Streamlining Cat Model Inputs: Extracting Risk Exposures from Cedent Documents with AI — Reinsurance and Property & Homeowners

Catastrophe modelers face a familiar bottleneck every renewal season: turning messy, multi-format cedent submissions into clean, model-ready exposure data under tight deadlines. Statements of Values (SOVs), Location Schedules, Appraisal Reports, and full Property Risk Submission Packages arrive as zipped folders of Excel files, PDFs, images, and email threads—often with inconsistent fields, partial addresses, or missing COPE and secondary modifier details. While the clock ticks toward treaty signings and facultative decisions, valuable time is lost reconciling spreadsheets and chasing down clarifications instead of running models and advising underwriters.

Nomad Data’s Doc Chat eliminates this bottleneck. Built specifically for high-volume, high-complexity insurance documentation, Doc Chat automatically ingests entire cedent submissions, extracts locations and values, normalizes construction and occupancy details, and maps them to RMS, AIR, or Oasis-compatible formats—ready for modeling. With real-time Q&A over the ingested files, catastrophe modelers can ask targeted questions like, “List all Florida locations within 1 mile of the coast with TIV > $10M” and receive verified answers with page-level citations in seconds. Learn more about the product here: Doc Chat for Insurance.

The Catastrophe Modeler’s Exposure Problem in Reinsurance and Property/Homeowners

For a catastrophe modeler in reinsurance, the core challenge is speed-to-confidence. You need to convert ambiguous cedent data into trusted cat-model inputs fast enough to inform pricing and capacity decisions. That means resolving inconsistent SOVs, standardizing COPE data, completing secondary modifiers, and applying policy terms correctly—all before RMS/AIR/Oasis runs can begin. The nuances of reinsurance make this harder than primary insurance because cedent submissions vary wildly by organization, territory, and broker, and often mix commercial property with homeowners schedules across multiple geographies and perils.

Common pain points include:

• Heterogeneous SOV structures: One cedent’s “Bldg Value” equals another’s “TIV – Bldg,” and a third uses merged cells or text notes in place of fields. Location Schedules might be split across tabs by state or peril, or embedded in PDFs or emails.
• Incomplete or ambiguous addresses: “Main & 3rd St.” with no number, cross-border addresses missing provinces, or U.S. addresses missing ZIP+4, making geocoding uncertain.
• COPE inconsistency: Construction and occupancy provided as free text (e.g., “steel frame w/ tilt-up panels,” “big box retail”) that must be mapped to model-ready codes (ISO class, occupancy groupings).
• Secondary modifiers scattered or absent: Roof geometry, roof covering, roof age, opening protection, number of stories, sprinkler/protection, distance to coast or brush, and first-floor height might be present but scattered across Appraisal Reports and engineering addenda—or simply missing.
• Policy terms complexity: Layering, sublimits (EQ/Flood/Wind/NWS), percentage deductibles by peril, BI/PD splits, time-element details, coinsurance, and blanket limits often live in binders and endorsements, not the SOV itself. Applying these nuances correctly to the exposure data is critical to getting modeled AALs, OEPs, and PMLs right.
• Global idiosyncrasies: Multi-country submissions include varying address conventions, measurement units, currencies, and building codes—plus distinct secondary modifiers by peril (e.g., seismic retrofits vs. roof deck attachment).
• Multiple source versions: Different files from broker, cedent, and prior-year submissions must be reconciled. Version control and deduplication are often manual and error-prone.

Modelers end up spending more time building a consistent exposure dataset than analyzing cat risk. The opportunity cost is material: every hour burned on data cleanup delays hazard analytics, event selection, sensitivity testing, and pricing discussions with treaty underwriters.

How the Process Is Handled Manually Today

Most catastrophe modelers and exposure analysts still rely on painstaking manual workflows before RMS, AIR, or Oasis runs can even begin. A typical process looks like this:

1) Intake and triage: Download broker and cedent submissions (SOVs, Location Schedules, Appraisal Reports, Property Risk Submission Packages), unpack zip files, and bookmark key documents for later reference.

2) SOV reconciliation and normalization: Open Excel files with merged cells and hide-and-seek headers. Standardize column names (e.g., TIV, Building, Contents, BI/TE, construction, occupancy). Split or pivot by location where necessary. Resolve formula errors. Unmerge cells. Extract tables from PDFs into CSV, repair broken rows, and retype values where OCR fails.

3) Address hygiene and geocoding: Normalize street names, parse city/state/ZIP or international equivalents, standardize country codes, convert units, and send batches to geocoding. Validate rooftop vs. parcel vs. city-centroid quality and de-duplicate near-identical records. Correct obviously wrong lat/long (e.g., points in the ocean, wrong country).

4) COPE and secondary modifiers: Translate free-text construction/occupancy to model codes, extract year built and number of stories, identify roof details, protection class, distance to coast or brush, flood elevations, first-floor height above grade, and sprinkler/alarms. Much of this is buried in Appraisal Reports, engineering letters, or email attachments requiring manual reading.

5) Terms and conditions: Read binders, endorsements, and term sheets to extract deductibles (all-peril, named storm, hurricane, wind/hail, EQ, flood), sublimits, occurrence and aggregate limits, SIRs, participation, territories, scheduled vs. blanket coverage, BI waiting periods, and seasonal occupancy modifiers. Then align all of this with how the model expects inputs.

6) Quality checks and mapping: Check for missing values, negative TIVs, duplicate locations, odd sums, and unit inconsistencies. Map fields into RMS/AIR/Oasis templates. Create import files, fix validation errors, iterate until clean.

7) Documentation and audit trail: Capture assumptions, record how ambiguous fields were resolved, and build a data dictionary so that downstream analysts and audit teams understand the final dataset.

In a hurry-up-and-wait reality, catastrophe modelers lose days to extraction, reformatting, and validation. This manual pipeline is a major reason teams search for “extract SOV data for cat modeling AI,” “automated location schedule ingestion,” “AI to pull property values from reinsurance cedent submissions,” and “process property risk documents for cat model input.”

extract SOV data for cat modeling AI: How Doc Chat Automates the Pipeline

Doc Chat automates end-to-end exposure preparation so cat modelers can move from intake to modeling in minutes, not days. It’s engineered for volume and complexity, ingesting entire claim and submission files—including thousands of pages—without adding headcount. The platform is purpose-built for insurance documentation, as detailed in our piece Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

Here is what the automation looks like for reinsurance and Property/Homeowners submissions:

• Submission ingestion across formats: Drag-and-drop folders containing SOVs, Location Schedules, Appraisal Reports, Property Risk Submission Packages, binders, endorsements, loss summaries, and broker emails. Doc Chat reads spreadsheets, PDFs (native and scanned), images, and email attachments together.
• Field normalization and schema mapping: The system learns your preferred RMS, AIR, or Oasis schema and standardizes column names automatically (e.g., TIV, Building, Contents, BI/TE, construction class, occupancy, year built, stories). It also maps free-text construction and occupancy to model codes using your playbook.
• Geocoding and address quality: Doc Chat parses, cleans, and geocodes addresses, returns lat/long plus match quality, flags low-confidence results, and proposes corrections. International addresses are handled with country-appropriate parsing.
• Secondary modifiers and COPE extraction: The AI pulls roof geometry and covering, roof age, opening protection, number of stories, sprinkler and alarm presence, protection class, distances to coast/brush/hydrant, flood elevations, and first-floor height directly from Location Schedules and Appraisal Reports, cross-referencing where needed.
• Terms and conditions extraction: The system reads binders and endorsements to extract deductibles (by peril and percentage vs. flat), sublimits, occurrence and aggregate limits, time-element parameters (e.g., BI waiting period), and any peril-specific notes (e.g., “Hurricane applies to counties X, Y”). It associates these terms with the appropriate locations and coverage parts.
• De-duplication and version control: Doc Chat reconciles multiple versions of the same SOV, merges tabs and attachments, and removes duplicates, preserving an audit trail of every change and source.
• Model-ready exports: With one click, generate RMS-, AIR-, or Oasis-compatible CSVs/templates and optional quality reports. You can also export a roll-up by treaty/program, peril, or geography, and queue it directly for your modeling platform.
• Real-time Q&A and explainability: Ask questions like “Show all coastal Florida locations within 1 mile of the shore with TIV > $10M and roof age > 20 years” or “List every location missing roof geometry for wind modeling,” then click the citations to verify the source page instantly. This page-level transparency mirrors the auditability many claims teams value, as described by Great American Insurance Group in this webinar recap.

The result: a clean, traceable exposure dataset that reflects the cedent’s intent and your firm’s modeling standard, prepared in minutes and ready for hazard analysis, event sets, and sensitivity studies.

automated location schedule ingestion: From Mixed Formats to Clean Coordinates

Location Schedules might contain 10,000+ rows split across tabs with different headers and regional quirks. Some include lat/long, others don’t; some state construction as “steel” while others say “metal”—each requiring interpretation. Doc Chat’s automated location schedule ingestion consolidates these tables across files, harmonizes field names, turns free text into standardized codes, and fills gaps using cross-document inference. It geocodes with quality codes and flags uncertainties so catastrophe modelers can review exceptions instead of reading every line.

Critically, Doc Chat links every datapoint back to its source. If an occupancy was inferred from an Appraisal Report, you can click to see the exact paragraph where it’s stated. If year built was found in an engineering attachment, the citation shows the page and file. This explainability helps catastrophe modelers defend assumptions to exposure managers, treaty underwriters, internal validation teams, reinsurers, and regulators.

AI to pull property values from reinsurance cedent submissions

Total Insured Value is rarely a single, clean number in cedent packages. Building, Contents, and Business Interruption/Time-Element values may appear on separate tabs, be subtotaled for campuses, or be described in footers. Appraisal Reports can include replacement cost updates that contradict the SOV. Doc Chat consolidates these signals into a single truth set, clearly labeling the lineage and confidence of each value. When choose-your-own-value situations arise, the modeler can select a rule (e.g., “prefer Appraisal update over SOV if dated within 24 months and higher by 5%+”) that Doc Chat will apply consistently across the portfolio.

Beyond values, Doc Chat also extracts and standardizes coverage part splits (PD vs. BI), blanket vs. scheduled limits, and coinsurance notes. It can compute roll-ups by peril region for treaty underwriting and quickly answer questions like, “What’s the TIV exposed to storm surge in Gulf Coast counties at elevations < 10 feet?”

process property risk documents for cat model input: A Q&A-Driven Workflow

Traditional workflows start with reading and end with modeling. Doc Chat flips the sequence by making the review interactive. Catastrophe modelers can interrogate the submission from day one:

• “List all locations missing secondary modifiers required for RMS wind.”
• “Which locations have conflicting construction classifications between SOV and Appraisal?”
• “Highlight addresses with low-confidence geocodes and propose fixes.”
• “Extract all named storm deductible terms and identify locations to which they apply.”
• “Produce an Oasis-compatible export and a separate AIR Touchstone import for the same set.”

This Q&A style accelerates completeness checks and turns exception handling into a manageable queue. It also supports data governance: every answer is accompanied by citations so auditors and reinsurer partners can verify the basis. For more on why this depth of reasoning matters in document AI, see Beyond Extraction.

What Doc Chat Extracts for Catastrophe Modeling

Doc Chat is tuned to the fields catastrophe modelers need for wind, convective storm, earthquake, flood, wildfire, and more. Typical outputs include:

Location and coordinate details
• Full address (standardized)
• Latitude/Longitude with geocode quality code
• Country/State/County/City and postal codes
• Distance to coast, distance to brush, protection class, nearest hydrant and fire station (where available)

Values by coverage part
• Building, Contents, BI/Time-Element, and TIV
• Currency standardization and FX conversion (optional)
• Roll-ups by campus, location ID, and treaty/program

COPE and secondary modifiers
• Construction class (mapped to model codes), occupancy (grouped), year built
• Number of stories, floor area (units normalized), basement presence
• Roof geometry and covering, roof deck attachment/age (where documented)
• Opening protection, sprinkler and alarm presence, susceptibility indicators
• First-floor height, flood elevation references (BFE), foundation type

Terms and conditions
• All-peril and peril-specific deductibles (flat/percent), sublimits, occurrence/aggregate limits
• BI waiting periods and time limitations
• Territorial and seasonal provisions
• Layering, attachment, coinsurance, blanket vs. scheduled details

Quality, lineage, and exceptions
• Field-level source citations (file, page, paragraph)
• Confidence scoring and exception flags
• Data completeness summary for each cedent package

Business Impact for Catastrophe Modelers and Reinsurance Teams

Catastrophe modelers measure success in speed-to-model, quality-of-input, and defensibility of assumptions. Doc Chat moves the needle across all three.

Time savings and throughput
• Move from multi-day prep to same-morning modeling. Teams report reductions from 10–20 hours per large submission to under an hour, even when Appraisal Reports and endorsements are involved.
• Ingest and normalize entire books of business during busy renewal peaks without temporary staffing.

Cost reduction
• Eliminate overtime and reduce the need for specialized data-cleaning resources.
• Lower rework from import failures and validation errors in RMS/AIR/Oasis.

Accuracy and consistency
• Improve geocode quality and reduce centroid errors that distort peril footprints.
• Standardize COPE and secondary modifiers to your modeling taxonomy.
• Ensure terms and conditions are applied consistently to the correct locations and coverages.

Faster quote turnaround and better capacity decisions
• Provide treaty underwriters with earlier AAL/PML scenarios and sensitivity runs.
• Support facultative decisions with faster, more complete secondary modifier capture (particularly impactful for wind and wildfire).

Across industries, the ROI from automating document-driven data entry is well documented. See AI’s Untapped Goldmine: Automating Data Entry for examples of organizations achieving rapid payback by replacing manual extraction with purpose-built AI pipelines.

Why Nomad Data’s Doc Chat Is the Best Solution for Reinsurance Exposure Prep

Nomad Data focuses on the real-world messiness of insurance documentation—volume, complexity, and the unwritten rules embedded in your team’s process. For catastrophe modelers, the differentiators matter:

• Volume: Doc Chat ingests entire cedent submissions—thousands of pages and rows—so ingestion and normalization move from days to minutes.
• Complexity: It understands that exclusions, endorsements, and terms hide inside dense PDFs. It reads these and applies your modeling playbook to reflect peril-specific deductibles and sublimits correctly.
• The Nomad Process: We train Doc Chat on your exposure schemas (RMS/AIR/Oasis), COPE taxonomies, and exception rules, delivering a personalized solution tuned to your modeling workflow.
• Real-time Q&A: Ask for lists, anomalies, or model-ready exports. Get instant answers with page-level citations you can trust.
• Thorough & complete: Doc Chat surfaces every reference to coverage, liability, or damages that affects exposure modeling—so no modifier, term, or value slips through.
• Partner in AI: You’re not just buying software. You’re gaining a strategic partner who evolves with your book, perils, and platform choices over time.

Security and governance are table stakes. Doc Chat supports document-level traceability, audit trails, and policy-grade data controls. For an example of why explainability and page-level citations build trust, see Great American’s experience in Reimagining Insurance Claims Management.

White-Glove Service and a 1–2 Week Implementation Timeline

Most reinsurers don’t have spare months for tool onboarding—especially heading into renewal. Nomad’s white‑glove service gets Doc Chat live in 1–2 weeks:

• Week 1: Discovery and calibration. We review your cedent submission samples, RMS/AIR/Oasis templates, COPE/secondary modifier taxonomies, and data-quality rules. We configure mapping and exception handling to mirror your standards.
• Week 2: Validation and rollout. Your modelers run Doc Chat on historical submissions and compare outputs to prior imports. We refine mappings as needed, integrate with your SFTP/S3 or modeling data share, and hand over a production-ready pipeline.

Teams can start with a simple drag-and-drop interface on day one and add integrations over time. For a broader look at how insurers adopt AI across workflows with minimal disruption, see AI for Insurance: Real-World AI Use Cases.

Examples: What Catastrophe Modelers Can Ask Doc Chat

Doc Chat’s Q&A mode turns every cedent submission into an interactive data source:

• “Which California locations have wood-frame construction, no sprinklers, and TIV > $5M?”
• “Show all locations within 0.5 miles of the WUI (wildland-urban interface) and roof age > 25 years.”
• “Identify locations with inconsistent roof geometry between SOV and Appraisal, and cite the source pages.”
• “List the hurricane deductibles by county for this program and the locations each applies to.”
• “Create an AIR import and an Oasis LMF-compatible CSV for the Florida subset.”

Each answer comes with links that take you to the exact PDF page or cell where the data was found, so you can audit and defend every assumption.

From Claims to Cat: Proof That Scale and Speed Already Work

Nomad’s insurance solutions have proven performance on massive document sets—even outside exposure prep. In claims, for example, Doc Chat summarized 10,000–15,000-page medical files in minutes where humans required weeks, as described in The End of Medical File Review Bottlenecks and Reimagining Claims Processing Through AI Transformation. The same scale and accuracy advantages now power reinsurance exposure workflows: whole-submission ingestion, cross-document inference, and page‑level explainability.

Quality, Governance, and Defensibility—Built In

Exposure preparation is only as good as its audit trail. Doc Chat maintains:

• File-, page-, and field-level citations for every extracted value and term.
• Versioning across SOV updates, ensuring you can roll back or compare changes.
• Exception queues and a completeness report per cedent submission (e.g., missing roof geometry for wind models or first-floor height for flood runs).
• A data dictionary of normalized fields mapped to your RMS/AIR/Oasis templates.

These controls allow catastrophe modelers to justify assumptions to exposure managers, model validation teams, rating agencies, and internal audit—with confidence.

Beyond SOVs: Portfolio Insights and Cedent Quality Scoring

Once you automate exposure extraction, new possibilities open:

• Cedent quality scoring: Rank cedents by data completeness (secondary modifiers present, geocode quality, terms clarity). Share scorecards with brokers and cedents to drive better upstream data.
• Portfolio accumulation: Instantly roll up exposure by peril region, CRESTA zones, or custom territories to inform retro and facultative strategies.
• Trend and drift detection: Compare year-over-year SOV changes and flag unusual shifts in TIV, construction mix, or secondary modifiers.
• Due diligence on books of business: Evaluate prospective acquisitions with automated policy and exposure reviews—echoing the approach discussed in the “Assessing Risk in Books of Business” section of AI for Insurance.

Security and IT Fit

Doc Chat is enterprise-grade. It supports SSO, least-privilege access, encryption in transit and at rest, and rigorous logging. It integrates with SFTP, S3, or your DMS and can output directly to secured shares for RMS, AIR, or Oasis pipelines. For teams trialing the system, the drag-and-drop UI requires no integration to start. As adoption grows, we layer in APIs and scheduled jobs without disrupting your modeling cadence.

How to Get Started—A Guide for Catastrophe Modelers

1) Identify your highest-friction submissions: Large, multi-country cedents with mixed-format SOVs, Appraisal Reports, and complex terms are great pilots.
2) Share your modeling template(s): Provide your RMS/AIR/Oasis import formats and any custom field mapping or coding standards.
3) Define your exception policy: Which uncertainties require human review (e.g., low-confidence geocodes, ambiguous occupancy)? Doc Chat will route these automatically.
4) Run a side-by-side: Process this quarter’s submissions manually and through Doc Chat. Compare cycle time, import error rates, and secondary modifier completeness.
5) Iterate and expand: Incorporate lessons into your playbook. Roll out to more cedents and add exports for additional modeling platforms as needed.

Frequently Asked Questions from Catastrophe Modelers

Which modeling platforms do you support?
Doc Chat exports to RMS-, AIR-, and Oasis-compatible formats and can be tuned to custom schemas used by your exposure management team.

How do you handle ambiguous or conflicting data?
We encode your decision rules (e.g., prefer the latest Appraisal Report; if within 5% of SOV, favor Appraisal; else flag for review). Every value retains its source citation and confidence score.

What about global submissions?
Doc Chat supports international address parsing and normalization, currency conversion (optional), and language-sensitive extraction across common global formats.

Can the system enrich missing fields?
Yes. Doc Chat cross-references across documents to fill gaps and can be configured to suggest likely values for secondary modifiers based on context—while clearly flagging any imputed field for human confirmation.

How fast is it?
Doc Chat processes large submissions in minutes and supports batch handling of multiple cedents concurrently, so modelers can move straight to analytics.

The Payoff: More Modeling, Less Manual Work

Catastrophe modelers deliver the most value when they’re analyzing hazard, testing assumptions, and guiding reinsurance capacity decisions—not when they’re unmerging cells, debugging import files, or decoding endorsements. By bringing “extract SOV data for cat modeling AI,” “automated location schedule ingestion,” “AI to pull property values from reinsurance cedent submissions,” and “process property risk documents for cat model input” into a single, automated pipeline, Doc Chat puts time back in your day and confidence back in your models.

If exposure preparation is slowing your treaty or facultative decisions, it’s time to see Doc Chat in action. Explore the product at Doc Chat for Insurance and reallocate your team’s effort from document wrangling to risk insight.