Flagging Coverage Gaps: AI Review of Ceded Policy Endorsements at Scale (Reinsurance) — A Catastrophe Modeler’s Playbook

Catastrophe modeling lives or dies on the quality and completeness of data. Yet in reinsurance, ceded submissions routinely bury critical coverage details in dense Policy Schedules, sprawling Endorsement Addenda, bespoke Policy Manuscripts, and long lists of Additional Insured Endorsements. The result is a high-stakes blind spot: if endorsements extend coverage in ways your models don’t recognize, you can underestimate aggregation and misprice catastrophe exposure at treaty or facultative levels. That’s the challenge. The solution is Doc Chat by Nomad Data — purpose-built AI agents that ingest entire reinsurance submissions, surface hidden endorsements, and make those exposures queryable in seconds.

In this article, we show how catastrophe modelers can use Doc Chat to perform an AI for extracting endorsements in cedent policy schedules at portfolio scale, rapidly identify coverage gaps in ceded business for reinsurance, and even find umbrella aggregation risk in reinsurance submissions. If you’ve ever typed “extract all AI endorsements from policy deck with AI” into a search bar, you’re in the right place. We’ll outline the manual pain, demonstrate how Doc Chat automates end-to-end document review, and quantify the business impact in speed, accuracy, and loss ratio improvement.

Why coverage gaps hide inside ceded submissions (and why cat modelers should care)

Reinsurers price volatility, not averages. When unmodeled extensions and carve-outs live in the long tail of cedents’ documentation, catastrophe modelers get incomplete signals. Consider a typical submission: a mix of bordereaux, SOVs, treaty slips, facultative certificates, and underlying policy decks. The “deck” can be a thousand pages of primary forms, state-specific amendments, and manuscript language. The coverage picture that drives your exceedance curves often lives in the back half of Endorsement Addenda — not in the slip.

For catastrophe modelers, this is particularly acute in:

Additional Insured (AI) endorsements: Blanket AI and per-project aggregate language extend insured status across owners/lessors/contractors and job sites, creating cross-project accumulation potential.
Umbrella/Excess follow-form nuances: “Follow form except as otherwise provided” language, manuscript pollution carve-outs, or flood definition changes can materially shift tail risk.
Per-location vs per-project aggregates: A subtle endorsement can convert portfolio-wide aggregates into multiple independently aggregating limits, amplifying event loss potential.
Wrap-ups and project-specific endorsements: OCIP/CCIP endorsements can roll numerous risks into the same coverage container (clash potential), while per-project aggregates raise questions about stacking.
Contingent and non-owned exposures: Endorsements that extend coverage to leased equipment, JV partners, franchisees, or vendors can multiply locations and relationships beyond the SOV.

Each of these details tweaks the modeled attachment point and tail exposure, yet many never make it into the cat model input schema unless someone actually finds and reads the relevant Policy Manuscripts and Additional Insured Endorsements. The problem is scale: submissions can include hundreds or thousands of policies, each with unique, non-standardized language. Manual review cannot keep up.

How catastrophe modelers handle this manually today

Most reinsurance teams run a hybrid process:

Initial ingestion: Receive zipped folders from cedents containing SOVs/bordereaux, policy decks, and endorsements in mixed formats (PDF, scanned, spreadsheets).
Sampling: Exposure analysts or cat modelers manually open a subset of the Policy Schedules and Endorsement Addenda for “representative” policies, trying to infer patterns.
Keyword search: Use CTRL+F for phrases like “Additional Insured,” “per project aggregate,” “flood,” “pollution,” “named storm,” “contingent BI,” “contractor,” “JV,” or specific ISO references (e.g., CG 20 10 04 13).
Crosswalk to model fields: Attempt to translate endorsement effects into model assumptions—adjusting vulnerability, occupancy, or secondary modifiers; adding location counts; or manually flagging accumulation groups.
Reconciliation: Compare conclusions to treaty wording, retro terms, and peer deals; chase clarifications from cedents; rerun models with manual adjustments.

Even in world-class shops, this approach faces limits:

Coverage drift is easy to miss: Manuscript changes hide in scattered pages; many “representative” policies are not representative.
Portfolio context is lost: You might find one per-project aggregate, but never see that it occurs across a third of the portfolio.
Cycle times balloon: Weeks can pass while policies are reviewed, assumptions debated, and models rerun—slowing quotes and eroding hit ratios.
Human consistency varies: Repetition and fatigue lead to oversight; two analysts can read the same Additional Insured Endorsements and reach different conclusions about accumulation.

Doc Chat: end-to-end automation for endorsement discovery and exposure alignment

Doc Chat for Insurance replaces the manual scavenger hunt with AI agents trained on reinsurance workflows. It ingests entire ceded submissions—thousands of pages of Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, and Policy Manuscripts—and makes the content instantly searchable, extractable, and verifiable at scale. Ask questions in plain English, get page-cited answers, and export structured outputs tailored to your modeling schema.

AI for extracting endorsements in cedent policy schedules

Instead of sampling, run complete extraction against every policy deck. Doc Chat reads each policy and endorsement, identifies coverage-modifying language, and normalizes it into structured fields (e.g., “Blanket AI,” “Per Project Aggregate,” “Follow-Form Exceptions,” “Pollution Carve-outs,” “Named Storm Definitions”). It then links those fields back to the exact pages and paragraphs where they were found, so modelers and underwriters can verify any conclusion in seconds.

Sample prompts a catastrophe modeler can use right away:

“List all Additional Insured endorsements and indicate whether they are blanket or scheduled. Provide page citations.”
“Identify endorsements that create per project or per location aggregates. Summarize the aggregation mechanism and limits.”
“Does the umbrella ‘follow form’ except for any specific exclusions? Extract the ‘except as otherwise provided’ language with citations.”
“Compare the named storm or flood definitions across all policies. Flag any manuscript variations.”
“Extract waiver of subrogation, primary and noncontributory clauses, and any contractor warranties with page cites.”

Identify coverage gaps in ceded business for reinsurance

Coverage gaps become visible when endorsement data is complete and standardized. Doc Chat surfaces inconsistencies across a portfolio—e.g., a subset of policies extends AI to owners/lessors with primary/noncontributory wording while others require parity with the Named Insured’s coverage. It highlights manuscripts that broaden pollution coverage or alter water peril definitions, and it flags places where per-project aggregates convert single-policy aggregates into multiple event-triggered buckets. This is exactly how you identify coverage gaps in ceded business for reinsurance before you model and price.

Doc Chat’s cross-checking catches contradictions between schedules, endorsements, and manuscripts. For example, the Policy Schedule might show a $2M per occurrence aggregate, while a Policy Manuscript quietly adds per-project aggregates that effectively stack limits across concurrent jobs—an accumulation risk your model must reflect.

Find umbrella aggregation risk in reinsurance submissions

Umbrella and excess policies often say “follow form,” but endorsements and manuscripts change the practical meaning. Doc Chat isolates follow-form exceptions and traces their interaction with underlying endorsements. It reveals when a blanket AI endorsement in the GL follows into the umbrella, or when an umbrella’s manuscripted flood definition (or pollution carve-out) diverges from the primary and increases or decreases modeled tail risk. This makes it straightforward to find umbrella aggregation risk in reinsurance submissions and quantify clash exposures across Named Insureds, AIs, projects, and counterparties.

Examples Doc Chat will return in seconds with citations:

“Umbrella Form X-UM-01: ‘Follow form except as otherwise provided’ — excepts pollution exclusion with manuscript carve-back (page 241).”
“Primary GL: CG 20 10 04 13 Blanket AI + Primary/Noncontributory (pages 45–47). Umbrella endorsements confirm follow-form on AI status (pages 312–316).”
“Per Project Aggregate endorsement (CG 25 03) applies to all jobs over 60 days (page 133).”

“Extract all AI endorsements from policy deck with AI”

Many modelers literally search for “extract all AI endorsements from policy deck with AI.” Doc Chat treats “AI” in this context as Additional Insured endorsements and assembles a full inventory by policy and endorsement number. It distinguishes between scheduled AI vs. blanket AI, identifies primary/noncontributory language, and flags conflicts or conditional wording (“when required by written contract”). It also connects those AI endorsements to project-specific or per-location aggregate endorsements, showing how the combination changes accumulation potential and catastrophe tail behavior.

What Doc Chat does behind the scenes

Nomad Data has designed Doc Chat to tackle precisely the barriers that make endorsement review so difficult in reinsurance. The key ingredients include:

Volume at reinsurance scale: Doc Chat ingests entire submission folders—thousands of pages at a time—and processes every page. No sampling required.
Complexity and variability: It recognizes ISO forms, state-specific amendments, and deeply customized Policy Manuscripts, pulling coverage concepts even when there is no standardized field.
The Nomad Process: We train Doc Chat on your playbooks and reinsurance standards, personalizing extraction outputs to your treaty pricing and catastrophe modeling schema.
Real-time Q&A: Ask questions like “Which endorsements change aggregation behavior?” or “Where is blanket AI extended to owners/lessees?” and receive answers with page-level citations across the whole submission.
Thorough and complete: The system surfaces every reference to coverage extensions, exclusions, and triggers so nothing important slips through the cracks—critical for preventing leakage and mispriced cat loads.

If you want a deeper dive into why “document scraping” requires inference rather than simple field extraction (especially for manuscripts), see Nomad’s perspective in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

From unstructured endorsements to cat-model-ready intelligence

For catastrophe modeling purposes, the goal isn’t just to find endorsements; it’s to operationalize them. Doc Chat translates qualitative coverage language into quantitative signals you can push into exposure systems and cat models:

Endorsement inventory: A consolidated list by policy: Additional Insured, per-project/per-location aggregates, primary/noncontributory, waiver of subrogation, wrap-ups, follow-form exceptions, flood/pollution/named storm definitions, contractor warranties, JVs, franchises, and more.
Exposure mapping: Flags endorsements that imply non-SOV exposures (leased equipment, franchisees, vendors), expanding the set of modeled relationships or accumulation groups.
Aggregation behavior: Identifies language that creates multiple aggregating buckets (per project/per location) or alters stacking/anti-stacking behavior relevant to tail outcomes.
Umbrella interaction: Detects whether endorsements and definitions follow into the umbrella/excess, alter retainers/deductibles, or create unexpected event triggers.
Structured outputs: Exports to your schema for automation—CSV/JSON fields you can reconcile with SOVs/bordereaux and push into cat modeling pipelines.

This is precisely how reinsurers speed up due diligence on ceded books. For a broader view of portfolio-level automation across reinsurance workflows, review Nomad’s article, AI for Insurance: Real-World AI Use Cases Driving Transformation (see the sections on “Assessing Risk in Books of Business” and “Reinsurers and Risk Assessment at Scale”).

Quantified impact for catastrophe modelers and reinsurance teams

When endorsement review becomes complete, verified, and fast, catastrophe modeling improves along every dimension:

Cycle time: Replace weeks of sampling and back-and-forth with minutes. Nomad routinely moves reviews from “days to minutes,” as highlighted in our client stories and webinar recaps. The impact compounds when you’re quoting across multiple cedents simultaneously.
Pricing accuracy: With reliable endorsement intelligence, modeled tails reflect actual aggregation behavior, reducing underestimation of clash/AI exposure and decreasing reliance on manual loadings.
Leakage reduction: Hidden coverage extensions and follow-form exceptions drive unexpected losses; surfacing them early reduces leakage and stabilizes loss ratios.
Scalability: Handle surge submission volumes (renewal season, market dislocations) without overtime or extra headcount.
Defensibility: Every AI-generated conclusion is page-cited. Pricing and modeling decisions become more auditable for internal governance, reinsurers, regulators, and retro partners.

For context on speed and accuracy at massive document scale, see the transformation stories in Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI. While that example centers on claims, the same Doc Chat infrastructure underpins reinsurance endorsement review—instant answers, page links, and audit-friendly transparency.

A realistic portfolio example: endorsement-driven accumulation risk

Imagine a North America property and casualty reinsurance treaty with 14 cedents, 3,800 underlying GL and umbrella policies, and 220,000 pages of combined Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, and Policy Manuscripts. The reinsurer wants to understand whether accumulation can occur across:

Multiple construction projects with per-project aggregates and blanket AI endorsements
Real estate portfolios with managers added as AIs on tenant policies (primary/noncontributory)
Service franchises (e.g., quick-serve restaurants) where franchisors are blanket AIs
Industrial clients with follow-form umbrellas that alter pollution or flood definitions

With Doc Chat, the team runs a full endorsement extraction, receiving a structured table per policy:

AI endorsement present? Blanket vs scheduled; contractor vs owner/lessor; primary/noncontributory; waiver of subrogation; “when required by written contract” conditionality
Aggregation language: per location; per project; stacking/anti-stacking clauses; per site aggregates by time threshold
Follow-form exceptions in umbrellas: pollution carve-backs; named storm definitions; flood sublimits or exclusions
Manuscript alerts: non-standard terms altering occurrence triggers, hours clauses, or perils definitions

Doc Chat reconciles these with the SOV/bordereau to flag where non-SOV relationships imply potential accumulation (e.g., an owner or franchisor named as AI across multiple insureds in the same MSA). The catastrophe modeler then re-assigns aggregation groups and adjusts event loss distributions accordingly. Output goes to the pricing actuary and underwriter with page citations so the entire deal team can align on assumptions. The reinsurer proceeds with confidence: modeled tails reflect the endorsements actually in force, not the idealized policy outline.

Why Nomad Data is the best solution for reinsurance endorsement review

Doc Chat isn’t generic OCR or off-the-shelf search. It’s a suite of AI agents engineered for insurance documentation and trained via The Nomad Process to match your team’s standards. Here’s what sets Nomad apart for catastrophe modelers and reinsurance underwriters:

Purpose-built for complexity: We extract concepts from inconsistent manuscripts, not just fields from forms. That matters when per-project aggregates or AI language lives in custom paragraphs no keyword could reliably catch.
Whole-file ingestion: We process thousands of pages per submission, across every cedent and policy, so you stop relying on samples. Every page is reviewed; nothing is “assumed away.”
Your rules, institutionalized: We embed your playbooks—how you define accumulation, what you treat as follow-form exceptions, how you map endorsement effects into model inputs—so the output fits your workflow immediately.
Real-time Q&A: Ask “Show me all policies with ‘per project aggregate’ and whether those aggregate limits stack across jobs” and get answers with citations you can trust.
Audit-grade transparency: Every extraction links back to source pages, eliminating black-box skepticism and smoothing internal governance.
Security and compliance: Nomad Data maintains enterprise-grade security controls (including SOC 2 Type 2), designed for carrier and reinsurer data governance needs.

To understand why this is not “just OCR,” read Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs. The core challenge in reinsurance endorsement review is inference across messy manuscripts; that’s precisely the problem Nomad set out to solve.

Implementation: white-glove onboarding in 1–2 weeks

Success in reinsurance depends on adoption without disruption. Nomad’s implementation roadmap is deliberately light:

Discovery (days 1–3): Share sample submissions and desired outputs. We capture your endorsement taxonomy (AI, per project/per location, P&NC, WOS, follow-form exceptions, pollutant/named storm definitions). We align on cat-model mapping and export formats.
Playbook training (days 3–7): We configure Doc Chat to your rules and workflows—what to flag, which citations to surface, how to reconcile with SOV/bordereau fields.
Pilot processing (days 7–10): Drag-and-drop a few real submissions; we validate extractions together, calibrating any manuscript nuances.
Scale (days 10–14): Start running full submissions. Optional integration with your exposure systems via API for automated exports into modeling pipelines.

This is hands-on, white-glove work. You get a partner, not just software. Nomad’s team has repeatedly seen that the fastest ROI often comes from automating “data entry”—in this case, transforming unstructured endorsements into structured, model-ready intelligence—so we optimize for value realized in the first two weeks.

Trust, verification, and governance

For catastrophe modelers, trust means page-level explainability, repeatability, and safe operations. Doc Chat is built with those requirements in mind:

Page-cited answers: Every conclusion is traceable to the original Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, and Policy Manuscripts.
Consistency at scale: Unlike human reviewers, the AI does not fatigue. It reads page 1 and page 1,000 with the same rigor.
Human-in-the-loop: Treat Doc Chat like a highly capable junior analyst. It does the reading and extraction; you set the rules and make the decisions. This model improves speed without sacrificing judgment.
Security posture: Enterprise-grade controls, including SOC 2 Type 2. Nomad does not train foundation models on your data by default.

These principles mirror the ethos discussed in our claims and medical-file case studies—speed with defensibility—see The End of Medical File Review Bottlenecks and Reimagining Claims Processing Through AI Transformation.

Common questions from catastrophe modelers

How does Doc Chat handle non-standard, manuscript-heavy policies?

Doc Chat is trained to infer concepts from bespoke language. It is not limited to predefined fields or ISO-only forms. It builds a semantic understanding of endorsements—e.g., recognizing when a manuscript modifies pollution exclusions or redefines “named storm”—and labels those concepts in structured outputs with citations.

Will Doc Chat miss something if a cedent uses unusual wording?

We configure Doc Chat using your playbooks and past examples, and we calibrate against your real submissions during onboarding. Because every answer is traceable to pages, reviewers can quickly confirm interpretations and adjust rules iteratively. Over time, the system captures more of your team’s unwritten heuristics, reducing variance and blind spots.

What about mapping to cat-model input fields?

We align extraction fields to your exposure schema (e.g., flags for per-project/per-location aggregation, AI presence and type, follow-form exceptions, peril-definition variations). We can deliver CSV/JSON aligned with your exposure system, easing ingestion into modeling workflows and pricing tools.

Can it reconcile endorsements with SOV/bordereau content?

Yes. Doc Chat cross-references endorsement-driven relationships (AIs, franchisees, JV partners) with SOV/bordereau records to surface non-SOV exposures and potential accumulation clusters (e.g., franchise chains within a metro area). It identifies mismatches where coverage suggests additional aggregation groups.

How fast can we get value?

In many cases, within days. Teams often start by drag-and-dropping a few submissions into Doc Chat and immediately running Q&A. Full, white-glove rollout typically lands in 1–2 weeks, including customized extraction formats and API integrations if desired.

Putting it all together: the catastrophe modeler’s new workflow

With Doc Chat, endorsement intelligence becomes a first-class input to pricing, not an afterthought:

Ingest: Upload the cedent’s full submission folder. Doc Chat classifies and reads every file: Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, and Policy Manuscripts.
Extract: Run a preset built for reinsurance endorsement discovery. Receive structured outputs for AI presence, aggregation behavior, follow-form exceptions, and peril-definition changes, with citations.
Interrogate: Ask targeted questions—“Show per project aggregates by NAICS and MSA,” “Where does named storm deviate from ISO?,” “Which umbrellas truly follow AI status?”—and validate with single-click page links.
Reconcile: Map endorsement-driven relationships to SOV/bordereau; create accumulation clusters; update cat model inputs. Export to your pricing pipeline.
Decide: Quote with confidence. Your modeled tails match real coverage mechanics.

The difference is profound: endorsement complexity stops being a source of modeling uncertainty and becomes a quantifiable, auditable feature of the risk.

Business outcomes you can expect

Reinsurers who operationalize endorsement intelligence with Doc Chat consistently report:

Faster quotes and better hit ratios: Endorsement review no longer drags renewals; you move first with defensible positioning.
Improved catastrophe load accuracy: You reduce manual loadings and reflect actual aggregation structures, improving competitiveness without sacrificing prudence.
Lower leakage and fewer surprises: Hidden coverage extensions are detected pre-bind, not discovered post-event.
Happier teams, stronger retention: Cat modelers and exposure analysts spend time on strategy and scenario analysis, not page-flipping and keyword hunts.
Portfolio-level insight: Quickly identify cedents whose endorsement posture drives outsized aggregation risk; tailor terms, exclusions, or pricing accordingly.

The macro lesson matches Nomad’s broader insurance experience: automate the reading and extraction, let humans focus on judgment. As we noted in AI for Insurance: Real-World AI Use Cases Driving Transformation, insurers and reinsurers that systematize unstructured-document intelligence build a durable edge.

Next steps: turn endorsements into competitive advantage

If your catastrophe models rely on samples and assumptions to deal with endorsement variation, you’re leaving accuracy—and margin—on the table. Doc Chat gives reinsurance teams a way to analyze every page, across every cedent, on every renewal, without expanding headcount.

Whether your top priority is “AI for extracting endorsements in cedent policy schedules,” a mandate to “identify coverage gaps in ceded business for reinsurance,” an urgent need to “find umbrella aggregation risk in reinsurance submissions,” or simply the practical goal to “extract all AI endorsements from policy deck with AI,” the path is the same: end-to-end automation, page-cited answers, and outputs aligned to your catastrophe modeling schema.

See how quickly your team can move from document chaos to endorsement clarity. Explore Doc Chat for Insurance, and ask us about a white-glove pilot that gets you live in 1–2 weeks.