Automated Treaty Review: Using AI to Analyze Facultative and Treaty Reinsurance Contracts in Minutes

Reinsurance analysts face a relentless document challenge: dozens or hundreds of Facultative Reinsurance Agreements, Proportional Reinsurance Treaties, Excess of Loss Treaties, slip policies, cover notes, endorsements, addenda, broker emails, and treaty accounts must be read, reconciled, and compared under intense time pressure. The stakes are high—small wording differences around exclusions, hours clauses, reinstatements, or claims control can materially change recoverables, increase leakage, or trigger disputes. That’s exactly where Nomad Data’s Doc Chat changes the game.

Doc Chat is a suite of purpose-built, insurance-grade AI agents that automates end-to-end treaty and facultative document review, clause extraction, and cross-comparison—turning days of manual reading into minutes of reliable analysis. If you’ve been searching for “AI for reviewing reinsurance treaties PDF” or ways to “automate treaty slip comparison in reinsurance,” this article shows how reinsurance analysts can use Doc Chat to instantly extract and verify coverage terms, exclusions, limits, and conditions across the whole reinsurance file—with page-level citations and an audit trail your compliance team will love. Explore Doc Chat for insurance here: Doc Chat by Nomad Data.

The Reinsurance Analyst’s Reality: Dense Wording, Inconsistent Formats, High Stakes

Reinsurance is a document-intensive business, and the complexity keeps rising. A reinsurance analyst is responsible for digesting and comparing stack after stack of PDFs: draft slip policies, binder notes, cover notes, lineslips, facultative certificates, treaty wordings, LMA/LSW market clauses, sanction clauses, ILU clauses, arbitration provisions (including Bermuda Form), service of suit, and dozens of endorsements. For proportional treaties, you need to validate ceding commission, profit commission, loss participation, swing rate mechanics, and portfolio transfer language. For excess of loss, you verify attachment points, occurrence limits, aggregate limits, reinstatements, corridors, hours clauses (72/96/168 hours), ultimate net loss definitions, loss aggregation, and inuring reinsurance. Facultative agreements introduce their own nuance: follow-the-fortunes/follow-the-settlements, back-to-back intent with the cedent’s original policy, claim cooperation vs. claims control, and cut-through endorsements.

What makes this especially challenging is that even the same treaty can be represented in multiple formats over time—initial slip, signed slip, cover note, and the final wording often differ. Endorsements accumulate across years; broker emails include negotiated clarifications; and the official treaty wording might reference or defer to market clauses by number rather than reproducing the full text. Meanwhile, analysts must tie all this back to bordereaux (premium and claims), statements of account (SOA), cash calls, reinstatement premiums, loss advices, notices of loss, and commutation agreements. Missing a single carve-back in a cyber exclusion, or a single event definition change, can materially alter recoveries.

How Manual Treaty Review Typically Works Today

Today’s process is still largely manual. A reinsurance analyst downloads PDFs from email or a DMS, bookmarks sections, and copy-pastes snippets into spreadsheets or internal templates. They compare a 2024 endorsement to a 2023 wording by line-by-line reading. They track clause nuances around:

• Follow-the-fortunes, follow-the-settlements (and any carve-outs that limit deference to the cedent’s claim decisions).
• Claims cooperation vs. claims control (including response timelines, control triggers, and remedy language).
• Sanctions compliance, service of suit, governing law, arbitration venue, severability, and salvages/subrogation.
• Ultimate net loss definitions, inuring reinsurance, franchise/deductible mechanics, retentions, and corridors.
• Event/occurrence and aggregation language, including catastrophe hours clauses and cyber/war/nuclear exclusions.
• Reinstatement provisions (paid/unpaid), pricing formulas, and conditions for additional reinstatements.
• Ceding commission, brokerage, profit commission, loss participation, and swing rate parameters for proportional treaties.

Analysts often juggle multiple document types—facultative certificates, proportional treaty wordings, per risk XL and catastrophe XL contracts, aggregate stop loss, slips, cover notes, LMA/LSW clauses, broker correspondence, and bordereaux. They confirm whether treaty terms are back-to-back with underlying policies or whether there are carve-outs (for instance, cyber risk via LMA cyber series exclusions or war/nuclear). Then they reconcile these terms with the quarterly statement of account, premium and claims bordereaux, claim advices, and actuarial projections. Every cross-check costs hours—and every missed nuance can cost millions.

Where Manual Review Breaks Down

Even the most diligent reinsurance analyst cannot read everything with equal focus when faced with thousands of pages and competing deadlines. Manual approaches struggle with:

• Volume: Massive files with multiple revisions, market clauses, and endorsements.
• Variability: Inconsistent formats across brokers, markets, and years; scanned PDFs and mixed quality.
• Hidden dependencies: A slip referencing an LMA clause number or a broker email that modifies a prior term.
• Cross-year diffs: Subtle language changes between 2022, 2023, and 2024 wordings that materially affect attachment, aggregation, or exclusions.
• Time pressure: Cat seasons, renewals, and commutations compress analysis windows.
• Knowledge drift: Institutional nuance lives in senior analysts’ heads, not in structured playbooks.

This is why insurers and reinsurers are increasingly searching for “facultative agreement clause extraction AI” and “extract exclusions from reinsurance contract.” They need an assistant that never gets tired, never skips a page, and always shows its work.

Doc Chat: AI Agents Built for Reinsurance Treaty and Facultative Review

Doc Chat by Nomad Data ingests entire treaty files—slips, cover notes, signed wordings, endorsements, addenda, and broker correspondence—plus related premium bordereaux, claims bordereaux, SOAs, loss advices, and commutation agreements. It reads every page in minutes, then answers your questions in real time with citations back to the source page. Ask:

“Summarize all exclusions across the 2024 Cat XL program and flag differences vs. 2023.”
“List the hours clause definitions and event aggregation language across all layers.”
“Extract all mentions of follow-the-fortunes/settlements and any carve-outs.”
“Compare ceding commission, profit commission, and swing rate mechanics vs. prior year.”
“Identify sanctions language and whether service of suit supersedes arbitration.”

Because Doc Chat is trained on your playbooks and standards, it tailors extractions to your team’s data dictionary—layer, attachment point, limit, aggregate, reinstatements, ceding commission, brokerage, profit commission formula, corridor, franchise/deductible, ultimate net loss, and more. And it works on complex or low-quality PDFs, making it ideal for “AI for reviewing reinsurance treaties PDF” use cases.

Automations That Replace Days of Manual Treaty Work

Doc Chat delivers practical automations for the reinsurance analyst:

• Automated slip-to-wording comparison: Instantly “automate treaty slip comparison in reinsurance,” detecting mismatches between the slip, the cover note, and the signed wording.
• Clause extraction and normalization: “Facultative agreement clause extraction AI” pulls follow-the-fortunes/settlements, claims cooperation/control, sanctions, service of suit, arbitration, choice of law, and cut-through, normalizing terms to your standard taxonomy.
• Exclusions and carve-backs: “Extract exclusions from reinsurance contract” across documents and years, including cyber (LMA series), war, nuclear, terrorism, communicable disease, or silent cyber carve-backs—flagging language drift year-over-year.
• Reinstatement reconciliation: Identify the number of paid/unpaid reinstatements, pricing formulas, and triggering conditions by layer.
• Aggregation and hours clauses: Compare occurrence definitions, catastrophe hours (72/96/168), and aggregation provisions across layers and treaties.
• Inuring reinsurance and back-to-back checks: Confirm inuring structures and detect where back-to-back intent with primary policies is incomplete.
• Proportional economics: Extract ceding commission, brokerage, profit commission, loss participation, swing rate, and corridor mechanics; generate variance reports vs. prior years.
• Portfolio-level review: Summarize terms across an entire book to support renewals, commutations, or M&A due diligence.

Every answer cites the page and document where the AI found the term. Compliance, audit, legal, and leadership can verify in one click—no scavenger hunt.

Facultative vs. Treaty: How Doc Chat Adapts to Each Structure

Facultative Reinsurance Agreements

Fac placements often arrive as certificates or bespoke agreements with negotiated clauses. Doc Chat extracts and compares claims control vs. cooperation, cut-through endorsements, back-to-back coverage intent, follow-the-fortunes, service of suit, and sanction clauses. It can also reconcile the fac wording to the underlying policy (if provided), identifying gaps such as cyber exclusions that break back-to-back intent. When the cedent sends loss advices, Doc Chat cross-checks claim circumstances to the agreement’s occurrence definitions and exclusions, surfacing potential dispute points up front.

Proportional Reinsurance Treaties (Quota Share, Surplus)

For proportional treaties, Doc Chat reads treaty wordings, slips, cover notes, and endorsements to extract ceding commission, brokerage, profit commission formulas, swing rate bands, and loss participations. It verifies inuring reinsurance, portfolio transfer language, and sunset clauses. When you load premium and claims bordereaux and statements of account, Doc Chat aligns the treaty economics to actuals and highlights anomalies—like a misplaced expense item in the bordereau or a profit commission formula change in an endorsement that didn’t get reflected in the SOA.

Excess of Loss Treaties (Per Risk XL, Cat XL, Aggregate Stop Loss)

For XoL programs, Doc Chat extracts attachment points, per-occurrence limits, annual aggregates, number/pricing of reinstatements, hours clauses, and event/occurrence definitions. It compares language across layers and years to catch subtle changes (e.g., the switch from 72-hour to 168-hour clause, a revised cyber carve-out, or a new communicable disease exclusion). It also reads claims advices and bordereaux to test whether losses meet aggregation definitions and whether reinstatements were charged per the wording.

AI for Reviewing Reinsurance Treaties PDF: A Practical, Auditable Workflow

Most treaty files are PDF-heavy, sometimes with poor OCR. Doc Chat handles them at scale and produces structured outputs that analysts can push into spreadsheets, data warehouses, or reinsurance administration systems. The workflow looks like this:

1. Drag-and-drop entire treaty folders—slips, cover notes, signed wordings, endorsements, LMA clause appendices, broker emails, bordereaux, SOAs, loss advices.
2. Ask natural-language questions: “Extract exclusions from reinsurance contract,” “List reinstatements for each layer,” “Compare 2023 vs. 2024 aggregation wording.”
3. Receive answers in seconds with citations and downloadable, structured data (CSV/JSON) mapped to your standard treaty data dictionary.
4. Export summarized highlights for underwriting or for reinsurance accounting to reconcile to bordereaux and statements of account.
5. Iterate: ask follow-up questions, run redline-style comparisons across years, or generate an executive summary for the treaty underwriter.

Because Doc Chat is trained on your internal playbooks and clause preferences, it preserves your institutional knowledge and standardizes treaty review, even as teams scale or rotate roles. For a deeper discussion of why this goes beyond simple “PDF scraping,” see Nomad’s perspective: Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

How This Was Done Manually—and Why It Doesn’t Scale

Historically, treaty review relied on senior analysts training juniors through shadowing, not documentation. Nuanced logic—“check this clause unless that endorsement changes it; if so, look for the broker email from March 3”—lived in people’s heads. Spreadsheets became ad hoc databases; folder structures became the “system of record.” This approach breaks down when faced with surge volumes (e.g., renewal season, cat events, commutation pipelines), when staff turnover or leave occurs, or when a regulator or reinsurer asks for a defensible audit trail of why a determination was made.

Nomad has seen this pattern across insurance lines. We’ve documented it in multiple articles, such as our overview of real-world AI adoption in insurance: AI for Insurance: Real-World AI Use Cases Driving Transformation, and our exploration of why automating “simple” data entry produces outsized ROI: AI's Untapped Goldmine: Automating Data Entry.

What Doc Chat Automates for the Reinsurance Analyst

Doc Chat automates the parts of treaty work that drain hours but don’t require uniquely human judgment:

• Document ingestion at scale: Entire claim and treaty files, including scanned PDFs.
• Classification and indexing: Slips, cover notes, wordings, endorsements, LMA/LSW clauses, and broker emails are recognized and organized.
• Clause extraction: Follow-the-fortunes/settlements, back-to-back intent, inuring reinsurance, U.N.L., sanctions, service of suit, arbitration, claims cooperation/control.
• Exclusion mapping: Cyber (LMA series), war, nuclear, communicable disease, terrorism; carve-backs; silent cyber language.
• Economics: Ceding commission, brokerage, profit commission, loss participation, swing rate, corridors, reinstatements (count and pricing), attachment points, limits, aggregates.
• Redline-style comparison: Year-over-year and layer-by-layer language drift detection.
• Data delivery: Structured outputs mapped to your treaty data dictionary for analytics and system updates.

In practice, this means “querying” your entire treaty file as if it were a database. You get instant responses and exact citations—no more guessing, no more hunting, no more manual re-typing.

Business Impact for Reinsurance Analysts and Teams

Reinsurance teams adopt Doc Chat for four reasons: speed, accuracy, consistency, and scale.

Speed: Reviews that took days collapse into minutes. Nomad customers regularly see 10x–100x acceleration on document-heavy processes. In complex claims environments, our customers have reported summarizing thousand-page files in under a minute—see Great American Insurance Group’s experience: Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI. Treaty files respond similarly: once all documents are ingested, questions resolve in seconds.

Accuracy: Fatigue doesn’t degrade Doc Chat’s attention. It reads page 1,500 as precisely as page 1 and cites every answer back to the source page. This reduces leakage, litigation risk, and friction with trading partners.

Consistency: Doc Chat encodes your team’s best-practice playbook. Every analyst follows the same process, producing defensible, standardized outputs that stand up to internal audits, reinsurer queries, and regulator reviews.

Scale: Surge volumes during renewals, cat seasons, or M&A due diligence no longer require overtime or temp hires. Doc Chat scales instantly, so your reinsurance analysts can focus on negotiation and strategy rather than document trawling.

Why Nomad Data Is the Best Fit for Reinsurance Treaty Review

Nomad isn’t a one-size-fits-all tool. We deliver a white-glove implementation tailored to your reinsurance workflows and documents:

• The Nomad Process: We train Doc Chat on your clause taxonomy, your data dictionary (layers, attachments, reinstatements, economics), and your review playbooks to produce outputs “in your language.”
• Implementation in 1–2 weeks: Drag-and-drop use begins day one. Full integration with your DMS and reinsurance systems typically completes within two weeks.
• Enterprise-grade trust: SOC 2 Type 2 controls, document-level traceability, page citations, and an audit trail by default.
• Real-time Q&A across massive files: Ask follow-ups, iterate, and explore scenarios—Doc Chat answers instantly and points to the source.
• Thorough and complete: Surfaces every reference to coverage, liability, damages, and economics to eliminate blind spots and leakage.

For a broader look at why eliminating document bottlenecks changes operating math, see The End of Medical File Review Bottlenecks and Reimagining Claims Processing Through AI Transformation. The lessons apply directly to treaty review: automate the rote, elevate the expert.

Deep Dive: Typical Questions Reinsurance Analysts Ask Doc Chat

Doc Chat is most powerful when analysts ask it like a colleague. Common prompts include:

• “Compare 2023 and 2024 wording for the Per Risk XL program; summarize changes to aggregation, hours clause, and excluded perils.”
• “List all reinstatements per layer, pricing formulas, and whether they’re paid or unpaid; show citations.”
• “Extract all mentions of follow-the-settlements/fortunes and any carve-outs; confirm if they conflict with claims control.”
• “Summarize sanctions, service of suit, and arbitration clauses; identify if Bermuda Form applies to any layers.”
• “Pull ceding commission, brokerage, profit commission, swing rate, and loss participation from the quota share treaty; highlight differences from the cover note.”
• “Map cyber, war, nuclear, terrorism, and communicable disease exclusions across all treaties and endorsements; flag any carve-backs.”
• “From the claims bordereau for Q2, identify which losses qualify for aggregation under the hours clause and whether reinstatements were triggered.”
• “Spot any inconsistencies between the slip, cover note, and signed wording on attachment points and limits; produce a variance report.”

Automate Treaty Slip Comparison in Reinsurance: From Slip to Signed Wording

The jump from slip to cover note to signed wording is where many mismatches creep in. Brokers may add clarifying emails; markets may add LMA clauses by reference; endorsements may partially override prior text. Doc Chat compares each step and flags conflicts, missing references, or changed definitions. It also detects when a referenced clause is absent from the final attachment package and prompts you to request it from the broker.

Extract Exclusions from Reinsurance Contract: Precision at Scale

Exclusions have multiplied and evolved—especially around cyber, war, nuclear, terrorism, and communicable disease. Doc Chat reads across all files and years to inventory every exclusion variant and carve-back. Because it normalizes the output to your taxonomy, you can see which layers or years deviate, and whether those deviations introduce risk to recoveries or create back-to-back gaps with underlying policies. If you’ve been searching “extract exclusions from reinsurance contract,” this is the turnkey solution.

Facultative Agreement Clause Extraction AI: Bespoke Wording, Standardized Insight

Facultative wordings are famously bespoke. Doc Chat’s “facultative agreement clause extraction AI” looks past formatting variation to find the clauses you care about—claims control vs. cooperation, cut-through, service of suit, sanctions, arbitration, U.N.L., subrogation, and back-to-back statements. It can align the fac certificate with the underlying policy to identify any breaks in intended coverage mirroring.

Security, Compliance, and Audit Readiness

Reinsurance documentation includes sensitive contract terms and client data. Nomad Data maintains stringent security standards (including SOC 2 Type 2). Doc Chat keeps your content within your tenant and returns page-cited answers with full traceability. Compliance teams and auditors can validate every data point quickly. This page-level explainability is one reason regulated carriers adopt Doc Chat. For real-world insight into why explainability matters, see the GAIG case study: Great American Insurance Group Accelerates Complex Claims with AI.

Integration Without Disruption

Doc Chat works out of the box: drag and drop documents and start asking questions. As you scale, Nomad integrates with your document management system, SFTP, S3, email capture, and reinsurance administration platforms to automate ingestion and export. Typical implementations complete in 1–2 weeks because all heavy lifting is handled by Nomad’s team. You are not buying a toolkit—you’re getting a tuned solution matched to your reinsurance processes.

Implementation Playbook: 1–2 Weeks to Value

Nomad’s white-glove onboarding ensures fast time to value and high adoption:

• Week 1: Discovery and training. We align on your clause taxonomy and outputs (e.g., treaty data dictionary fields). We ingest a representative treaty pack (slip, cover note, wording, endorsements, LMA appendices, broker emails, bordereaux, SOAs).
• Week 2: Validation and rollout. Analysts test with known treaties and ask standard prompts. We fine-tune extractions, finalize export formats, and connect to your systems. Teams begin using Doc Chat in live work.

Because your playbooks are encoded in Doc Chat, new analysts are productive immediately, and senior analysts spend their time on negotiation and strategy rather than manual compares.

Quantifying the ROI: Fewer Blind Spots, Faster Decisions, Better Recoveries

Clients typically see:

• Time savings: 70–95% reduction in treaty review time; renewals executed faster; commutations assessed sooner.
• Cost reduction: Lower reliance on overtime or external reviewers during surge periods; fewer disputes that require legal spend.
• Accuracy improvements: Page-cited answers reduce misinterpretation; consistent extraction minimizes missed clauses and leakage.
• Scalability: Handle more treaties and facultative placements without increasing headcount.

Beyond immediate productivity gains, Doc Chat unlocks analyses that were previously uneconomic—portfolio-wide clause inventories, year-over-year drift mapping, and systematic alignment of bordereaux/SOAs with treaty economics. As we discuss in AI’s Untapped Goldmine: Automating Data Entry, the biggest wins often start with automating “simple” extractions at massive scale.

From Data to Strategy: Turning Treaty Words into Decisions

Reinsurance analysts add the most value when they move from reading to reasoning. Doc Chat accelerates the reading so teams can focus on what terms imply for pricing, recoveries, commutations, and negotiations. With instant access to exclusions, reinstatements, aggregation definitions, and economics, analysts can simulate “what if” scenarios—how a different hours clause would have aggregated losses, or how a revised cyber carve-back alters net exposure.

Change Management: Building Trust in AI for Treaty Review

Trust grows when teams see their own treaty packs answered accurately in seconds. We encourage clients to test Doc Chat on known cases to validate answers and citations. That’s how GAIG built internal trust, as described here: GAIG Accelerates Complex Claims with AI. We also emphasize keeping humans in the loop. As outlined in Reimagining Claims Processing Through AI Transformation, AI serves as a capable junior that cites its work; analysts confirm and decide.

Frequently Asked Questions from Reinsurance Analysts

Can Doc Chat handle scanned PDFs and mixed-quality treaty files?
Yes. It ingests large, variable-quality PDFs and still returns precise, page-cited answers.

Does it support our clause taxonomy and internal data dictionary?
Yes. That’s core to Nomad’s approach—we train on your playbooks so extractions land in your standard fields.

Is there an audit trail?
Every answer includes document and page citations and can be exported with a timestamped trail suitable for internal and external audits.

How quickly can we go live?
Most teams start same-day with drag-and-drop. Full integration typically takes 1–2 weeks.

Where should we start?
Many reinsurance teams begin with one program (e.g., Cat XL or Per Risk XL) or one treaty year, then scale across the portfolio once value is proven.

If You’ve Been Searching for AI for Reviewing Reinsurance Treaties PDF—This Is It

Whether your priority is “automate treaty slip comparison in reinsurance,” “extract exclusions from reinsurance contract,” or “facultative agreement clause extraction AI,” Doc Chat gives reinsurance analysts a fast, accurate, and defensible way to work. It reads everything, cites everything, and scales instantly. The result: fewer blind spots, faster renewals and commutations, better recoveries, and more time spent on strategy rather than manual reading.

The next step is simple: load a recent treaty pack and ask Doc Chat your hardest questions. See what happens when AI is trained to think like your best reinsurance analyst—and never gets tired. Learn more or request a walkthrough here: Nomad Data’s Doc Chat for Insurance.