Instant Extraction of Limits, Sublimits, and Deductibles from Complex Policy Schedules – Built for the Chief Risk Officer

Chief Risk Officers live at the intersection of solvency, capital, and portfolio performance. Yet the single most fundamental input for risk quantification – the precise limits, sublimits, and deductibles that govern loss – is often buried in policy schedules, declarations pages, and endorsements that vary wildly by carrier, broker, and line of business. The result: delays, blind spots, and uneasy reliance on spreadsheets that can’t keep pace with the business.

Nomad Data’s Doc Chat solves this challenge with purpose-built, AI-powered agents that instantly read the entirety of your policy files, normalize naming conventions, and surface every relevant limit, sublimit, deductible, waiting period, and trigger – across Property & Homeowners, Specialty Lines & Marine, and General Liability & Construction. Whether you are trying to extract limits from policy schedules AI-style at portfolio scale, find deductible in insurance policy automatically for a single complex account, or deploy AI to aggregate sublimits in commercial insurance across dozens of programs, Doc Chat reduces days of manual review to minutes while delivering page-cited, audit-ready answers.

The CRO’s Challenge: Limits and Deductibles Hidden in Plain Sight

In today’s market, policy artifacts are sprawling and inconsistent. A single renewal pack may include a declarations page, multiple schedules of locations and values, manuscript endorsements, catastrophe perils carve-outs, sublimits for time element and special coverages, and deductible matrices that differ by state, project, or peril. Worse, the final answer rarely appears on a single page: a limit on the dec page can be fully redefined by an endorsement three attachments later, while a deductible percentage in the schedule might be subject to a minimum figure or a franchise condition hidden in a footnote.

For Chief Risk Officers who must quantify net exposures, assign capital, and attest to solvency, this fragmentation creates risk on several fronts:

• Capital modeling and RBC/BCAR/Solvency II assumptions drift from reality because sublimits and deductibles are overlooked or misapplied.
• Reinsurance towers are improperly constructed when attachment points, waiting periods, or exclusions are misread.
• Accumulation management underestimates retained risk when per-location limits and per-occurrence deductibles are not normalized across programs.
• Board and regulator communications depend on manual rollups that cannot be refreshed with the cadence modern risk governance demands.

Doc Chat eliminates these bottlenecks by ingesting entire claim or policy files, mapping how declarations pages, policy schedules, and endorsements interact, and returning complete, cross-checked answers in seconds – with links back to the exact page for validation.

Line-of-Business Nuances the CRO Must Control

Property & Homeowners

Property schedules and endorsements are notoriously diverse. A blanket limit on the dec page may be narrowed by an endorsement; peril-specific deductibles vary by named storm, wind/hail, and earthquake; and time element coverage often has sublimits and waiting periods that materially change modeled outcomes. Doc Chat reads every schedule row, special conditions, and footnote to capture:

• Per-location and blanket limits; building, contents, BI/extra expense, and time element limits; ordinance or law coverage parts; debris removal and pollution sublimits.
• Peril-specific deductibles (e.g., 5% Named Storm subject to $250,000 minimum), waiting periods for ingress/egress and civil authority, and aggregate deductibles for catastrophe events.
• Coinsurance and margin clauses, agreed value endorsements, valuation conditions (RCV vs ACV), and coverage triggers that influence loss adjustment.
• Dependencies and supply chain sublimits, contingent business interruption, service interruption, and off-premises power endorsements.

Output is normalized across carriers so the CRO can make apples-to-apples comparisons of net limits, sublimits, and deductibles by peril, geography, occupancy, and construction class.

Specialty Lines & Marine

Marine and specialty policies introduce voyage, conveyance, and warehouse-to-warehouse constructs, as well as deductible franchises and average clauses that can materially change retained loss. Schedules can span cargo classes, conveyances, storage locations, and project phases. Doc Chat reliably captures:

• Voyage and storage sublimits; conveyance-specific limits; project cargo and delay-in-startup sublimits; testing and commissioning endorsements.
• Franchise deductibles and special conditions (e.g., loss paid in full if threshold is exceeded), salvage and sue-and-labor clauses, and pairs/sets language.
• Named locations and yards, inland transit carve-outs, territorial limits, and deviation/held covered provisions that impact attachment.

By consolidating all endorsements and schedules into a single structured view, CROs can quantify retained risk across logistics networks, large industrial projects, and global storage exposures, and align reinsurance accordingly.

General Liability & Construction

GL and construction programs hinge on the interplay of per occurrence limits, general aggregates, products-completed operations aggregates, and per-project/per-location aggregate endorsements. Deductibles, self-insured retentions (SIRs), defense inside vs. outside limits, and manuscript additional insured endorsements can change net exposure dramatically. Doc Chat detects and structures:

• Per-occurrence and aggregate limits, per-project/per-location aggregate endorsements (e.g., CG 25 03), and other aggregate erosion rules.
• SIRs and deductibles, threshold and reimbursement conditions, and defense cost treatment (inside/outside limits).
• Primary and non-contributory language, waiver of subrogation endorsements, and contractual risk transfer requirements affecting recovery potential.
• Wrap-ups (OCIP/CCIP) with project-specific schedules and completed ops tail obligations.

For construction and premises-oriented risks, Doc Chat also normalizes additional insured endorsements across manuscripts so your capital and reinsurance assumptions reflect true net exposure, not the optimistic view implied by the dec page alone.

How the Manual Process Works Today (and Why It Breaks)

Even the best-run CRO offices rely on document-intensive workflows:

1. Risk analysts manually read policy schedules, declarations pages, and endorsements for each program and copy limits, sublimits, deductibles, and waiting periods into spreadsheets.
2. They reconcile conflicts (e.g., a dec page limit modified by a later endorsement) and attempt to standardize naming conventions across carriers.
3. They re-key data into capital models, cat modeling inputs, reinsurance placement exhibits, and ORSA/BCAR/Solvency II reporting packs.

This approach is slow, expensive, and error-prone. It breaks down under growth, M&A, and renewal season pressure. When internal stakeholders ask for a refreshed view after a mid-season endorsement or a treaty restructure, the team often needs days or weeks to rebuild the rollup. Critical details – a civil authority waiting period, a named storm minimum deductible, a per-project aggregate – frequently slip through the cracks, undermining solvency calculations and treaty performance.

As we argue in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, this problem isn’t just about optical character recognition. It is about inference across scattered, inconsistent sources – precisely where generic tools fall short and specialized insurance AI must excel.

How Doc Chat Automates Limits, Sublimits, and Deductibles Extraction

Doc Chat is a suite of insurance-trained agents that ingest entire books of policies and endorsements, interpret interactions between documents, and answer questions with citations. The Nomad Process tailors the system to your playbooks so outputs align with your taxonomy, reinsurance structures, and capital model inputs.

What this means for a CRO:

• End-to-end ingestion: Upload policy schedules, declarations pages, endorsements, and program summaries – gigabytes at a time. Doc Chat scales to thousands of pages per file and thousands of files per run.
• Coverage understanding: The model interprets how endorsements amend the dec page and schedules, resolves conflicts, and surfaces the authoritative value with citations back to page and paragraph.
• Normalization & mapping: AI maps carrier-specific language to your standard coverage taxonomy (e.g., property BI vs. time element, perils, aggregates, SIR vs. deductible), enabling portfolio rollups.
• Peril- and geography-aware logic: Extracts peril-specific deductibles and waiting periods (e.g., Named Storm, EQ, Flood) and associates them with locations and schedules of values when included.
• Currency and unit harmonization: Converts currencies and normalizes units (sq. ft./m2, miles/km) inline with your modeling conventions.
• Real-time Q&A: Ask, ‘List all deductibles for Named Storm by state and minimum dollar amounts’ or ‘Show per-project aggregates and SIR terms on OCIPs’ – answers return instantly with links to the source.
• Export-ready outputs: Generate structured CSV/JSON for capital models, treaty exhibits, rating agency BCAR packs, and ORSA documentation without re-keying.

Typical CRO prompts include:

• ‘Extract limits from policy schedules AI: Return blanket, per-location, BI/EE, and special peril sublimits for all Property policies, with citations.’
• ‘Find deductible in insurance policy automatically: For each account, list AOP, wind/hail, named storm, EQ, and flood deductibles, including percentage, minimums, and waiting periods.’
• ‘AI to aggregate sublimits in commercial insurance: Aggregate all civil authority, ingress/egress, contingent BI, and dependent property sublimits by state and NAICS exposure.’

Because every answer includes page-level citations, your team can trust and verify outputs instantly – a key requirement for audit, reinsurance partners, and regulators. As highlighted in Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI, page-cited answers are essential to building internal trust and accelerating adoption.

Business Impact for the Chief Risk Officer

When Doc Chat takes over the heavy lifting of reading, reconciling, and normalizing policy artifacts, CRO organizations realize immediate gains:

• Time savings: Reviews that once took analysts 4–8 hours per program compress to minutes. Portfolio-wide refreshes that previously required multi-week sprints complete in a morning.
• Cost reductions: Manual re-keying and spreadsheet consolidation steps disappear. Surge staffing during renewal season is largely eliminated.
• Accuracy and completeness: The system maintains identical attention from page 1 to 1,500, reducing missed endorsements and footnotes that drive leakage. Consistent coverage mapping stabilizes models.
• Better reinsurance placement: Cleaner, faster, and more defensible exposure rollups improve negotiations, collateral discussions, and reinsurance audits.
• Stronger solvency posture: ORSA, RBC/BCAR, and Solvency II reporting reflect real limits, sublimits, deductibles, and waiting periods – not optimistic interpretations. Confidence in tail risk improves.
• Higher throughput with the same team: Analysts shift from rote extraction to scenario analysis, stress testing, and strategic capacity allocation.

These outcomes mirror the broader productivity gains we detail in AI’s Untapped Goldmine: Automating Data Entry and Reimagining Claims Processing Through AI Transformation: when high-value teams stop doing low-value data entry, velocity and decision quality increase simultaneously.

Why Nomad Data’s Doc Chat Is the Best Fit for CROs

CRO organizations need more than a generic document reader. They need an insurance-native platform guided by white glove experts that can reflect their exact coverage taxonomy, reinsurance logic, and solvency reporting requirements. Nomad Data delivers on five dimensions:

• Volume at enterprise scale: Ingest entire policy books and endorsement backlogs; Doc Chat processes massive files and portfolios without added headcount.
• Complexity with inference: Detects exclusions, endorsements, and trigger language that modify limits, sublimits, and deductibles – even when scattered across inconsistent documents.
• The Nomad Process: We train Doc Chat on your playbooks and standards so the outputs match your risk taxonomy, capital model, and treaty structures.
• Real-time Q&A and citations: Natural language queries deliver exact answers with page links for instant verification.
• White glove implementation: Dedicated experts handle configuration, mapping, and integration. Typical timelines are 1–2 weeks from kickoff to production value.

Security and governance are first-class concerns. We operate under rigorous controls, and our approach to traceability provides clear, document-level provenance for every answer – reinforcing regulatory confidence and reinsurance partner trust, as highlighted in the GAIG case study.

From PDFs to Portfolio Intelligence: What the Workflow Looks Like

Doc Chat is designed to minimize change management and deliver value fast.

1. Kickoff and playbook capture (Days 1–3): We align on your coverage taxonomy, peril definitions, reinsurance needs, and solvency reporting structures. You provide representative policy schedules, declarations pages, endorsements, and program summaries.
2. Configuration and tuning (Days 3–7): We tune extraction presets to your outputs: Property (limits, sublimits, waiting periods, peril-specific deductibles), Specialty & Marine (voyage, conveyance, franchise), and GL/Construction (occurrence, aggregates, SIRs, defense).
3. Pilot run and validation (Days 7–10): We process a subset of accounts; your analysts verify citations and outputs and request any refinements. Adjust mappings as needed.
4. Rollout and integration (Week 2): Export structured data to your capital models, risk dashboards, reinsurance exhibits, and data warehouses via API, SFTP, or direct connectors.

Users can also start immediately with drag-and-drop uploads while integrations are completed – a pattern that shortens time-to-value and builds trust faster.

What Doc Chat Actually Extracts – Concrete Examples by LOB

Property & Homeowners Examples

For a coastal portfolio, Doc Chat will:

• List blanket and per-location limits for building, contents, and BI/EE.
• Extract peril-specific deductibles: e.g., Named Storm 5% subject to $250,000 minimum; Wind/Hail 2% with $100,000 minimum; EQ 10% by location code.
• Surface time element sublimits: Civil Authority $1,000,000; Ingress/Egress $500,000; Contingent BI $2,000,000; each with waiting periods.
• Note Ordinance or Law A/B/C limits and applicable sublimits for demolition and increased cost of construction.
• Identify pollution cleanup, debris removal additional limits, and any annual aggregates reducing available limit.

Outputs align with peril and location, enabling seamless ingestion into cat models and capital assumptions, with precise deduction of retained risk before reinsurance.

Specialty Lines & Marine Examples

For a global cargo account, Doc Chat will:

• Identify voyage limits by conveyance (ocean, air, truck, rail), warehouse limits, and project cargo allowances.
• Extract franchise deductibles and conditions where no loss is paid unless the threshold is exceeded, then paid in full.
• Capture delay-in-startup or soft costs sublimits and any testing and commissioning time limits or conditions.
• Record territorial restrictions, deviation clauses, held covered conditions, and pairs and sets language affecting loss calculation.

The final data set flags exposures that are often missed in manual reviews, which materially improves treaty terms and retained risk calculations.

General Liability & Construction Examples

For a national contractor OCIP, Doc Chat will:

• Extract $2,000,000 per occurrence, $4,000,000 general aggregate, and $2,000,000 products-completed operations aggregate, with per-project aggregate endorsement details.
• Capture SIR terms (e.g., $500,000 per occurrence) and defense treatment (inside or outside limits), plus any reimbursement timing.
• Identify additional insured endorsements, primary and non-contributory language, and waivers of subrogation that shift net exposure.
• Surface completed ops tail duration and any sunset clauses.

Doc Chat then maps these terms into portfolio rollups so the CRO can see true net retained GL exposure by project, state, and subcontractor profile.

Answer Engine Optimization for CRO Use Cases: High-Intent Queries We Handle

We designed Doc Chat to answer the exact questions CROs ask in renewal season, treaty negotiation, and ORSA prep. Common prompts include:

• ‘extract limits from policy schedules AI’ – Return all limits and sublimits by coverage part, peril, and location, including conflicts resolved via endorsement chronology.
• ‘find deductible in insurance policy automatically’ – Enumerate all deductibles with type (percentage vs. flat), minimums, waiting periods, and per-occurrence vs. per-location applicability.
• ‘AI to aggregate sublimits in commercial insurance’ – Roll up civil authority, ingress/egress, contingent BI, dependent properties, and special time element sublimits by geography and line of business.

Every answer is thorough and complete, with the ability to follow up instantly: ‘Now show only the policies with Named Storm minimum deductible above $250,000 and a civil authority waiting period greater than 72 hours.’

Governance, Security, and Auditability

Doc Chat is built for regulated insurers. You control data residency and retention, and every extracted value includes a trail back to the original document and page. Outputs are consistent and defensible across teams and time, reducing audit risk and smoothing reinsurance reviews. As our clients have seen, the combination of speed and explainability accelerates internal adoption and external stakeholder confidence.

From Data Entry to Decision Intelligence

The CRO mandate is not to type faster; it is to make better, quicker decisions. Still, rote data entry has been the bottleneck. As we outline in AI’s Untapped Goldmine: Automating Data Entry, even advanced risk work is often trapped behind the grind of transcribing policy terms into spreadsheets. Doc Chat removes that friction so your team can focus on:

• Stress testing retention structures against peril-specific deductibles and sublimits.
• Negotiating reinsurance with confidence in attachment accuracy and net position by peril and geography.
• Refreshing ORSA, RBC/BCAR, and Solvency II packs whenever endorsements arrive – not just at year end.
• Rapidly onboarding acquired books by extracting and normalizing limits, sublimits, and deductibles before close.

Frequently Asked Questions from CROs

How does Doc Chat handle conflicting terms across dec pages, schedules, and endorsements?

We apply chronology- and authority-aware logic that mirrors your legal playbook: endorsements that amend prior terms take precedence, manuscript endorsements may outrank boilerplate, and schedule footnotes can restrict otherwise broad limits. Outputs always include citations, so legal and compliance can confirm intent.

Can you support multiple carriers and policy administration systems?

Yes. Doc Chat is document-centric, not system-centric. It learns carrier-specific language and formats and maps them into your standard taxonomy. Our APIs push normalized outputs into your capital models, data warehouse, and risk dashboards regardless of source system.

What about currencies, percentages, and minimums?

We normalize currencies using your reference curves and represent percentage deductibles alongside minimums and conditions (e.g., ‘5% subject to $250,000 minimum, per location’). Waiting periods and franchise conditions are captured and rendered consistently.

How quickly can we go live?

Most CRO teams see production value in 1–2 weeks. We start with drag-and-drop uploads, tune presets to your taxonomy, validate with a pilot set, and then connect exports to your models and dashboards.

Will the AI miss subtle coverage nuances?

Doc Chat is trained to surface subtle modifiers and cross-references across attachments, footnotes, and definitions. It is designed to be thorough and complete – and every result is verifiable via citations. Your experts remain in the loop for final judgment.

Proof in Action

Across clients processing thousands of policy packets, Doc Chat routinely compresses multi-day review queues into minutes. As noted in our client stories, AI’s strengths go beyond speed: consistency and completeness increase as document volume grows. For CROs, that translates to more accurate capital allocation, fewer reinsurance surprises, and a demonstrably stronger solvency posture.

Start Where It Hurts Most

If you’re exploring where to deploy AI first, pursue the highest-ROI wedge: automating the extraction and normalization of limits, sublimits, and deductibles from policy schedules, dec pages, and endorsements. It is the single most leveraged step in your portfolio risk pipeline. Once solved, downstream processes – reinsurance, capital modeling, ORSA – accelerate immediately.

Next Steps

See how fast your team can move when the system reads and reasons across every page on your behalf. Explore Doc Chat for Insurance, or dive deeper into the difference between true document reasoning and simple extraction in Beyond Extraction. Within 1–2 weeks, most CRO teams are exporting structured, audit-ready policy terms straight into their capital and reinsurance workflows.

Summary for the CRO

Doc Chat delivers instant, auditably accurate answers to the hardest question in portfolio risk: what, exactly, is covered and on what terms – across Property & Homeowners, Specialty Lines & Marine, and General Liability & Construction. With ‘extract limits from policy schedules AI’ capability built-in, the ability to ‘find deductible in insurance policy automatically’, and scalable ‘AI to aggregate sublimits in commercial insurance’, your team can finally move from document chasing to decisive risk management.