Flagging Coverage Gaps: AI Review of Ceded Policy Endorsements at Scale

Reinsurers live and die by their ability to see risk clearly—especially the risk that hides in the footnotes. For an Exposure Analyst, that means combing through thousands of pages in ceded submissions to find the endorsements and manuscript clauses that quietly change loss potential. The problem? Critical details are embedded across Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, and Policy Manuscripts—all formatted differently by every cedent and broker.

Nomad Data’s Doc Chat ends the guesswork. Purpose-built for insurance document intelligence, Doc Chat ingests entire reinsurance submissions, automatically extracts endorsements, and flags coverage gaps and aggregation drivers that manual review often misses. Whether you need to identify coverage gaps in ceded business for reinsurance or find umbrella aggregation risk in reinsurance submissions, Doc Chat turns days of manual review into minutes—complete with page-level citations and structured outputs you can feed directly into your exposure models.

Why hidden endorsements are the Exposure Analyst’s biggest blind spot

In Reinsurance, exposure modeling and treaty pricing depend on accurate, complete visibility into underlying coverage. Yet the exact drivers of tail risk—blanket Additional Insured grants, Primary & Noncontributory wording, Per Project or Per Location Aggregates, Waiver of Subrogation, Designated Ongoing/Completed Ops, and manuscript expansions—are buried in variable, unstructured documents. An Exposure Analyst must reconcile what the cedent says is covered with what is actually covered across thousands of endorsements and schedules.

Complicating matters, endorsements rarely use consistent language. Many submissions blend ISO forms (e.g., CG 20 10, CG 20 37) with manuscript endorsements, local carrier forms, and broker-designed endorsements. Terms like “insured,” “named insured,” and “additional insured” may be defined separately across multiple documents. Umbrella and excess forms incorporate or replace underlying terms inconsistently, and follow-form language can silently pull expansive primary terms into upper layers. The net result: small phrase differences trigger big changes in accumulation potential—exactly the sort of changes that manual review misses when time is short.

AI for extracting endorsements in cedent policy schedules

Exposure Analysts often ask for a repeatable way to run a fine-toothed comb across a submission. If your team is searching “AI for extracting endorsements in cedent policy schedules,” you are looking for more than OCR or keyword search. You need an engine that understands insurance semantics, connects the dots between policies and layers, and normalizes outputs for modeling. That’s precisely what Doc Chat delivers.

Doc Chat reads entire reinsurance packages—Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, Policy Manuscripts, umbrella/excess forms, declarations, and bordereaux—and constructs an endorsement graph that shows which clauses apply to whom, where, and under what conditions. It handles mixed file types, scanned PDFs, and inconsistent formatting. Then it answers exposure-critical questions in seconds, such as “Which policies grant blanket AI status to vendors?” or “Where is ‘Primary and Noncontributory’ introduced into umbrella follow-form language?”

The nuances of the problem for Exposure Analysts in Reinsurance

From an Exposure Analyst’s vantage point, the most consequential risks often hide in the interactions between documents, not on any single page. Consider a cedent with a large contractor portfolio, where primary GL policies include blanket Additional Insured and Primary & Noncontributory endorsements tied to written contracts. The umbrella purports to be follow form, but also includes a manuscript condition that changes which contract provisions constitute a “written contract,” effectively widening AI status in certain jurisdictions. On paper, everything looks normal. In an event, however, dozens of third parties may access limits—creating unexpected aggregation that a treaty did not price.

Other nuances a Reinsurance Exposure Analyst must chase down:

• Per Project/Per Location Aggregates: These raise the number of available aggregates, materially changing catastrophe scenarios in construction and real estate portfolios.
• Additional Insured (AI) ambiguity: Blanket AI grants triggered by “written contract” or “agreement” language vary widely. Some forms extend to parties with indirect contracts, expanding insured populations.
• Waiver of Subrogation: Seemingly benign wording can close recovery avenues across many claims, increasing net loss to the cedent—and to you.
• Completed Operations tail: AI coverage that survives completion by a fixed number of years alters long-tail severity and frequency expectations.
• Umbrella/excess interplay: Follow-form exceptions, self-contained coverage parts, drop-down triggers, and “maintenance of underlying” conditions create complexity at the very layers reinsurers assume are simple.
• Manuscript endorsements: Small, bespoke changes (often broker-created) subtly redefine insured status, definitions of occurrence, or other loss drivers.

These nuances demand an exhaustive read—something humans cannot consistently perform across thousands of pages per submission and hundreds of submissions per renewal season.

How the process is handled manually today

Most Exposure Analysts still rely on manual triage and spreadsheet-driven tracking. A typical workflow includes:

• Downloading cedent packages from broker portals and shared drives—often dozens of PDFs per account, mixing policy decks, Endorsement Addenda, loss runs, SOVs, and correspondence.
• Skimming declarations and Policy Schedules for a form index, then attempting to match that index to file attachments.
• Keyword searching for “Additional Insured,” “Primary & Noncontributory,” “Per Project,” “Per Location,” “Waiver of Subrogation,” “Contractor,” “Vendor,” etc., then copy/pasting hits into an Excel tracker.
• Hunting for umbrella/excess terms that either follow form or carve out key primary conditions—with manual cross-referencing between layers.
• Reconciling conflicts among manuscript language, ISO endorsements, and per-location schedules, then formatting conclusions for model inputs.

Even with templates and macros, this process is slow, error-prone, and unscalable. In peak season, teams resort to spot checks or sampling. That’s when blind spots grow—and leakage follows.

How Nomad Data’s Doc Chat automates the full endorsement review

Doc Chat is a suite of specialized AI agents that read, extract, cross-check, and summarize entire reinsurance submissions—with explainable answers and export-ready outputs. Unlike generic OCR or “summarize this PDF” tools, Doc Chat is trained on the insurance problem: it understands policy structure, endorsement semantics, and how small language changes impact exposure.

At a high level, Doc Chat performs these steps for reinsurance submissions:

• Bulk ingestion and normalization: Load entire ceded packages at once (thousands of pages). Doc Chat automatically detects document types—Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, Policy Manuscripts, declarations, umbrella/excess forms, bordereaux—and normalizes them for analysis.
• Form and clause recognition: Identify ISO form codes, carrier forms, and manuscript titles—even when scanned, incomplete, or misindexed. Extract exact clause text with page-level citations.
• Endorsement graph building: Map who gets coverage (named insureds, additional insureds, vendors, owners), under which conditions (written contract, scheduled parties, automatic status), and for which operations (ongoing vs. completed) and locations (per project/per location).
• Umbrella/excess mapping: Detect follow-form references, self-contained coverage parts, drop-down mechanisms, maintenance-of-underlying requirements, and any manuscript exceptions that expand or restrict AI rights.
• Risk driver flagging: Automatically surface aggregation drivers: blanket AI, primary & noncontributory, waiver of subrogation, per project/location aggregates, completed ops tails, and broadened insured definitions.
• Exposure Q&A: Ask natural language questions such as “List all blanket AI grants tied to construction contracts,” “Which policies include ‘Primary and Noncontributory’ for additional insureds?” and “Where does umbrella follow form not extend AI coverage?”
• Structured outputs: Export endorsement findings as structured fields for portfolio roll-ups, model ingestion, and treaty pricing memos—without manual rekeying.

The difference is not incremental. It is transformational. As we documented in “Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs,” the real challenge is inference—teaching systems to read like an experienced analyst and synthesize judgment from scattered evidence. Doc Chat is purpose-built to make those inferences at scale and with consistency.

Use case deep dive: find umbrella aggregation risk in reinsurance submissions

Searching for “find umbrella aggregation risk in reinsurance submissions”? Doc Chat evaluates umbrella and excess structures against underlying terms to reveal where aggregation may be larger than pricing assumes. Examples include:

• Follow-form drift: Umbrella claims to follow form but carves out “written contract” limitations—result: AI status applies more broadly up-tower than in the primary.
• Completed operations survivability: Umbrella grants AI for completed ops longer than the primary, creating longer tails for third-party claims.
• Per project/location aggregation: Additional aggregates at the umbrella increase available limits per jobsite or building, affecting cat load.
• Definition mismatches: “Insured,” “you,” and “additional insured” definitions differ between layers, widening insured populations where you least expect.

Doc Chat presents each driver with citations and a concise explanation, plus a CSV you can hand directly to modeling teams. No more manual stitching of umbrella nuances across dozens of files.

“Extract all AI endorsements from policy deck with AI” — yes, including Additional Insured grants

When analysts say “extract all AI endorsements from policy deck with AI,” they typically mean Additional Insured endorsements (not artificial intelligence). Doc Chat recognizes both the intent and the context. It identifies and organizes AI endorsements across the submission, including but not limited to:

• ISO forms such as CG 20 10 (ongoing ops), CG 20 37 (completed ops), CG 20 26, and CG 20 33.
• Blanket Additional Insured endorsements conditioned on written contracts or agreements.
• Project-specific AI endorsements with Per Project Aggregate implications.
• Manuscript AI grants created by brokers or carriers, with nonstandard triggers and durations.

For each AI endorsement, Doc Chat details: who qualifies; the trigger (written contract, scheduled parties, automatic); scope (ongoing vs completed operations); whether Primary & Noncontributory applies; and any Waiver of Subrogation. It then traces whether umbrella/excess layers follow or modify those rights—pinpointing aggregation implications.

Business impact for Exposure Analysts: speed, scale, accuracy

Doc Chat converts the endorsement review workstream from a manual bottleneck into a scalable, repeatable capability. As covered in our piece “The End of Medical File Review Bottlenecks,” Nomad’s platform can process roughly 250,000 pages per minute and maintain consistent accuracy on page 1,500 the same as page 1. The same performance advantages apply to reinsurance submissions, where endorsement inference—not raw OCR—is the determinant of quality.

Quantified impact you can expect:

• Time savings: Reduce endorsement review from days to minutes per submission. Free seasoned analysts to focus on portfolio-level insights.
• Cost reduction: Avoid overtime, staffing spikes, and external vendor review fees during peak renewal season.
• Accuracy uplift: Consistent extraction removes human fatigue and variance, cutting missed endorsements and interpretation errors.
• Scalability: Instantly scale up for surge volumes or special reviews (e.g., portfolio “lookback” on blanket AI or waiver of subrogation across construction business).
• Portfolio intelligence: Aggregated, structured outputs support better cat loading, clash modeling, and treaty strategy.

In our webinar recap “Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI,” a large carrier reported moving from days of manual searching to seconds using Nomad. The same principles drive exposure analysis: question-driven review with page-level citations builds trust and compresses cycle time.

Real-time Q&A across the entire submission

Exposure Analysts can interrogate an entire submission in plain English and get defensible answers with citations. Ask:

• “List all endorsements that grant Additional Insured status and indicate whether Primary & Noncontributory applies.”
• “Identify all Per Project or Per Location aggregate endorsements in primary and whether umbrella follows.”
• “Show manuscript endorsements that alter the definition of ‘insured’ or ‘occurrence’ across any layer.”
• “Where are Waiver of Subrogation rights extended to third parties? Does excess follow?”

Doc Chat returns results with clickable links to the exact source page. Oversight, audit, and internal model governance are simplified because every conclusion is transparent.

From manual to automated: a side-by-side comparison

Manual, repetitive processing

Today, experienced analysts scan submissions, extract endorsements into spreadsheets, and reconcile conflicts by hand. Negative consequences include slow cycle times, inconsistent extractions, and missed aggregation drivers that inflate loss picks or underprice treaties.

End-to-end automation with Doc Chat

Doc Chat ingests, extracts, and cross-checks every page, surfacing each reference to coverage, insured status, and aggregation triggers. It then structures everything for your exposure and pricing workflows. As described in “AI’s Untapped Goldmine: Automating Data Entry,” the ROI from automating repetitive extraction is immediate and compounding—especially where rekeying drives delay and error.

How Doc Chat plugs into the reinsurance exposure workflow

Doc Chat is not a stand-alone summarizer. It’s a workflow engine tailored to your Exposure Analyst playbook:

• Pre-bind reviews: Rapidly assess cedent submissions for endorsement-driven exposure creep before committing capacity.
• Post-bind monitoring: Periodically re-scan updated Endorsement Addenda and bordereaux to catch midterm manuscript changes that alter risk.
• Portfolio sweeps: Run bulk analyses across a book (e.g., all construction risks) to quantify blanket AI prevalence, per project/location aggregates, or waiver of subrogation exposure.
• Retro and commutations: Verify historical exposure assumptions by re-reading archived decks with today’s AI, ensuring accurate negotiation baselines.

Outputs are configurable: CSVs for ingestion into your modeling stack, PDFs for underwriting files, or JSON for direct API integration with internal systems.

Identify coverage gaps in ceded business for reinsurance—before they become losses

Coverage gaps arise when submissions imply one thing but documents say another. Doc Chat cross-checks across the entire submission to identify coverage gaps in ceded business for reinsurance, such as:

• Umbrella that silently broadens AI scope beyond primary.
• Manuscript endorsements that redefine “insured” or expand completed-ops tails.
• Per-project aggregates present in primary but not accounted for in pricing.
• Waiver of subrogation and P&N combinations that magnify net loss.

When Doc Chat finds a gap, it provides the exact language with a concise impact note—for example, “AI applies to any person or organization with whom you have contracted directly or indirectly (see pages 241, 263). Umbrella follows (page 489). Expect increased third-party defense and indemnity participation.”

Why Nomad Data is the best partner for reinsurance document intelligence

Doc Chat stands apart for Volume, Complexity, and The Nomad Process:

• Volume: Ingest entire ceded files—thousands of pages at a time—so reviews move from days to minutes without adding headcount.
• Complexity: Exclusions, endorsements, and triggers often hide in dense, inconsistent policies. Doc Chat digs them out, enabling more accurate exposure estimation and treaty decisions.
• The Nomad Process: We train Doc Chat on your exact playbooks, documents, treatment standards, and modeling inputs. The result is a personalized agent aligned to your team’s workflow.

Implementation is fast. Most teams are live in 1–2 weeks, supported by Nomad’s white-glove service. Start with simple drag-and-drop usage; integrate via API once your analysts are comfortable. Our approach and its business impact are explored in “Reimagining Claims Processing Through AI Transformation” and “AI for Insurance: Real-World AI Use Cases Driving Transformation.”

Security, governance, and auditability that satisfy risk committees

Reinsurance involves sensitive policyholder and counterparty data. Doc Chat is built for enterprise-grade security and auditability, including SOC 2 Type II controls and page-level explainability for every answer. Your data remains yours; foundation model providers do not train on customer data by default. Each extraction is traceable to the exact page, satisfying internal model governance, compliance, and regulator expectations.

Analyst spotlight: daily tasks that Doc Chat accelerates

For an Exposure Analyst, Doc Chat turns routine but critical tasks into one-click jobs:

• Endorsement census: “Produce a list of all AI/PN/WS endorsements, by policy, with applicability and umbrella follow-form status.”
• Aggregation mapping: “Show all per project/per location aggregates and how many aggregates could be accessible in a single event.”
• Definition diffs: “Compare ‘insured’ and ‘occurrence’ definitions across layers and flag mismatches.”
• Manuscript tracker: “Extract all manuscript clauses that expand insured status or broaden covered operations.”

The outputs drop into your pricing memo, exposure model, or treaty committee deck—no additional rekeying.

From pilot to production in 1–2 weeks

Doc Chat’s rollout follows a simple path:

• Pilot: We configure a preset tailored to your exposure lens (e.g., AI/PN/WS and per project/location). Your analysts upload a few real submissions and validate outputs against known answers.
• Refinement: We encode your house rules—how you interpret gray areas—and align outputs to your modeling formats.
• Production: Analysts use Doc Chat for daily pre-bind and portfolio sweeps; IT enables API feeds to your data lake or modeling engine.

Because Doc Chat is a managed solution—not a DIY toolkit—value shows up immediately while we do the heavy lifting behind the scenes.

Frequently asked questions from Exposure Analysts

Can Doc Chat read scanned and poorly labeled files?

Yes. Doc Chat handles mixed-quality scans and inconsistent broker packaging. It auto-classifies document types and recognizes endorsements even when an index is incomplete or misnumbered.

What if an endorsement is referenced but missing?

Doc Chat flags missing attachments and incomplete endorsements (e.g., “CG 20 10 referenced on schedule, not found in attachments”) so you can request corrections pre-bind.

How do we ensure the model understands our interpretations?

We encode your playbooks during onboarding. Doc Chat then applies your rules consistently (for example, your default stance on ambiguous “written contract” language or how you categorize completed ops tails).

Will analysts still review outputs?

Yes. Think of Doc Chat as a tireless junior analyst who reads everything, cites every conclusion, and structures the output. Humans remain in the loop for judgment and escalation.

Putting it all together: a day-in-the-life transformation

Morning: Your broker drops a 1,600-page construction submission. Instead of scanning declarations and form indexes, you upload the entire package to Doc Chat. Within minutes, you have a dashboard showing all AI/PN/WS endorsements, per project aggregates, completed ops tails, and umbrella follow-form exceptions. You export a CSV to your exposure model and a PDF appendix with citations for underwriting.

Afternoon: A treaty committee asks whether blanket AI exposure is rising across your book versus last year. You run a portfolio sweep across saved submissions. Doc Chat produces a comparative trend analysis: blanket AI prevalence rose 12%, with a notable shift toward manuscript AI in the Southeast region. You include charts and sample citations in your deck. No late night spreadsheeting.

Why the old way is no longer defensible

Given submission sizes and the stakes for reinsurers, manual review can no longer keep pace. Endorsement inference is an AI-sweet spot because it demands reading comprehension, context tracking, and consistent application of rules—exactly where humans fatigue. As we argue in “Beyond Extraction,” document intelligence isn’t a technical parlor trick; it’s a new discipline that institutionalizes your best analysts’ unwritten knowledge into scalable, teachable processes.

Get started

If your team is searching for AI for extracting endorsements in cedent policy schedules, wants to identify coverage gaps in ceded business for reinsurance, needs to find umbrella aggregation risk in reinsurance submissions, or aims to extract all AI endorsements from policy deck with AI, the fastest route is to see Doc Chat on your own files. We can stand up a pilot in days and a production workflow in 1–2 weeks.

Learn more and request a demo at Doc Chat for Insurance. Free your Exposure Analysts from manual endorsement hunts and put their expertise where it belongs: on the portfolio, not on the page.