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

Turn Cedent Submissions into Model-Ready Exposure in Minutes: How Catastrophe Modelers Use Doc Chat to Extract SOVs, Locations, and Values

Every Catastrophe Modeler knows the real bottleneck is not the model run—it’s preparing the input. Reinsurers and Property & Homeowners teams receive Cedent files filled with Statements of Values (SOV), location schedules, appraisal reports, and sprawling property risk submission packages. These come in every format imaginable—scanned PDFs, Excel with merged cells, embedded tables inside emails, and narrative engineering surveys. Manually wrangling this mess into a clean, peril-ready file for RMS, AIR, or your internal cat platform can consume days. That’s why Catastrophe Modelers are turning to Nomad Data’s Doc Chat, a suite of AI-powered agents built to read, extract, normalize, and validate exposures at scale—so you can move from document intake to model-ready in minutes, not weeks.

Doc Chat ingests entire submission packages, classifies document types, extracts fields line by line, infers missing COPE and secondary modifiers from narrative text, geocodes with confidence tiers, reconciles totals, and exports standardized, model-specific templates. Whether you need to extract SOV data for cat modeling AI, perform automated location schedule ingestion, or use AI to pull property values from reinsurance cedent submissions, Doc Chat gets you to clean exposure data fast. Learn more about the product here: Doc Chat for Insurance.

The Nuance: Why Cedent Submissions Challenge Catastrophe Modelers

In Reinsurance and Property & Homeowners, the diversity of cedent documentation is the core challenge. Two cedents rarely share a common schema. Your inbox might include a clean SOV as XLSX beside a scanned 200-page appraisal PDF, a location schedule embedded in a PowerPoint, and a binder email attaching an outdated schedule that contradicts the latest upload. Critical attributes for wind, quake, flood, wildfire, and severe convective storm are scattered across spreadsheet tabs and narrative reports from risk engineers. As a Catastrophe Modeler, your job is to stitch these sources into a single modeling truth before you ever press run.

Specific nuances make this hard:

Heterogeneous SOV formats: City/state in a single cell, multi-building locations, floor-level occupancy, currency mixes, and TIV vs Coverage A ambiguity. Some cedents list Building, Contents, BI separately; others roll them into TIV.
Geocoding ambiguity: Addresses with missing postal codes, non-standard abbreviations, or international formatting; PO Boxes during catastrophe modeling are noise; coordinates in DMS vs decimal; address strings embedded in narrative lines.
COPE and secondary modifiers buried in prose: Roof cover/shape/age, opening protection, year built and major renovations, sprinkler status, distance-to-hydrant/station, construction class, and occupancy type often appear only in an Appraisal Report or engineering survey.
Data conflicts: A Location Schedule may suggest Occupancy = Retail while the Property Risk Submission Package describes mixed-use with residential and office. Appraisals may update square footage or replacement cost, but not the SOV totals.
Unit and currency variance: Square meters vs square feet; meters vs feet for distance-to-coast or defensible space; multiple currencies across treaty participants with inconsistent FX dates.
Portfolio scale: Tens of thousands of locations across countries and peril zones. The manual process doesn’t scale during renewal season, M&A diligence, or when a cat event triggers urgent re-modeling.

For Catastrophe Modelers, getting to a clean, peril-ready dataset requires consistent rules, institutional knowledge of each cedent’s quirks, and a mountain of manual rework. The cost of errors is real—poor geocodes or missing secondary modifiers can distort hazard intensity, vulnerability curves, and ultimately your view of exceedance probabilities and PMLs.

How It’s Handled Manually Today: Slow, Risky, and Hard to Scale

The current manual workflow often looks like this:

Intake and triage: Unzip submission packages; catalog Statement of Values (SOV), Location Schedules, Appraisal Reports, and other documents. Flag scanned documents for OCR.
Rekey and normalize: Copy/paste or retype fields into your exposure template. Decipher merged cells, separate building/content/BI, split line items where a location includes multiple buildings, and normalize occupancy codes.
Geocode: Run addresses through internal geocoders or GIS tools, iterate on failures, and assign confidence tiers. Convert DMS to decimal degrees when needed.
Augment attributes: Read narrative reports for COPE details: roof cover/age, sprinkler and alarm, wall construction, story count, distance-to-coast, defensible space for wildfire, and soil type or code proxies for quake.
Map to cat model schema: Translate raw fields to model-ready inputs and secondary modifiers per peril. Create separate templates for wind, quake, flood, wildfire, and SCS where your platform requires it.
Validate and reconcile: Recalculate TIV by location and by policy to reconcile to cedent totals. Resolve contradictions between SOV and appraisal notes. Track questions for cedents and await responses.
Version control and repeat: When new documents arrive, re-run the entire QA chain to ensure the final model file reflects the latest schedule—and nothing is double-counted.

Even seasoned Catastrophe Modelers and exposure analysts can spend days per cedent. The process introduces fatigue-driven error risk and makes surge capacity during renewal season almost impossible without temporary staff or costly outsourcing. When an event occurs, manual preparation can delay the time-to-model just when leadership needs a refreshed view of losses.

Automating SOV and Location Schedules with Doc Chat: From Intake to Export

Nomad Data’s Doc Chat changes this equation. Designed specifically for insurance documents, Doc Chat ingests entire cedent submission packages—thousands of pages per claim file or submission—classifies each file, and extracts the fields Catastrophe Modelers need. It handles complexity far beyond template matching and brittle keyword search. Instead, it reads like a domain expert and applies your team’s playbook to produce standardized, modeling-ready outputs.

What Doc Chat does for Catastrophe Modelers in Reinsurance and Property & Homeowners:

Document understanding at scale: Automatically detects Statement of Values (SOV), Location Schedules, Appraisal Reports, engineering surveys, and broader Property Risk Submission Packages. It can process scanned PDFs and images with strong OCR.
Automated location schedule ingestion: Parses rows from messy spreadsheets and embedded tables, normalizes column headers, splits multi-building records, and assembles a master location list without copy/paste.
Field extraction and standardization: Address, city, state/province, postal code, country, latitude/longitude (when present), year built, construction class, occupancy, stories, square footage, roof cover/shape, roof age, opening protection, sprinkler, alarm, building/contents/BI values, and more.
Geocoding with confidence: Converts and validates coordinates, geocodes addresses to parcel or rooftop when available, and flags geocode quality tiers with explainable reasons.
COPE inference from narrative: Extracts secondary modifiers from Appraisal Reports and surveys, such as roof type, decking and nailing patterns, combustible attachments, defensible space, distance-to-hydrant, and elevation indicators relevant to flood.
Reconciliation and QA: Cross-checks SOV/TIV rollups against cedent totals, detects duplicates across versions, validates unit consistency (ft/m; sq ft/sq m), and harmonizes currencies using your FX policy.
Model schema mapping: Exports peril-specific, model-ready templates aligned to your internal schemas or leading cat vendors.
Real-time Q&A with citations: Ask, “List all locations within 1 mile of the shoreline” or “Show roof cover for every building lacking sprinkler,” and get answers with page-level links back to the source document for auditability.

This end-to-end pipeline reflects the core thesis we’ve shared publicly: document automation is not just about scraping fields—it’s about inference from unstructured, inconsistent sources. For a deeper dive into why this matters for insurance document work, read: Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

High-Intent Use Cases Cat Modelers Search For

Doc Chat directly addresses common Catastrophe Modeler search intents, including:

extract SOV data for cat modeling AI
automated location schedule ingestion
AI to pull property values from reinsurance cedent submissions
process property risk documents for cat model input

Each of these is solved by Doc Chat’s ability to ingest, extract, normalize, and validate across mixed-format cedent packages with full traceability.

From Questions to Answers: Real-Time Q&A for Catastrophe Modelers

Instead of spending hours hunting through PDFs and Excel tabs, Catastrophe Modelers can ask Doc Chat natural-language questions and receive instant, verifiable answers:

“Export a CSV of all locations with TIV > $10M within 1 km of coastline; include geocode confidence and distance-to-coast.”
“Show construction class and roof cover for every school occupancy; flag where opening protection is unclear.”
“List wildfire defensible space indicators and presence of combustible attachments for all California risks.”
“Recalculate TIV by Building/Contents/BI; reconcile to cedent totals and list discrepancies by location ID.”
“Identify duplicate addresses and consolidations across the two SOV versions attached to the October 3 email.”
“Map these attributes to our RMS wind secondary modifier schema and produce the model import file.”

Every answer includes citations back to the Statement of Values, Location Schedules, Appraisal Reports, or the relevant pages of the Property Risk Submission Package. Verification takes seconds, not days. For an overview of how real-time document Q&A transforms insurance teams, visit Doc Chat for Insurance.

Beyond Extraction: Inferring Secondary Modifiers from Narrative Documents

Cat models reward secondary modifiers—roof shape, deck attachment, opening protection, roof cover, roof age—because they materially shift vulnerability curves. But these details often live inside narrative engineering surveys and Appraisal Reports, not in the SOV. Doc Chat’s agents read these documents like seasoned exposure analysts. They “connect the dots” across pages to build a coherent picture of each location’s COPE and peril-relevant characteristics.

Practical examples:

Wind: Extracts roof cover (e.g., BUR, TPO, metal), roof shape (hip/gable/flat), parapet details, opening protection (shutters/impact windows), roof age and replacement dates, deck attachment indicators drawn from renovations.
Quake: Identifies year built and major retrofits, frame vs masonry vs steel, soft-story indicators, and proxies for soil class (when reports reference site soils, liquefaction susceptibility, or local building code era).
Flood/Storm Surge: Pulls elevation markers, NFIP zone references, basement presence, first-floor elevation, and notes on floodproofing or barriers.
Wildfire: Surfaces defensible space, ornamental vegetation, combustible attachments (e.g., decks and sheds), roof cover combustibility, and nearby fuel notes.
Severe Convective Storm (SCS)/Hail: Extracts roof age, cover, deck type, slope, and maintenance notes that influence susceptibility.

This is where Doc Chat’s approach shines. As described in our broader perspective on document intelligence, AI for insurance isn’t limited to simple table scraping—it’s about replicating expert inference at scale. See: AI’s Untapped Goldmine: Automating Data Entry.

Data Quality, Geocoding, and Standardization for Reliable Model Inputs

Clean exposure data depends on consistent standards. Doc Chat enforces your organization’s rules so that every cedent submission lands in the same model-ready shape—every time.

Key capabilities for Catastrophe Modelers:

Address normalization: Standardizes addresses across countries, strips PO Boxes, harmonizes abbreviations, and prepares data for high-confidence geocoding.
Advanced geocoding: Converts DMS to decimal degrees, uses hierarchical geocoding (rooftop, parcel, street, centroid), and labels results with confidence tiers and rationale.
Unit and currency management: Detects and converts units (sq m ↔ sq ft; m ↔ ft) and normalizes currencies with your approved FX methodology.
Deduplication and version control: Identifies duplicate locations across multiple attachments, consolidates by best-known attributes, and preserves a chain of custody.
Schema mapping and validation: Applies your internal exposure schema or vendor-specific templates. Validates required fields per peril and flags missing or conflicting entries.
Portfolio checks: Supports treaty-level rollups, territory analyses, and concentration checks (e.g., too many high-TIV locations within a specific coastal band).

Because every answer includes document-level citations, audit trails become effortless—crucial for reinsurers who must defend model assumptions to management, regulators, and retro partners. This principle of page-level explainability is a cornerstone of our approach across insurance. For a broader overview, see AI for Insurance: Real-World AI Use Cases Driving Transformation.

The Business Impact: Faster Time-to-Model, Better Accuracy, Lower Cost

When Catastrophe Modelers in Reinsurance and Property & Homeowners offload document work to Doc Chat, four outcomes follow:

Speed: Move from days or weeks of manual prep to minutes. Doc Chat has been engineered to process large volumes rapidly, with customers regularly collapsing multi-day reviews into near-real-time runs.
Accuracy: Consistent extraction and inference from every page, every time, reduces the human fatigue that leads to mis-geocoding and missed modifiers. Better inputs drive better loss estimates.
Scalability: Tackle renewal surges, large bordereaux, and M&A portfolio reviews without adding headcount. Turnaround times shrink; model cycles multiply.
Cost and morale: Free Catastrophe Modelers and exposure analysts from rekeying, reconciliation, and version control. Teams focus on selection, stress testing, and insight that improve treaty outcomes.

Across our insurance clients, we repeatedly see the same pattern: the most valuable “AI win” is not a flashy new model, but the elimination of manual document bottlenecks. That’s where cycle time and accuracy improve together. As we’ve written before, the big opportunity is automating the high-volume data entry tasks hidden inside complex document workflows—exactly what cedent submissions represent for Catastrophe Modelers. Explore this perspective in AI’s Untapped Goldmine: Automating Data Entry.

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

Doc Chat is not generic OCR wrapped in a UI. It’s insurance-native, built around the realities of cedent submissions and the demands of catastrophe modeling. Several differentiators matter to Catastrophe Modelers in Reinsurance and Property & Homeowners:

Volume handling: Ingest entire submission packages—thousands of pages across SOVs, Location Schedules, Appraisal Reports, and emails—in one go.
Complexity and inference: Find and normalize the hidden facts inside messy tables and narrative engineering text, surfacing secondary modifiers and COPE details that change modeled loss.
The Nomad Process: We train Doc Chat on your exposure schema, your cedent-specific rules, and your model import templates. It works your way from day one.
Real-time Q&A: Ask “Which locations lack opening protection?” or “Show all facilities within 1,000m of a water body” and get answers plus source citations.
Thorough and complete: Eliminate blind spots with exhaustive cross-referencing across all attached documents, ensuring nothing slips through.
White glove service, fast implementation: We deliver a production-ready solution in 1–2 weeks, partner closely with your modeling team, and evolve the system with your feedback.

With Doc Chat, you’re not buying a tool—you’re gaining a partner that understands the stakes of getting exposure data right. Learn how it works for insurance teams on our product page: Doc Chat for Insurance.

Security, Governance, and Explainability

Reinsurers handle sensitive cedent information. Doc Chat is designed for enterprise security and compliance. We maintain robust controls and provide document-level traceability for every extracted field and every inference. Page-level citations ensure your Catastrophe Modelers can defend each data point—from TIV splits to roof modifiers—during internal reviews, regulatory checks, or retro pitch meetings. This combination of security and transparency underpins trust in AI-augmented workflows across the insurance value chain.

Event Response, Renewal Season, and Portfolio Analytics

Doc Chat benefits Catastrophe Modelers beyond day-to-day ingestion:

Event response: Quickly re-run portfolios as new information arrives from cedents. Doc Chat ingests updates, reconciles deltas, and regenerates model-ready inputs swiftly.
Renewal readiness: Accelerate pre-renewal analysis by standardizing every cedent’s data into a uniform template. Improve data quality requests with precise, cited questions.
Portfolio segmentation: Produce peril-specific slices and concentration analyses with a few questions—no manual rework needed.

The result is a modeling team that can answer strategic questions on demand, backed by a defensible chain of evidence.

Where the Rubber Meets the Road: Concrete Examples

Consider a reinsurance Catastrophe Modeler handed a submission with:

An XLSX SOV with merged headers and inconsistent column names.
A 120-page Appraisal Report with narrative references to roof replacements, opening protection, and elevation.
A PDF Location Schedule exported from a legacy system with address anomalies and partial coordinates.
An email chain clarifying currency splits and BI methodology.

With Doc Chat, the workflow looks like this:

Drag-and-drop the entire package into Doc Chat.
Doc Chat classifies each file, extracts SOV line items, normalizes headers, and splits multi-building records.
It geocodes addresses, converts any DMS coordinates, and flags confidence tiers to your standards.
It mines the Appraisal Report for COPE and secondary modifiers, linking each attribute to the page where it was found.
It reconciles TIVs and currency conversions against the email clarifications.
You ask, “Export our RMS wind import template” and get a ready-to-load CSV, plus a validation log.

No macros. No copy/paste. No hunting through PDFs. Just model-ready inputs with explainability.

Answering the Search: How Doc Chat Solves Your Exact Queries

“extract SOV data for cat modeling AI”

Doc Chat reads SOVs across formats, extracts building/contents/BI by location, standardizes fields, and prepares peril-specific exports, complete with quality checks and reconciliation against cedent totals.

“automated location schedule ingestion”

Whether embedded tables in emails or awkward exports from legacy tools, Doc Chat ingests, normalizes, deduplicates, and maps every record to your schema, then validates required fields for model readiness.

“AI to pull property values from reinsurance cedent submissions”

Pulling values is only the start. Doc Chat maps values to coverage components, enforces units and currencies, and checks sums—so your Catastrophe Modelers trust the downstream modeling results.

“process property risk documents for cat model input”

From Property Risk Submission Packages and Appraisal Reports to Location Schedules and policy binders, Doc Chat produces clean, peril-specific files aligned to your import templates, with full citations for audit.

Why This Matters Now

Catastrophe risk is dynamic—exposure growth in hazard-prone geographies, increased secondary perils, code evolution, and supply-chain sensitivities. Data latency and inconsistency undermine the value of your modeling and underwriting decisions. The reinsurers and Property & Homeowners carriers winning in today’s market are those who compress time-to-model, standardize data quality, and push expert inference into their everyday workflows.

Doc Chat gives Catastrophe Modelers a structural advantage: a consistent, high-fidelity exposure pipeline. It’s the kind of investment that accelerates speed to quote, improves hit ratios, reduces leakage from data errors, and supports more defensible portfolio views.

Implementation in 1–2 Weeks, With White Glove Service

Nomad Data’s implementation model is designed for quick, low-friction deployment. We set up Doc Chat to mirror your exposure templates, cedent-specific rules, and peril-specific validation. Your Catastrophe Modelers can begin drag-and-drop processing immediately, while IT-led integrations to your modeling and data lakes proceed in parallel.

What to expect:

Discovery: We review your current templates, cedent examples, and validation rules for Reinsurance and Property & Homeowners.
Configuration: We train Doc Chat on your schema, dictionaries, and COPE priorities. We also align export formats to your cat model import requirements.
Pilot: Your team runs real submissions, compares results, and iterates on rules. Page-level citations make validation fast and transparent.
Rollout: We provide training, usage guidelines, and support. Most teams are fully productive within 1–2 weeks.

This “partner-first” model is core to how we operate. As we argued in our broader research, successful document automation requires blending domain expertise with AI engineering and careful interviewing of stakeholders to capture the rules that have never been written down. Read more here: Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

From Bottleneck to Advantage: A New Operating Model for Cat Teams

Shifting document processing from manual to AI-driven transforms what Catastrophe Modelers do every day. Teams reallocate time from wrangling rows and cells to stress testing, scenario analysis, and treaty optimization. They become strategic advisors to underwriters rather than document processors. And because Doc Chat keeps a transparent audit trail, model assumptions are easier to defend to stakeholders across underwriting, risk, and finance.

This is the real promise of AI in insurance—not replacing expert judgment, but removing the drudge work that prevents experts from applying their judgment at scale. For additional context on how AI is reshaping core insurance workflows, including books-of-business risk review and reinsurer due diligence, explore AI for Insurance: Real-World AI Use Cases Driving Transformation.

Get Started

If you’re a Catastrophe Modeler in Reinsurance or Property & Homeowners and you’re ready to standardize cedent data, speed up time-to-model, and improve accuracy, it’s time to see Doc Chat in action. Load your toughest submission—scanned Appraisal Reports, messy Location Schedules, multi-currency SOVs—and watch the system produce modeling-ready outputs with full citations.

See how fast you can transform “process property risk documents for cat model input” from a pain point into a competitive advantage. Visit Doc Chat for Insurance to learn more or request a tailored demo.