Flagging Coverage Gaps: AI Review of Ceded Policy Endorsements at Scale — Built for Catastrophe Modelers in Reinsurance

Catastrophe modelers live and die by the fidelity of policy terms. In reinsurance submissions, coverage is often defined less by the headline declarations and more by what’s buried across Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, and lengthy Policy Manuscripts. The challenge is simple to state and notoriously hard to solve at scale: find every endorsement, interpret its impact on peril, limit, sublimit, deductible, and aggregation, and translate that into accurate modeled exposure. Miss even one Additional Insured expansion or a subtle sublimit carve-out, and portfolio losses can diverge from modeled results.

This is exactly where Doc Chat by Nomad Data changes the game. Doc Chat’s purpose‑built, AI‑powered agents read entire reinsurance submissions end-to-end—thousands of pages at a time—then answer questions in real time, extract structured fields for catastrophe models, and surface hidden endorsements, exclusions, and trigger language that quietly reshape risk. For catastrophe modelers, the result is fewer blind spots, fewer unmodeled exposures, and treaty results that align with reality instead of wishful thinking.

The nuance: why endorsements break catastrophe modeling for reinsurance

When a cedent submits a book for treaty support—whether property cat, all‑risk with sublimits, or casualty layers that can clash—modelers need more than an SOV. The terms determine modeled outcomes: the hours clause definition, occurrence language, peril-specific deductibles, flood zones, Ordinance or Law A/B/C treatment, named storm wording, wind-driven rain inclusions/exclusions, service interruption, contingent time element, civil authority, and waiting periods. For casualty-driven accumulations, Additional Insured (AI) endorsements and Blanket AI grants can explode the footprint of exposure across geographies and counterparties, creating clash scenarios that don’t appear on a property SOV.

Endorsements are the friction point:

• Following-form Umbrella and Excess: subtle divergences in drop-down language, aggregates by project or designated premises, or who is an insured expansions (e.g., CG 20 10, CG 20 37) alter aggregation risk.
• Manuscript Property Forms: tailored sublimits for perils like flood outside SFHA, EQ sprinkler leakage, named storm in Tier 1 counties, and time element coverage that may or may not follow the property limit.
• Cat-critical carve-outs: communicable disease, cyber, war/SRCC, or utility services endorsements that create or restrict modeled loss drivers.
• Deductible complexity: percent-of-value wind deductibles by occupancy, construction class, coastal distance, or zip; different waiting periods across time element coverages.
• Aggregation mechanics: 72/96/168-hour clauses by peril, interlocking clauses, annual aggregates, and sublimit exhaustion rules that materially affect reinsurance recoveries.

For a catastrophe modeler, each of these terms must map to model inputs and financial modules. Yet the needed clauses are scattered among Policy Schedules, Endorsement Addenda, e-mails, and scanned PDFs. Multiply by 5,000 policies in a ceded portfolio, each with its own manuscript quirks, and manual review becomes a bottleneck—and a source of model risk.

How catastrophe modelers handle this manually today

Most reinsurance teams still rely on analysts to manually comb through submission packages to reconcile terms with modeling assumptions. The typical workflow looks like this:

Document collection and triage: the cedent provides SOVs, bordereaux, Policy Schedules, Endorsement Addenda, Policy Manuscripts, certificates, and occasionally loss runs and engineering reports. Analysts open each PDF, locate relevant endorsements, and copy terms into spreadsheets or modeling notes.

Term identification and mapping: for every policy, the team tries to find peril-specific deductibles, sublimits, occurrence/aggregation language, civil authority and ingress/egress coverage, service interruption details, contingent business interruption triggers, Ordinance or Law A/B/C coverage, Named Storm wording, and whether flood/EQ are excluded, limited, or fully covered. On casualty/umbrella, they hunt for Additional Insured endorsements, Blanket AI, Designated Construction Project General Aggregate, Primary and Noncontributory, Waiver of Subrogation, and any manuscript expansions of who is an insured.

Normalization to model schema: terms are translated to RMS, Verisk (AIR/Touchstone), or internal model schemas—mapping waiting periods, hours clause assumptions, peril/coverage codes, deductible hierarchies, and occurrence definitions to the financial model.

Reconciliation and QA: the extracted terms are cross-checked against cedent statements, rating plans, prior treaties, and bordereaux fields. Discrepancies trigger e-mail back-and-forth with cedents, delaying cycle time and creating version control risk.

Even for elite teams, this process is:

• Slow: weeks of reading and re-keying text across a book; surge submissions overwhelm capacity.
• Expensive: high-skill analysts spend hours on repetitive extraction instead of model calibration and sensitivity analysis.
• Error-prone: fatigue leads to missed endorsements, inconsistent mapping, and unmodeled aggregation pathways.
• Hard to scale: each new ceded portfolio requires more people—or cuts in diligence depth.

AI for extracting endorsements in cedent policy schedules

Doc Chat ingests entire submission packages—Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, Policy Manuscripts, bordereaux, loss runs, SOVs—and builds a unified, searchable index. Ask a plain-English question such as, “List all named storm deductibles by location and occupancy,” or “Does Flood follow property limits or is it sublimited?” and Doc Chat returns the answer with page-level citations that click back to the exact language. This is not generic summarization; it’s purpose‑built extraction, normalization, and cross-checking against your modeling schema.

Doc Chat’s advantages for catastrophe modelers include:

• Volume: ingest thousands of pages per submission—entire books at once—so you analyze every policy, not just a sample.
• Complexity: find exclusions, endorsements, and trigger language buried in inconsistent formats and manuscript forms; surface terms that shift peril-based losses.
• Real-Time Q&A: pose modeling questions (“What’s the hours clause by peril?” “Which policies carry Ordinance or Law C?” “Which locations have service interruption coverage and what’s the waiting period?”) and get instant answers with citations.
• Schema mapping: extract terms into RMS/AIR/Verisk-ready fields—deductibles, sublimits, hours clause, occurrence definitions—reducing manual translation work.
• Cross-document inference: when terms are implied by endorsements rather than declared on the dec page, Doc Chat assembles the inference chain so you can model it confidently.

extract all AI endorsements from policy deck with AI

“AI endorsements” are a double entendre here: you want artificial intelligence to extract every Additional Insured (AI) grant across a policy deck. Doc Chat hunts for Blanket Additional Insured language, scheduled AI lists, and project‑specific endorsements, then compiles:

• Who qualifies as an Additional Insured (owners, lessees, contractors, vendors),
• Where coverage applies (designated premises, project schedules, across locations),
• How aggregation behaves (project aggregate, premises aggregate, or shared general aggregate),
• Priority of coverage (Primary and Noncontributory), and
• Any drop-down mechanics in umbrella/excess forms.

For a catastrophe modeler, these AI endorsements can expand the insured footprint—think a retailer’s vendors or a contractor’s project owners—creating clash pathways in catastrophe or casualty scenarios. Doc Chat surfaces them at scale, so you can quantify aggregation risk, not just note it.

identify coverage gaps in ceded business for reinsurance

Coverage gaps often hide in the interplay between main form and endorsements. Doc Chat automatically flags mismatches like:

• Peril carve-outs not reflected in SOV assumptions (e.g., Flood covered only outside SFHA; EQ limited to sprinkler leakage; Named Storm limited in Tier 1 coastal ZIPs).
• Time element gaps: waiting periods silently extended for service interruption; civil authority limited to 1 mile when modeling assumed 5 miles; ingress/egress absent unless ordered by civil authority.
• Ordinance or Law inconsistencies: Coverage A included but B/C excluded; sublimits attach separate aggregates.
• Occurrence definition differences by peril: 72 hours for wind, 168 hours for flood; interlocking or rolling windows.
• Umbrella following-form exceptions that remove coverage assumed to drop down in modeled scenarios.

Doc Chat does more than extract; it cross-checks the extracted terms against cedent-provided bordereaux fields, calling out inconsistencies. When an SOV signals “All Risks inclusive of Flood,” but endorsements limit Flood to $250K with a 5-day waiting period for service interruption, Doc Chat highlights the mismatch and anchors it to page citations. You get defensible reasons to adjust peril modules, terms, or rating assumptions.

find umbrella aggregation risk in reinsurance submissions

For casualty-driven accumulations and clash modeling, umbrella and excess endorsements are where aggregation hides. Doc Chat identifies:

• Who is an insured expansions that extend coverage to entities not listed in the schedule.
• Project aggregates and designated premises limitations that alter how losses stack.
• Primary/Noncontributory wording that reshapes how policies respond across counterparties.
• Drop-down mechanics where umbrella fills gaps in underlying policies—changing modeled frequency and severity.
• Manuscript strikes/edits to standard ISO terms, including definitions that widen insured status or coverage triggers.

Because Doc Chat reads the entire deck—Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, Policy Manuscripts—it can thread together implications the cedent never summarizes. For the catastrophe modeler, that means a clearer view of clash hotspots across named and additional insureds, with the ability to export normalized fields for ingestion into your clash or aggregate models.

From manual to automated: what changes with Doc Chat

Instead of analysts digging for needles in PDFs, Doc Chat automates end‑to‑end review and puts modelers in control of answers:

1) Ingest and classify — Drag-and-drop or pipeline your submission: SOVs, bordereaux, Policy Schedules, Endorsement Addenda, Policy Manuscripts, Additional Insured Endorsements, loss runs. Doc Chat classifies documents by type and policy.

2) Extract and normalize — The agent pulls deductibles, sublimits, peril definitions, occurrence/hours clause, waiting periods, time element triggers, Ordinance or Law details, and AI/blanket endorsements. It normalizes output into your modeling schema (RMS, Verisk/AIR, Touchstone, or an internal format).

3) Cross-check and reconcile — Extracted terms are compared to cedent-provided fields in bordereaux/SOVs; discrepancies are flagged with page-level evidence and reasoning. Doc Chat highlights missing or conflicting data and proposes queries for cedents.

4) Real-time Q&A — Ask “Which policies include civil authority coverage beyond 5 miles?” or “Where is flood sublimited to under $500K?” and receive answers with citations and a downloadable, structured table.

5) Export and integrate — Push the normalized terms and flags to your model pre‑processor, data lake, or rating tools via API. Doc Chat becomes the intake and preparation layer for cat modeling and treaty analytics.

What catastrophe modelers can ask Doc Chat—live, on any submission

Because Doc Chat supports real-time, page-cited answers, modelers can interrogate the file like a colleague:

• “List Named Storm deductibles by location, and mark those with percent-of-value by occupancy class.”
• “Extract all time element coverage triggers and waiting periods for service interruption, contingent business interruption, and civil authority.”
• “Summarize Ordinance or Law coverage by policy (A/B/C, sublimits, aggregates).”
• “Identify policies where flood is excluded inside SFHA but included outside, with sublimits.”
• “List the hours clause and occurrence definition by peril for each policy.”
• “extract all AI endorsements from policy deck with AI and classify as blanket or scheduled; show any ‘Primary & Noncontributory’ wording and project aggregates.”
• “Find endorsements that alter who is an insured under the umbrella; specify drop-down exceptions.”
• “Show all endorsements that reference wind-driven rain, roof covering, or leakage exclusions impacting wind loss.”

Every answer includes a link that takes you to the exact page, eliminating debate and accelerating underwriting, modeling, and pricing. For an example of how page-level citations build trust and speed, see the GAIG story in our webinar recap: Reimagining Insurance Claims Management.

Structured outputs tailored for RMS/AIR/Touchstone and internal models

Doc Chat isn’t just a reader—it’s an extraction and transformation engine. We configure outputs to match your modeling ecosystem:

• Perils and coverages: EQ, Flood, Named Storm, Wind/Hail, Tornado/Hail, Fire/All Other Perils; property, time element, ordinance or law A/B/C, service interruption, contingent BI, civil authority.
• Deductibles: percent-of-value by peril, minimum/maximum, by occupancy/construction/distance to coast; per-location vs per-occurrence.
• Sublimits and aggregates: peril-specific sublimits, annual aggregates, project aggregates, designated premises limits.
• Occurrence/aggregation: hours clause by peril, interlocking provisions, waiting periods for time element.
• Umbrella/casualty terms: who is an insured, Additional Insured classes, Primary/Noncontributory, waiver of subrogation, drop-down mechanics.

The output can be delivered as CSV, JSON, or pushed via API into pre‑processors that feed RMS or Verisk/AIR. For reinsurers conducting diligence on books of business, see how we streamline portfolio-scale analysis in our overview of AI for Insurance: Real-World Use Cases.

How Doc Chat finds the signal in messy manuscripts

Endorsements rarely state, “This policy’s modeled Flood loss should be capped at $250K with a 5-day waiting period for service interruption.” Instead, the signal appears as scattered clauses: an exclusion on page 14, a sublimit on page 61, a manuscript definition on page 103, and a carve-back in an addendum. Doc Chat’s advantage is inference at scale—it assembles those breadcrumbs into a coherent term set and explicitly shows its work.

If you’ve ever wondered why “document scraping” isn’t just web scraping for PDFs, we recommend our piece Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs. In short: web pages present fields; manuscripts require inference and cross-reference—the very tasks Doc Chat was engineered to automate.

Business impact for catastrophe modelers and reinsurance analytics

For a reinsurance analytics team, accuracy and speed are both critical. Doc Chat delivers on both, with measurable benefits:

Time savings: Move from weeks of manual review to minutes of automated extraction. Doc Chat routinely ingests thousands of pages per submission and provides usable, schema‑mapped outputs the same day. According to our clients and published results, processes that took days now take minutes, freeing modelers to focus on uncertainty analysis and portfolio strategy.

Cost reduction: By eliminating repetitive reading and re-keying, Doc Chat reduces loss-adjustment style overhead within underwriting and modeling teams. Fewer outside specialists are needed to decode manuscripts, and surge volumes no longer demand temporary staffing.

Accuracy and completeness: Machines don’t fatigue. Doc Chat reads page 1,500 with the same rigor as page 1 and returns page-cited outputs. It consistently surfaces the coverage, exclusion, and endorsement references that drive catastrophe loss. The result: fewer missed terms, fewer unmodeled exposures, and tighter alignment between modeled and actual recoveries.

Scalability: Cat season and renewal crunch no longer throttle diligence depth. Doc Chat scales instantly, allowing full‑book reviews rather than sampling. That means better pricing, more confident capacity decisions, and faster turnarounds for cedents.

We’ve documented these gains broadly in insurance and claims operations—see The End of Medical File Review Bottlenecks and AI’s Untapped Goldmine: Automating Data Entry—and the same dynamic applies in reinsurance submissions with policy terms and endorsements.

White-glove setup, trained on your playbooks, live in 1–2 weeks

Every reinsurer and broker has unique modeling conventions and reviewer heuristics. Doc Chat is trained on your playbooks: how you map waiting periods to time element modules, how you interpret hours clauses by peril, your preferred rules for flood/EQ sublimits, and your schema for RMS/AIR. We configure the extraction logic and the export format so outputs “fit like a glove.”

Implementation is measured in days, not quarters. Most teams start with drag‑and‑drop usage on day one and integrate to internal systems within 1–2 weeks via modern APIs. As adoption grows, we enable single sign‑on, DMS connectors, and automated feeds from cedent portals. Throughout, Nomad’s team provides white-glove service—from rule elicitation and prompt tuning to ongoing model calibration—so your analysts can focus on the business, not the plumbing.

Defensible answers: audit trails and page-level citations

Modelers and underwriters must justify their assumptions to committees, actuaries, regulators, and cedents. Doc Chat provides document-level traceability and page-level citations for every extracted term and every answer. This creates a transparent audit trail that supports internal governance, reinsurer/retro partners, and regulatory requirements. Information security is foundational—Nomad Data maintains SOC 2 Type 2 controls and never trains foundation models on your data by default.

Examples: what Doc Chat surfaces that humans often miss

Across reinsurance submissions, we consistently see the following patterns—each one materially impacts catastrophe or clash results:

• Named Storm vs. Wind/Hail: policies that impose a percent-of-value deductible only for “Named Storm,” leaving non-named wind treated differently than assumed in base modeling.
• Service Interruption limits: small sublimits or longer waiting periods than modeled; off-premises service interruption missing in forms that appear to include it.
• Ordinance or Law B/C: assumed unlimited but actually sublimited, or tied to separate aggregates that exhaust quickly in clustered events.
• Flood bifurcation: included outside SFHA but excluded inside, with special treatment for basements or first-floor improvements—critical for urban portfolios.
• Hours clause drift: EQ at 168 hours, wind at 72, flood at 120; umbrella wording that changes aggregation across multiple occurrences.
• Additional Insureds: blanket vendor coverage or owner/lessor endorsements that spread casualty risk across networks of counterparties—and trigger Primary & Noncontributory duties.
• Drop-down exceptions: umbrellas that do not follow underlying for certain perils or classes—contrary to modeling assumptions.
• Manuscript definitions: “Occurrence” or “Property Damage” definitions diverging from ISO, changing how losses stack.

Doc Chat not only identifies the text but explains the implication pathway—linking the endorsement to the coverage trigger, to the applicable sublimit/deductible, and to the aggregation rule.

Operationalizing insights: from endorsement to model input

Once Doc Chat extracts terms, it can produce ready-to-load artifacts for your workflow:

• Term tables by policy, peril, coverage, deductible, sublimit, waiting period, hours clause, and aggregation rules.
• Discrepancy reports comparing extracted terms to cedent-declared fields in bordereaux/SOVs.
• Additional Insured maps summarizing who qualifies, where coverage applies, and how aggregation behaves—inputs to clash modeling.
• Normalization scripts that transform free-text endorsements into RMS/AIR codes.
• QA packages with page-cited evidence for committee or partner review.

These deliverables plug into model pre‑processors, pricing tools, and exposure management systems so that what you extract is what you model.

Why Nomad Data’s Doc Chat is different

Most “document extraction” tools were built for simple forms. Reinsurance submissions are not simple—they’re a weave of ISO forms, carrier endorsements, and manuscripts where the rules aren’t written down. Nomad’s approach is different:

• Built for complexity: Detects endorsements and trigger language across inconsistent, scanned, or image-heavy PDFs. It performs cross-document inference to assemble terms that only emerge from multiple pages.
• Trained on your playbooks: We encode your modeling rules, underwriting guidelines, and schema conventions so outputs align to how your team models and prices risk.
• Real-time, page-cited answers: Modelers can interrogate any submission conversationally and receive citations that stand up to audit.
• Speed to value: White-glove onboarding; 1–2 week implementation; immediate drag‑and‑drop usage; modern APIs for quick integration.
• Partnership: You’re not buying generic software. You’re gaining a strategic partner who evolves with your treaty seasons and modeling changes.

For a deeper dive into how AI is transforming claims and document-heavy insurance workflows—lessons directly applicable to reinsurance submissions—see Reimagining Claims Processing Through AI Transformation.

Addressing common concerns: hallucinations, security, and governance

Catastrophe modelers rightly demand reliability and security. Doc Chat mitigates the big risks:

• Evidence-backed outputs: Every extracted field is linked to source text with page-level citations. If a clause isn’t in the file, Doc Chat shows that too.
• Security: SOC 2 Type 2 controls, role-based access, and options for private deployments. Your documents are your data.
• Governance: Detailed audit trails of prompts, answers, and versions. Output validation workflows can require human confirmation before model ingestion.

In our experience, hallucinations are rare in constrained, document-grounded tasks like endorsement extraction. As we explain in our blog on automating data entry, the economics and reliability of document-grounded AI are compelling for exactly these high-volume tasks—see AI’s Untapped Goldmine.

Putting it all together: a modeler’s day with Doc Chat

Imagine a renewal week where three cedents deliver 10,000 pages each. Instead of triaging by what you can get to, you upload every submission. Within minutes, Doc Chat returns:

• A normalized table of deductibles, sublimits, hours clause, and waiting periods by policy/peril/coverage.
• A discrepancy log highlighting 127 places where SOV/bordereaux fields conflict with endorsements.
• An Additional Insured report marking five cedents with Blanket AI expansions likely to increase clash exposure.
• Page-cited packets for committee, so underwriting can explain every adjustment and assumption.

You then ask follow-up questions—“Which policies have flood within SFHA?” “Show all civil authority distances under 1 mile.” “Which umbrellas do not follow underlying for P&C?”—and get answers with citations. You export the final term table to RMS/Verisk, press ‘run,’ and spend your time on sensitivity testing instead of sifting PDFs.

Search-driven scenarios Doc Chat solves out of the box

We deliberately tuned Doc Chat to align with the most common reinsurance and catastrophe modeling intents we hear from clients. That includes the very queries professionals are searching for:

AI for extracting endorsements in cedent policy schedules

Doc Chat crawls Policy Schedules and Endorsement Addenda to compile every clause that affects peril, coverage, deductibles, and aggregation mechanics—ready to map to model inputs.

identify coverage gaps in ceded business for reinsurance

Cross-check extracted terms with cedent-declared values to flag where coverage is excluded, sublimited, or subject to waiting periods that invalidate base model assumptions.

find umbrella aggregation risk in reinsurance submissions

Uncover Additional Insured expansions, project aggregates, Primary & Noncontributory obligations, and drop-down exceptions that create clash and accumulation risk across counterparties.

extract all AI endorsements from policy deck with AI

Use artificial intelligence to capture every Additional Insured (AI) endorsement—blanket and scheduled—across the Policy Manuscript and addenda, with a roll-up of how those endorsements impact who is an insured, where coverage applies, and how aggregates behave.

Getting started

You can start with a single ceded book: upload the Policy Schedules, Endorsement Addenda, Additional Insured Endorsements, and Policy Manuscripts, plus the SOV and bordereaux. In hours—not weeks—you’ll receive a page‑cited extraction, a discrepancy report, and a model-ready term table. From there, integrate via API so new submissions flow automatically through Doc Chat into your modeling pipeline.

The fastest path is often a live working session. Bring a submission you know cold and ask Doc Chat your toughest modeling questions. This is how teams move from curiosity to conviction. When you’re ready, our team will configure outputs to your RMS/AIR schema and stand up the pipeline within 1–2 weeks.

Conclusion: the end of endorsement blind spots

Catastrophe modeling in reinsurance demands ruthless clarity on terms. With Doc Chat, catastrophe modelers no longer have to choose between speed and completeness. The system ingests the full submission, surfaces every relevant endorsement, reconciles terms with cedent declarations, and exports model-ready fields with page-cited evidence. That means faster submissions, fewer unmodeled exposures, more confident pricing, and treaty outcomes that track to modeled expectations.

Ready to eliminate endorsement blind spots from your ceded portfolio? Explore Doc Chat for Insurance and see how quickly your team can move from PDFs to defensible model inputs.