects risks with higher precision.

A short checklist to operationalize AI-driven prior claims and litigation detection

To embed Doc Chat effectively within underwriting operations, we recommend:

Standardize the inputs. Ensure broker submission packages include updated loss run reports for all prior carriers, litigation summaries refreshed within 90 days, ACORD forms, SOV/COPE data, and any relevant contracts or OSHA logs. When something is missing, Doc Chat flags it automatically.

Codify risk thresholds by LOB. Define what “material” means per line: GL BI suits >$50k reserves; Property water losses within 36 months; Marine contamination claims linked to specific commodities or routes. Doc Chat can be tuned to these thresholds.

Use question‑first review. Don’t skim; ask. Create a shared set of prompts—“AI review for open litigation in submissions,” “prior claims detection automation underwriting,” “SOV address‑loss reconciliation,” and “reserve activity that implies ongoing dispute.”

Require citation-backed notes. Only accept underwriting notes that carry page‑linked references from Doc Chat. This policy drives consistent quality and easier peer review.

Monitor outcomes and refine. Track declinations, pricing changes, and loss results against Doc Chat flags. Iterate your presets quarterly to keep pace with emerging patterns and jurisdictions.

Frequently asked questions from underwriting teams

What if my brokers send images and scans? Doc Chat handles mixed‑quality scans and scanned images, reading them alongside native PDFs and spreadsheets. You get a unified set of answers with citations regardless of format.

Can Doc Chat check public court sources? Many carriers prefer to keep Doc Chat focused on submitted materials for speed and governance, then use separate tools or teams for external validation. Nomad can discuss options for enrichment and validation workflows that fit your compliance posture.

How do we keep false positives low? During onboarding, we tune Doc Chat to your definitions of “open,” “disputed,” “material,” and “recent” by LOB. The model learns your vocabulary and thresholds, reducing noise and driving higher signal quality.

Does AI replace underwriters? No. Doc Chat reads and extracts; underwriters decide. Think of it as a highly reliable junior analyst that never tires, always cites sources, and follows your playbook.

The competitive edge: better submissions, faster quotes, cleaner books

Carriers and MGAs that adopt Doc Chat for prior claims and open litigation detection win on three fronts:

• Broker experience: Faster, better‑informed responses reduce friction and position you as a preferred market.

• Underwriter leverage: Decision‑ready facts at submission allow you to negotiate terms that reflect true risk.

• Portfolio health: Cleaner books and fewer post‑bind surprises translate to more predictable loss ratios.

In a world where submission volumes and complexity keep growing, underwriters who keep reading manually will fall behind. Those who deploy Doc Chat move decisively—quoting faster, selecting smarter, and documenting every decision with audit‑ready rigor.

Next step: apply AI to your next submission cycle

Pick a representative set of recent General Liability & Construction, Property & Homeowners, and Specialty & Marine submissions—especially those with large, messy packets and mixed‑quality scans. Load them into Doc Chat and run your first “AI review for open litigation in submissions” and “prior claims detection automation underwriting” presets. Compare the output to your existing notes. You’ll see the same transformation other insurers have reported in our case studies and blogs: from days to minutes, with better completeness and confidence.

Underwriting’s future is question‑first, machine‑assisted, and citation‑backed. Bring that future to your desk today.