Automating Catastrophe Exposure Reviews: From Policy Schedules to Geospatial Reports in Minutes - Property & Homeowners, Specialty Lines & Marine

Automating Catastrophe Exposure Reviews: From Policy Schedules to Geospatial Reports in Minutes - Property & Homeowners, Specialty Lines & Marine
At Nomad Data we help you automate document heavy processes in your business. From document information extraction to comparisons to summaries across hundreds of thousands of pages, we can help in the most tedious and nuanced document use cases.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Automating Catastrophe Exposure Reviews: From Policy Schedules to Geospatial Reports in Minutes

Catastrophe modelers across Property & Homeowners and Specialty Lines & Marine are under unrelenting pressure: convert messy schedules and policy packets into clean, geocoded, peril-specific exposure views fast enough to support underwriting, portfolio management, event response, and reinsurance negotiations. The challenge is that the relevant facts—addresses, COPE elements, per‑location sublimits, peril endorsements, named storm deductibles, voyage itineraries—are scattered across property schedules, declarations pages, coverage summaries, and reinsurance submissions, often in inconsistent spreadsheets and multi-thousand‑page PDFs.

Nomad Data’s Doc Chat for Insurance fixes this in minutes. Purpose-built, insurance‑trained AI agents read entire files, extract locations from policy schedule and related documents, normalize fields, automate geocoding for insurance policies down to rooftop precision, map perils and sublimits from dec pages and endorsements, and output ready‑to‑load geospatial datasets for RMS, Verisk/AIR, RQE, Touchstone, Esri, and QGIS. Catastrophe modelers can ask natural‑language questions like “show TIV within 2 miles of coast with named storm deductibles above 5%” and get instant, cite‑back answers with links to source pages—no manual hunting required.

Why Catastrophe Exposure Work Is Hard for Property & Homeowners and Specialty/Marine Modelers

On paper, exposure prep seems simple: pull addresses, TIV, construction, occupancy, year built, protection, and coverage terms. In practice, catastrophe modelers must reconcile:

  • Inconsistent property schedules/SOVs: columns named fifteen different ways for TIV, COPE, or sprinkler status, or embedded in PDFs and email bodies instead of spreadsheets.
  • Coverage details hidden in declarations pages, coverage summaries, binders, and endorsements: named storm vs. wind, storm surge treatment, flood sublimits, earthquake shock/fire-following, ordinance or law, debris removal, time elements (BI/ALS), and deductibles on a per‑location or per‑occurrence basis.
  • Marine and specialty nuance: schedules of vessels and cargo, warehouse and terminal locations, voyage period exposures (port-to-port), and tide/surge/typhoon zones that change by season and region.
  • Reinsurance demands: cedants must provide peril‑segmented TIV, attachment distributions, and accumulations by geography for reinsurance submissions and treaty renewals, often on compressed timelines.

The result: cycle times stretch, manual errors creep in, and modelers spend more energy cleaning data than analyzing AEP/OEP, PML/TVaR, RDS, or negotiating with brokers and reinsurers on rate-on-line, occurrence/aggregate structures, hours clauses, or reinstatements.

How It’s Handled Manually Today

Most catastrophe exposure reviews still rely on manual workflows:

Modelers collect broker submissions via email and portals, then copy/paste from SOV spreadsheets, multi-tab Excel files, and PDF property schedules and coverage summaries. They manually reconcile COPE fields, look up BCEGS/PPC, cross-check with inspection reports, decode perils and deductibles from declarations pages and endorsements, and try to geocode addresses with patchwork tools. For international risks, they correct transliteration, format changes (unit vs. suite vs. piso), and missing postal codes. Marine portfolios add another layer: voyage declarations and terminal/warehouse schedules that must be temporally and spatially aligned.

To complete one exposure pack, a catastrophe modeler might:

1) Normalize disparate schedules; 2) Geocode addresses; 3) Identify and fix address failures; 4) Parse per‑peril sublimits and deductibles; 5) Map construction classes (ISO 1–6 or local equivalents) and occupancy codes; 6) Compute proximity to coastline, rivers, quake faults, wildfire WUI; 7) Produce shapefiles and model‑specific import sheets; 8) Prepare exhibits for underwriting committees and reinsurers. Each handoff—underwriting, exposure management, reinsurance, actuarial—adds rework and delay. Accuracy also declines when volume spikes, a reality documented in Nomad’s coverage of complex document processing and the difference between web scraping and true document inference.

AI for Catastrophe Exposure Analysis: How Doc Chat Automates End-to-End

Doc Chat’s insurance-trained agents turn unstructured submissions into structured, geospatially-ready exposure datasets at portfolio scale. This is AI for catastrophe exposure analysis designed specifically for property and marine insurance.

1) Ingest every file, in any format

Doc Chat ingests entire submissions—property schedules, declarations pages, coverage summaries, binders, endorsements, reinsurance submissions, bordereaux, loss control/COPE reports, flood elevation certificates, wind mitigation forms (e.g., OIR‑B1‑1802), engineering surveys, and marine voyage schedules—thousands of pages at a time. It reads PDFs, Excel, CSV, Word, email threads, and embedded images and turns them into a single, consistent data model with complete traceability back to source pages.

2) Normalize and standardize schedules

Column names and units are standardized (e.g., TIV vs. ITV vs. SI; $ vs. £; meters vs. feet), COPE fields are aligned to your internal taxonomy, and missing fields are inferred when supported by documents (e.g., construction from inspection notes, occupancy from line descriptions). For global portfolios, local address conventions and diacritics are preserved so geocoding accuracy remains high.

3) “Extract locations from policy schedule” with rooftop geocoding

Doc Chat uses a layered approach to automate geocoding for insurance policies: deterministic parsing, postal/CASS validation, multi-provider rooftop geocoding, and fallbacks to centroid methods with confidence scoring. It flags ambiguous or low-confidence results for human review, cites the exact row and column in the property schedule or the lines in the coverage summary, and suggests corrections (e.g., “Street number transposed; verified against USPS; confidence 0.98”). Multi-building campuses or large terminals can be split into sub‑locations based on unit/suite identifiers, parcel IDs, or coordinates mentioned in inspection reports.

4) Map coverage, perils, and deductibles from dec pages and endorsements

Beyond simple field extraction, Doc Chat performs true policy inference. It reads declarations pages, endorsements, and coverage summaries to determine peril applicability (named storm vs. any wind, storm surge inclusion, flood sublimits, earthquake shock vs. fire‑following, strike/riot/civil commotion, terrorism carve‑outs), sublimits by location or group, and complex deductible structures (e.g., 5% named storm with min/max per location, time‑element deductibles for BI/ALS). It constructs a per‑location coverage table that matches your modeling system’s expected schema.

5) Deduplicate and reconcile conflicts

Duplicate rows and conflicting values are resolved with rules you control: latest effective date wins, source precedence (broker SOV vs. inspection vs. dec page), or highest confidence value. Changes are logged and fully auditable.

6) Geospatial enrichment and hazard proximity

Doc Chat computes distances to coastlines, major rivers, levees, and known floodplains; overlays public and licensed hazard layers (FEMA zones, USACE surge, NOAA/NHC coastal buffers, USGS faults, wildfire WUI, elevation), and tags each location with peril‑specific attributes for downstream modeling. For marine, it enriches with port polygons, terminal coordinates, and seasonal cyclone/typhoon climatology to reflect voyage exposures.

7) Outputs your team can use immediately

Deliverables include:

- Cleaned exposure spreadsheets aligned to RMS, AIR/Touchstone, RQE, or your bespoke schema
- Shapefiles/GeoJSON for Esri ArcGIS, QGIS, and portfolio analytics tools
- Per‑peril TIV summaries, location heatmaps, and accumulation reports
- Exhibit packs for underwriting committees and reinsurance submissions
- A fully cited data room with links back to source pages and rows

8) Real-time Q&A across the entire file

Instead of reading, your catastrophe modelers simply ask questions and get page‑linked answers in seconds. Examples that teams use daily include:

  • “List all U.S. locations within 1 mile of coastline with TIV > $5M and named storm deductible ≥ 5%.”
  • “Show all warehouses in FEMA Zone AE or VE with flood sublimits < $500k; include dec page citations.”
  • “Which addresses failed rooftop geocoding? Provide suggested corrections and confidence scores.”
  • “Break out TIV by construction class (ISO 1–6) and occupancy for Texas and Louisiana.”
  • “For marine, list vessels typically berthed at Port of Houston with cargo TSI > $10M and nearest surge depth > 6 ft.”

Every answer links back to the exact paragraph in the declarations page, the cell in the property schedule, or the clause in the endorsement. This is the transparency reinsurers and auditors expect.

Business Impact: Time, Cost, and Accuracy You Can Measure

Exposure teams see dramatic gains when they replace manual extraction with automated inference. Doc Chat ingests thousands of pages per minute and returns complete, geocoded datasets in minutes—turning a multi-day cat review into an afternoon task and reducing the rework that plagues busy seasons and renewal crunches.

Time savings: Manual schedule normalization and geocoding for a mid‑sized portfolio (5,000–15,000 locations) typically consumes days. Doc Chat performs the same work in minutes, even when peril terms require deep endorsement parsing. Faster exposure prep accelerates underwriting decisions, improves speed‑to‑bind, and enables earlier RDS and accumulation views for management and regulators (NAIC, ORSA, Solvency II, Lloyd’s RDS).

Cost reduction: Less overtime, fewer external vendors for data cleanup or modeling file conversions, and lower friction in reinsurance negotiations. Teams reallocate hours from repetitive data handling to high‑value analysis and scenario planning, a shift Nomad has seen across clients and described in its coverage of AI’s impact on document‑heavy processes (AI’s Untapped Goldmine).

Accuracy improvement: Machines do not tire. They read page 1 and page 1,500 with equal rigor, ensuring consistent peril mapping, deductible application, and geocoding confidence. This reduces leakage (e.g., under‑recognized named storm deductibles or missing flood sublimits) and improves modeled loss metrics (AEP/OEP, PML/TVaR) used in pricing and capital allocation.

Negotiating leverage: Clean, peril‑segmented TIV, transparent deductibles, and geospatial exhibits shorten reinsurer Q&A cycles, support better terms and rate-on-line, and provide an audit trail that reinsurers trust. With Doc Chat you can respond to broker or reinsurer requests the same day, backed by page‑level citations.

Automating Geocoding for Insurance Policies and Producing Geospatial Reports—Fast

The phrase “automate geocoding for insurance policies” often implies fragile, single‑provider lookups. Doc Chat’s approach is different: multi‑engine rooftop geocoding, postal verification, linguistic normalization for international addresses, and confidence‑scored fallbacks—plus a guided queue for the small percentage of addresses that need human eyes. The output is a geospatial package your catastrophe modelers can trust, complete with shapefiles, GeoJSON, and modeling‑system‑ready CSVs.

Because Doc Chat is built for insurance, it ties geocoding to peril analytics in one shot—coastal proximity, FEMA zone tagging, levee/floodway checks, wildfire WUI overlays, elevation pulls, nearest USGS fault distances, surge depth estimates by return period, hail/wind climatology overlays, and custom accumulations around risk aggregations such as ports/terminals or ZIP3/CRESTA zones.

From Property Schedules and Declarations Pages to Reinsurance Submissions—Without Rework

For cedants and brokers, every reinsurance renewal is a test of exposure clarity. Reinsurers want to see per‑peril TIV, deductible distributions, and accumulations near coastlines or fault zones. They ask for explanations when surge is excluded, or when flood sublimits are applied unevenly. Manually preparing those views means re‑reading coverage summaries and declarations pages to verify terms across hundreds of policies.

Doc Chat does this automatically. It extracts named storm vs. all‑wind language, flags storm surge inclusion/exclusion, identifies flood and quake sublimits and time‑element deductibles, and compiles a reinsurer‑ready exposure pack with exhibits, all linked back to source pages. If an underwriter or broker requests a new cut (e.g., “Top 100 TIV within 2 km of coastline with flood sublimit < $250k”), your modelers generate it on demand.

Specialty Lines & Marine: Voyage, Terminal, and Warehouse Exposures

Marine and specialty property present unique exposure dynamics:

- Voyage risk: exposure follows itineraries, calling for seasonal cyclone/typhoon analytics and port‑to‑port surge/wind views.
- Terminal/warehouse schedules: large campuses, container yards, and storage sheds that require sub‑location modeling and capacity aggregation.
- Hull/Cargo documentation: schedules of vessels, storage limits, and time‑in‑port distributions often buried in PDFs and email attachments.

Doc Chat ingests these document types alongside property schedules and parses voyage declarations, terminal coordinates, and capacity limits to produce geocoded, peril‑tagged exposure files and interactive maps. Modelers can ask: “Show all terminals within 500 m of surge depth ≥ 3 ft under a 1‑in‑50 scenario” or “List vessels assigned to typhoon ports in Q3 with TSI > $20M.” Those questions are answered instantly, with citations back to voyage schedules, policy endorsements, or coverage summaries.

Why Nomad Data’s Doc Chat Is the Best Choice for Exposure Teams

Nomad Data has built Doc Chat specifically to solve the document‑inference problems insurance teams struggle with. As our article Beyond Extraction explains, the work isn’t just “reading” PDFs; it’s codifying unwritten underwriting and coverage rules into repeatable, auditable logic. That’s why carriers like GAIG have trusted Nomad for complex, high‑volume review, seeing day‑to‑minutes transformations (webinar replay).

What sets Doc Chat apart for catastrophe modelers:

- Volume: ingest entire submissions—thousands of pages, multi‑tab SOVs, email threads—in minutes without extra headcount.
- Complexity: accurately interpret peril triggers, endorsements, and deductible structures from declarations pages and endorsements.
- The Nomad Process: we train Doc Chat on your playbooks, per‑peril taxonomies, and modeling schemas, so outputs drop straight into your RMS/AIR/RQE or Esri workflows.
- Real‑time Q&A: ask portfolio‑scale questions and get answers with page‑level citations.
- Thorough & complete: no more blind spots—Doc Chat surfaces every reference to coverage, liability, or damages relevant to cat exposure.
- Security & governance: SOC 2 Type II, document‑level traceability, and defensible audit trails that satisfy reinsurers, regulators, and internal audit.

Most importantly, Doc Chat is delivered as a white‑glove, custom solution that fits how your catastrophe modeling team actually works. Typical implementation runs 1–2 weeks from kickoff to first live portfolio. No data science staffing, no long IT queue, and integrations to Guidewire, Duck Creek, Sapiens, Esri, Snowflake, or SFTP are straightforward.

Implementation in 1–2 Weeks: What Getting Started Looks Like

Nomad’s delivery model is designed for speed and certainty. A typical Property & Homeowners or Specialty/Marine install looks like this:

  • Week 1: Discovery and sample files. We review your current property schedules, coverage summaries, declarations pages, and reinsurance submissions; define your COPE taxonomy; decide on per‑peril schemas; and configure geocoding rules and hazard overlays.
  • Days 3–5: Prototype. We run a pilot on a real submission, output a modeling‑system‑ready CSV and shapefile, and walk your team through the QA queue with confidence scores and source citations.
  • Week 2: Integration and rollout. Optional connections to policy admin, DWH, SFTP; user training on real‑time Q&A; playbook tuning for edge cases (e.g., storm surge carve‑outs, time‑element deductibles, voyage variations).

From there, Doc Chat scales immediately to handle backlogs, renewal spikes, or event‑response surges without adding headcount—a benefit echoed across Nomad’s broader insurance AI work in AI for Insurance: Real‑World Use Cases.

Compliance, Auditability, and Trust

Exposure management interacts with multiple governance layers—internal model risk committees, reinsurer reviews, and regulatory reporting (NAIC, ORSA, Solvency II, Lloyd’s RDS). Doc Chat preserves a complete chain of custody for every data point it extracts or infers, tying each field to the originating page, clause, or cell. You can export complete audit packets with timestamps, users, and before/after values, and you control model guardrails that limit what the AI can decide vs. what it must flag for human review.

Event Response and Portfolio Steering

When a hurricane, flood, quake, or wildfire threatens, catastrophe modelers need a live view of exposed TIV, expected surge depths, wind speeds, or shake intensities—now, not tomorrow. Doc Chat lets you instantly filter to at‑risk locations (“within 3 miles of coastlines in the forecast cone” or “in ZIPs with expected gusts > 80 mph”), export impact lists for claims readiness, and share map‑based dashboards with executives. The same geospatial foundation used for renewals becomes your event‑response engine.

Examples of High-Impact Use Cases Your Team Can Run on Day One

- Build a clean, peril‑segmented exposure file from mixed PDFs and spreadsheets in under an hour; export for RMS/AIR and Esri.
- Produce reinsurer exhibits: TIV by distance‑to‑coast, by FEMA zone, by construction class, with deductible histograms and top‑site heatmaps.
- Identify locations where flood sublimits are below underwriting guidance; route to underwriters with citations to declarations pages.
- For marine, compute accumulations by terminal polygon and seasonal typhoon overlays; surface top storage yards by TSI and surge vulnerability.
- Trigger QA on low‑confidence geocodes and address anomalies; track resolution to completion.

“Extract Locations from Policy Schedule” Is Only the Beginning

Many teams arrive asking whether Doc Chat can “extract locations from policy schedule.” It can—and it does so at remarkable speed and accuracy. But the real value is downstream: peril inference from endorsements, deductible logic applied per location, geospatial enrichment, accumulation analytics, and reinsurer‑ready exhibits with source traceability. As Nomad has written, the biggest wins come when AI automates the inference work, not just the text extraction—see Beyond Extraction for why this distinction matters.

A Hypothetical Before-and-After: Property & Marine Blend

Before: A mid‑market carrier receives a 9,000‑row SOV for a coastal property book plus a marine terminal schedule. The broker’s packet includes PDFs of endorsements and coverage summaries with named storm language, flood sublimits, and a mix of BI/ALS time‑element terms. The exposure team spends a week cleaning the SOV, geocoding by hand, interpreting deductibles, reconciling contradictions, and creating exhibits. Reinsurer Q&A generates another week of back‑and‑forth to validate surge exclusion and flood sublimits near coastal terminals.

After: With Doc Chat, the same file processes in ~40 minutes. Doc Chat standardizes COPE, geocodes with rooftop accuracy, overlays FEMA and surge layers, and infers peril terms from declarations pages. It outputs RMS/AIR‑ready files plus Esri layers and a reinsurer exhibit pack. The exposure team answers follow‑ups within hours, citing dec pages directly from the Q&A console. Renewal closes early with tighter terms and a lower rate‑on‑line.

Answering the Most Searched Questions—Clearly and Completely

AI for catastrophe exposure analysis: what should I expect?

Expect end‑to‑end automation: document ingestion, schedule normalization, rooftop geocoding with confidence scoring, peril/deductible inference from dec pages and endorsements, geospatial enrichment, and ready‑to‑load outputs (RMS/AIR/RQE and Esri). Also expect page‑linked Q&A so every number is defensible in underwriting committees and reinsurance negotiations.

How do I automate geocoding for insurance policies without losing accuracy?

Use a layered approach: postal validation, multi‑engine rooftop geocoding, confidence scoring, and a guided exception queue. Tie geocoding to peril enrichment (coastline distance, FEMA zones, fault proximity, WUI) so you produce accumulator‑ready exposure with one pass. Doc Chat implements exactly this flow, out of the box.

Can you reliably extract locations from policy schedule PDFs and emails?

Yes. Doc Chat reads PDFs, spreadsheets, emails, and embedded images; identifies table structures; normalizes columns; and cites the exact source for each extracted field. When a cell or paragraph is ambiguous, the system flags it for human review and suggests a correction, preserving a complete audit trail.

Security, Data Stewardship, and IT Fit

Nomad Data is SOC 2 Type II certified. Your documents stay under rigorous access controls, and nothing is used for model training unless expressly approved. Doc Chat deploys with minimal IT lift: users start with a secure drag‑and‑drop interface; optional integrations with policy admin systems (Guidewire, Duck Creek, Sapiens), geospatial platforms (Esri), and data clouds (Snowflake) are typically completed in 1–2 weeks.

Ready to See It on Your Files?

Upload a recent renewal submission or treaty pack and watch Doc Chat build a geocoded, peril‑tagged exposure file with reinsurer exhibits in minutes. Then ask it questions you normally field from brokers or reinsurers. You’ll see the same “from days to minutes” transformation noted by peers in our GAIG webinar replay—and the operational lift described in AI’s Untapped Goldmine.

Explore Doc Chat for Insurance at nomad-data.com/doc-chat-insurance.

Learn More