Verify Claims Attachments Instantly: AI Matching of Claims Files with Reinsurance Treaty Layers (Reinsurance, General Liability & Construction) — For Claims Handlers

Verify Claims Attachments Instantly: AI Matching of Claims Files with Reinsurance Treaty Layers

Claims handlers working across Reinsurance and General Liability & Construction face a recurring bottleneck: verifying that a reported loss truly attaches to the correct reinsurance treaty layer. The proof often spans hundreds or thousands of pages: primary and umbrella policies, layer schedules, excess of loss treaties, attachment point documentation, bordereaux, broker slips, coverage letters, counsel invoices, and more. Manually cross-walking amounts, dates of loss, occurrence definitions, and exhaustion documents drains time, delays settlements, and risks errors. This is exactly the challenge Nomad Data’s Doc Chat was built to solve.

Doc Chat for Insurance automates attachment verification by reading entire claims files end-to-end and instantly cross-mapping underlying exhaustion to treaty triggers. Ask plain-language questions—“Does this claim breach the $5M underlying and attach to the 5x5 layer?”—and get precise answers with page-level citations. If your day-to-day work includes reconciling Claims Files, Layer Schedules, Excess of Loss Treaties, and Attachment Point Documentation, Doc Chat reduces triage from days to minutes.

The Nuance: Reinsurance and GL/Construction Claim Attachment Is More Than a Number

For a Claims Handler supporting Reinsurance on General Liability & Construction risks, attachment validation is a nuanced exercise in interpretation and reconciliation—not just arithmetic. A loss rarely arrives as a perfectly packaged figure labeled “attaches to 5x5.” Instead, you receive a dynamic, evolving file: primary and umbrella policies and endorsements, multiple insureds and additional insureds, wrap-up/OCIP participation, indemnity tenders, defense invoices, reserve worksheets, and payments posted over months or years. You must align these with complex treaty constructs (ultimate net loss definitions, follow-the-fortunes/follow-form, occurrence definitions, prior acts, batch/aggregation rules, defense inside/outside limits, deductibles and SIRs, co-participations, corridors, and reinstatements).

In construction especially, layers interact with coverage nuances like additional insured endorsements (e.g., CG 20 10/CG 20 37), “primary and noncontributory” wording, subcontract indemnity clauses, wrap-up exclusions, products-completed operations, and jobsite or completed operations dates. Jurisdiction matters too—rules about horizontal vs. vertical exhaustion and stacking can fundamentally change when a claim pierces a layer. The claim file must be reconciled against the treaty wording and layer schedules to answer deceptively simple questions:

Which occurrence(s) apply and what is the period of loss?
What exactly is Ultimate Net Loss here—indemnity only or defense too? And do defense costs erode underlying limits?
Have the primary and umbrella layers been properly exhausted and documented?
Are there sublimits or exclusions that change the math (e.g., products-completed ops sublimits, contractual liability limitations, exterior cladding exclusions)?
Do additional insured tenders change which policy erodes first?
Are reinstatements required for the treaty layer to respond, and if so, has the cost been calculated correctly?

Attachment validation is therefore a document inference problem as much as a data extraction problem. The information you need is scattered across claim notes, policy endorsements, loss runs, bordereaux, proof of loss forms, coverage counsel memos, reserve change logs, and carrier/broker correspondence—each with its own structure and terminology. You must extract, infer, and reconcile before you can conclude, with confidence, “This reinsurance layer attaches.”

How It’s Handled Manually Today

Most teams tackle this with a labor-intensive, multi-tab spreadsheet and a checklist-based review of documents. The manual workflow typically looks like this:

Intake and sorting: Organize PDFs (Claims Files, Layer Schedules, Excess of Loss Treaties, Attachment Point Documentation), plus endorsements, FNOL forms, loss run reports, ISO claim reports, broker slips/cover notes, reinsurance wordings, and Statements of Account (SoA). Split, bookmark, and tag documents by type.
Policy and layer reading: Read primary, umbrella, and excess policies; extract attachment points, limits, sublimits, defense treatment, batch/aggregation clauses, prior/ongoing operations language, and additional insured endorsements. Reconcile with layer schedules and treaty term sheets.
Occurrence analysis: Determine occurrence vs. claims-made triggers, define the occurrence(s), and identify whether horizontal or vertical exhaustion applies in the relevant jurisdiction. Confirm dates of loss and completed operations windows.
Underlying erosion proof: Compile primary/umbrella erosion via payment registers, indemnity breakdowns, defense invoices, reserve and valuation memos, and loss bordereaux. Cross-check for sublimits and exclusions that change effective erosion.
Ultimate Net Loss calculation: Confirm whether defense is inside or outside limits at each layer. Reconcile any self-insured retentions or deductibles, co-participations or corridors, and reinstatement mechanics.
Reinsurance attachment test: Apply treaty terms to the reconciled loss. Identify whether the loss attaches to, for example, the 5x5 layer, then compute share, reinstatement charges, and any hours clauses or event aggregation criteria.
Documentation pack: Build a defensible narrative and a “proof pack” of citations and excerpts supporting attachment for internal review, reinsurer queries, auditors, or arbitration.

This approach has three recurring weaknesses: it’s slow, it’s error-prone under volume/pressure, and it’s hard to standardize across people. When piles of attachments, endorsements, and correspondence exceed a few hundred pages, fatigue and inconsistency creep in. Seasonal surges or catastrophe events compound backlogs. And every carrier and cedant uses a slightly different documentation style, which breaks brittle, template-based extraction methods.

Doc Chat: Purpose-Built AI to Automate Excess Layer Verification

Doc Chat by Nomad Data eliminates the bottleneck by ingesting entire claim files—often thousands of pages—and automatically extracting, cross-checking, and summarizing the facts that determine whether a claim attaches to a reinsurance treaty layer. It is not a “generic summarizer.” It is a set of purpose‑built, AI‑powered agents trained on your playbooks, your treaty wordings, and your coverage standards. You get answers in minutes with citations back to the exact page, including those inside dense, inconsistent policies and endorsements that traditional tools miss.

Here’s how Doc Chat handles the core work of automate excess claim layer verification for Claims Handlers in Reinsurance and General Liability & Construction:

End-to-end ingestion at scale: Doc Chat reads entire Claims Files, Layer Schedules, Excess of Loss Treaties, Attachment Point Documentation, broker cover notes, policy forms, endorsements, bordereaux, counsel reports, and accounting statements without pagination limits.
Attachment point extraction: Using advanced language understanding, agents extract and crosscheck attachment points, limits, sublimits, deductibles/SIRs, and defense-inside/outside-limits across policies and endorsements—no templates required.
Underlying erosion reconciliation: The system compiles indemnity and expense erosion from payment registers, invoices, and loss runs; flags mismatches (e.g., expense not eroding where expected); and aligns totals to the correct layer programs.
Occurrence and aggregation logic: Doc Chat applies your definitions of occurrence, your batch rules, and jurisdictional exhaustion conventions (horizontal vs vertical), then tests whether the loss is a single event or multiple occurrences across policy years.
Treaty matching: It then cross-maps the reconciled loss to treaty triggers (e.g., 5x5, 10x10, multi-layer towers), computes potential attachment, participation, and reinstatements, and indicates whether and where the claim should respond.
Real-time Q&A: Ask, “Can we match claim amount to excess treaty automatically?” or “Which invoices moved the loss over the $5M attachment?” Doc Chat returns answers with linked citations so you can verify instantly.
Defensible proof packs: Auto-generate an audit-ready attachment memorandum that includes the attachment decision, basis of decision, calculation detail, and a pin-cited appendix linking back to the source pages.

This is the leap from “reading documents” to replicating expert inference—the very challenge outlined in Nomad’s perspective on advanced document automation, Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

AI for Validating Claim Attaches to Reinsurance Layers: How It Works in Practice

Doc Chat operationalizes the full attachment workflow as a repeatable, teachable process. From intake to attachment decision, the system follows steps your team already recognizes—only faster and more consistently:

Load the file: Drag and drop the claim package. Include Claims Files, Layer Schedules, Excess of Loss Treaties, Attachment Point Documentation, broker slips, endorsements, defense invoices, reserve memos, bordereaux, SoAs, cash calls, and coverage opinions.
Auto-classify and index: The agent classifies each document (policy, endorsement, invoice, loss run, counsel report), auto-tags it, and builds an index with section headings and bookmarks.
Policy intelligence: It extracts attachment points, limits, sublimits, AI endorsements (e.g., additional insured), defense treatment, and any special wording that affects Ultimate Net Loss—flagging potential conflicts or exclusions.
Underlying erosion engine: The agent compiles a running total of indemnity and expense by policy and time period, ties each amount to a page-level citation, and distinguishes expense eroding vs non-eroding, SIR application, and deductibles.
Occurrence determination: It applies your occurrence and aggregation rules, testing the file for multiple events or policy-year splits, and then normalizes the totals to the tower matrix in your layer schedule.
Layer test: The system compares normalized loss to treaty layers, computes where attachment should occur, and calls out any missing documents or inconsistencies that would change the result.
Answer and explain: It delivers a concise decision—attach/no attach, which layer, how much, reinstatements—and an explanation with linked citations so you can verify every assertion.

The result is speed with defensibility: page-level explainability that claims handlers, auditors, reinsurers, and regulators can trust. As Great American Insurance Group noted, this mode of work—pose a claim question, get a source-cited answer—transforms file review from days to minutes; see the case study recap, Reimagining Insurance Claims Management.

What Doc Chat Automates That Manual Processes Miss

Complexity is the enemy of consistency. Manual reviews buckle under volume and newer claim handlers struggle to apply unwritten rules. Doc Chat handles the complexity and institutionalizes your best practices:

Hidden endorsements: Finds defense-inside/outside-limits language buried deep in umbrella forms or late endorsements that swing erosion math—something humans commonly overlook when fatigued.
Sublimit traps: Surfaces sublimits (e.g., products-completed ops) or exclusions (“your work,” EIFS, exterior cladding) that alter exhaustion and attachment.
Additional insured cascades: Triangulates AI endorsements and tender correspondence to validate which policy erodes first and whether an additional insured tender shifts exhaustion sequence.
Jurisdiction-sensitive logic: Applies your configured approach to horizontal vs vertical exhaustion and batch rules to ensure attachment decisions mirror the law and your playbook.
Reinstatement math: Calculates reinstatement requirements and costs for the layer responding and ensures charges are correctly reflected in the accounting.
Aggregation challenges: Tests whether multiple claimants/events should be treated as one occurrence under treaty wording, then shows the impact on attachment.

Case Example: Construction Bodily Injury Claim with Multiple Claimants

Consider a General Liability claim from a multi-year construction project with three bodily injury claimants arising from a crane incident. The cedant submits a reinsurance notification asserting the loss attaches to the 5x5 layer (excess of $5M up to $10M). The file includes primary and umbrella policies for two policy years, wrap-up documentation (OCIP), subcontractor agreements, AI endorsements (CG 20 10 and CG 20 37), certificates of insurance, incident reports, OSHA logs, defense invoices, indemnity payment logs, mediator’s statement, and coverage counsel analysis, plus the treaty wordings and layer schedules.

Manually, a claims handler must parse: whether the crane incident is one occurrence or multiple; which entities are insured under which policies; whether defense costs erode the primary and umbrella; whether additional insured tenders alter the exhaustion order; whether completed-operations dates trigger a different policy year; whether the umbrella has a products-completed ops sublimit; and whether jurisdiction requires horizontal exhaustion. Then they must reconcile all indemnity and defense payments to validate that $5M is actually, provably exhausted before the 5x5 responds.

With Doc Chat, this becomes a question-driven flow:

“Summarize applicable policies, endorsements, and attachment points across the two policy years with citations.”
“List all defense vs indemnity payments by policy and date; indicate whether they erode the limits; roll up to a running total and show when $5M is crossed.”
“Do AI endorsements and tenders change exhaustion order? Cite the endorsement and tender correspondence.”
“Does the incident qualify as one occurrence? Apply configured jurisdictional rules and note any batch/aggregation language that applies.”
“Confirm whether the loss attaches to 5x5, compute the reinsurer’s participation and any reinstatement charges, and produce a proof pack with page citations.”

In minutes, the automate excess claim layer verification workflow is complete—with a defensible memo that would usually take a senior handler a day or more to assemble.

High-Intent Workflows Embedded in the Product

Doc Chat was designed to meet the exact search intents many insurance professionals express when evaluating solutions. Within the interface, claims handlers can:

Run “AI for validating claim attaches to reinsurance layers” as a preset agent that normalizes occurrences, tests exhaustion, and checks treaty triggers.
Use a “match claim amount to excess treaty automatically” preset to compute whether and where a loss attaches given payments to date and policy-year splits.
Trigger “extract and crosscheck attachment points AI” to pull and reconcile attachment language across all relevant policies and endorsements.

Each preset produces answers with page-level citations and a downloadable attachment proof pack, ensuring your team can move rapidly while remaining audit-ready.

Business Impact: Time, Cost, Accuracy, and Scalability

Nomad Data clients consistently report dramatic improvements when they replace manual attachment verification with Doc Chat. As highlighted in our claims transformation article, Reimagining Claims Processing Through AI Transformation, summarization that took 5–10 hours now completes in under a minute on typical files, and even 10,000–15,000 page records can be processed in minutes. For attachment checks, those gains compound because Doc Chat not only reads the documents but also computes the attachment math and explains it.

Expected outcomes for Claims Handlers in Reinsurance and GL & Construction include:

Cycle time reduction: Cut attachment verification from days to minutes. Eliminate backlogs during surge volumes.
Lower loss-adjustment expense: Reduce hours spent reconciling documents and building proof packs. Reallocate senior handlers to negotiation and strategy.
Higher accuracy and fewer disputes: Consistent extraction of attachment points, limits, sublimits, and defense treatment. Fewer missed endorsements. Better, earlier answers for cedants and brokers.
Scalable surge response: Process entire towers and multiple claimants across years without adding headcount; handle catastrophe or construction-portfolio spikes effortlessly.
Morale and retention: Replace drudge work with investigative, judgment-heavy tasks that keep expert staff engaged.

These efficiency and quality gains translate into real financial impact: faster settlements, less leakage, more defensible decisions, better reinsurance accounting, and improved reinsurer relations.

Why Nomad Data’s Doc Chat Is Different

Most tools stop at basic OCR or template-based extraction. Doc Chat goes further because it was built for the messy, high-stakes reality of insurance documents:

Volume without compromise: Ingest entire claim files—thousands of pages—and answer complex questions in minutes, not days. See our perspective on ending backlogs in The End of Medical File Review Bottlenecks.
Complexity mastery: Dig out exclusions, endorsements, and trigger language hiding in dense, inconsistent policies. This is where attachment decisions are often won or lost.
The Nomad Process: We train Doc Chat on your playbooks, coverage standards, treaty nuances, and exhaustion rules. The result is a personalized agent that mirrors your best people.
Real-time Q&A: Ask for a list of all attachment points, a roll-up of erosion by layer, or a summary of occurrences—get answers instantly with citations.
Thorough and complete: Surface every reference to coverage, liability, damages, and limits. Eliminate blind spots and leakage so nothing important slips through.
Your partner in AI: Nomad delivers a white-glove service model, co-creating solutions that evolve with your needs and deliver lasting impact.

If you’ve ever tried “generic AI” and felt underwhelmed, you’re not alone. As we explain in AI’s Untapped Goldmine: Automating Data Entry, the biggest wins come from tailoring AI to your real workflows—and packaging results in the structures your teams already use.

Security, Explainability, and Audit Readiness

Reinsurance and GL & Construction claims contain sensitive information. Doc Chat is engineered for enterprise-grade security and rigorous traceability:

Security-first: Built for carriers and reinsurers with strict compliance requirements, including SOC 2 Type 2–aligned controls.
Page-level explainability: Every answer includes citations to the exact page and section—a critical feature for internal review, broker/cedant discussions, reinsurer audits, and regulators.
Defensible decisions: The system doesn’t just summarize. It creates a documented rationale and supporting evidence, ready for peer review and external scrutiny.

Implementation: White-Glove Onboarding in 1–2 Weeks

Doc Chat is ready on day one for drag-and-drop document review. To go deeper, our white-glove team configures the agent to your playbooks, exhaustion logic, and treaty variations. This is typically a 1–2 week implementation—not a months-long IT project. As adoption grows, we integrate with your claims and reinsurance systems via modern APIs to push structured outputs (attachment memos, bordereau-ready data, SoA support) into your workflows.

The result is immediate productivity plus rapid time-to-value—what Great American Insurance Group called “such a huge time saver” in their internal evaluation of the platform.

What Documents Doc Chat Handles for Attachment Verification

Doc Chat reads and reconciles the document types that drive attachment decisions in Reinsurance and GL & Construction:

Policy and treaty: Primary, umbrella, excess policies; endorsements; layer schedules; excess of loss treaties; cover notes/broker slips; treaty endorsements; reinstatement notices.
Claim and loss: Claims files; FNOL forms; ISO claim reports; loss run reports; bordereaux; proof of loss; adjuster notes; reserve worksheets; insurer coverage letters.
Construction specifics: OCIP/CCIP wrap documentation; subcontract agreements; additional insured endorsements (CG 20 10, CG 20 37); certificates of insurance; incident reports; OSHA logs; change orders.
Financial and legal: Payment registers; defense invoices; expert reports; mediation briefs; arbitration decisions; reinsurance statements of account (SoA); cash call documentation.

By normalizing terms and cross-referencing amounts, Doc Chat continuously validates which figures count toward exhaustion, which do not, and how that translates to treaty layer attachment.

Answers to the Most Searched Questions

“AI for validating claim attaches to reinsurance layers”—what does Doc Chat actually do?

It ingests the full claims file, pulls the relevant attachment points and limits, reconciles indemnity and expense erosion, tests occurrence/aggregation and exhaustion rules, and produces an attach/no-attach decision with layer, amount, and citations—plus reinstatement calculations where applicable.

Can we “match claim amount to excess treaty automatically” without reformatting documents?

Yes. Doc Chat does not require templates. It reads native PDFs, scans, and mixed document sets, then maps normalized totals to your layer schedule and treaty matrix. You get an automatic match, explanation, and a downloadable proof pack.

How does it “extract and crosscheck attachment points AI” across inconsistent policy forms?

Doc Chat uses advanced language understanding to find attachment points, limits, and defense treatment wherever they appear—in base forms, endorsements, and correspondence—and then crosschecks the results across all documents to resolve conflicts.

What about jurisdictional rules (horizontal vs vertical exhaustion, stacking)?

We configure Doc Chat to reflect your jurisdictional approach and playbooks. The agent follows your instruction set, so decisions remain consistent with your standards and legal guidance.

From Bottleneck to Advantage: The Bigger Picture

Attachment verification sits at the crossroads of claims, coverage, and accounting. When it runs slowly, everything slows—reserving, recoveries, SoAs, and settlement strategy. When it is fast, accurate, and defensible, you move decisively. You can front-foot discussions with cedants and brokers, answer reinsurer questions with evidence, and keep financials clean and current.

That’s why we built Doc Chat as a partner for claims organizations—not just a feature. It captures your best practices and turns them into a scalable capability that makes every Claims Handler more effective on day one. In our experience with large carriers, this approach is how teams unlock the biggest rewards from AI—by focusing on their most repetitive, needle-moving processes and institutionalizing expert judgment. See our broader industry view in AI for Insurance: Real-World AI Use Cases Driving Transformation.

Getting Started: A Simple Path to Automated Attachment Verification

You can be up and running fast:

Pilot with real files: Bring a few recent attachment disputes or complicated GL construction claims. Upload the full package.
Validate accuracy: Ask Doc Chat the same questions your senior handlers resolved. Compare decisions and citations.
Tailor the agent: We codify your exhaustion rules, attachment playbooks, and jurisdictional preferences.
Roll out: Start with attachment verification; expand to demand review, legal analysis, fraud detection, and portfolio audits as needed.

You’ll see the same pattern Great American Insurance Group experienced: immediate time savings, higher confidence, and growing enthusiasm as teams shift from manual hunting to judgment and negotiation.

Conclusion: Turn Attachment Checks from a Chore into a Competitive Edge

In Reinsurance and General Liability & Construction, proving that a claim truly attaches to a treaty layer is essential—and historically painful. With Doc Chat, your Claims Handlers can validate excess claim layer attachment in minutes, not days, with source-cited confidence. You get consistent, defensible outcomes that accelerate settlements, strengthen reinsurer relations, and reduce leakage. And because Doc Chat is implemented via white-glove onboarding in 1–2 weeks, the payoff begins almost immediately.

If you’re searching for ways to automate excess claim layer verification, match claim amount to excess treaty automatically, or extract and crosscheck attachment points AI—Doc Chat was designed for you. The only question is how quickly you want to turn your attachment bottleneck into a strategic advantage.