Streamlining Cat Model Inputs: Extracting Risk Exposures from Cedent Documents with AI – Exposure Analyst (Reinsurance, Property & Homeowners)

Reinsurance exposure teams live and die by speed and accuracy. When catastrophe season looms, the difference between a timely portfolio roll-up and a missed treaty deadline often comes down to how fast an Exposure Analyst can convert messy cedent submissions into clean, model-ready data. The challenge: Statements of Values (SOVs), Location Schedules, Appraisal Reports, and full Property Risk Submission Packages arrive in every possible format—Excel, PDFs, scans, email attachments—with inconsistent fields, missing COPE details, and ambiguous valuation bases. Meanwhile, cat modelers need accurate, normalized inputs for RMS, Verisk Extreme Event Solutions (formerly AIR), or CoreLogic in hours, not days.

Doc Chat by Nomad Data removes this bottleneck. Purpose-built, AI-powered document agents ingest entire submission packets, extract SOV data for cat modeling, normalize location attributes, infer COPE and secondary modifiers from supporting documents, and deliver model-ready files for immediate import—complete with page-level citations back to source materials. Exposure Analysts can ask natural-language questions like, “Which Florida locations within 5 miles of the coast have TIV > $10M and roof age > 20 years?” and get instant, defensible answers across thousands of pages.

The Reinsurance Exposure Analyst’s Reality in Property & Homeowners

In Reinsurance across Property & Homeowners, the Exposure Analyst must reconcile cedent data into a common standard under extreme time pressure. Broker submissions mix Statement of Values (SOV) spreadsheets with Location Schedules in inconsistent schemas; Appraisal Reports are embedded as PDFs with COPE and roof details; and Property Risk Submission Packages may bundle policy forms, slips, binders, coverage schedules, and inspection narratives. Key modeling attributes are scattered: construction class (ISO 1–6), occupancy, year built, stories, roof type, roof age, roof deck attachment, opening protection, fire protection (sprinklers, alarms), distance to hydrant/station, distance to coast, flood zone, wildfire defensible space, defensible clearing, and more. Currency conversions (USD/EUR/GBP), TIV splits (building, contents, BI), valuation basis (RCV vs ACV), and deductible structures further complicate the picture.

Cat modeling platforms (RMS, Verisk/AIR, CoreLogic) demand rigor. Missing or mis-mapped fields can distort AALs, EP curves, and secondary modifier impacts. Portfolio rollups require de-duplication across treaties and years, and governance demands audit trails for each assumption used. In short: it’s not simply data entry; it’s forensic data engineering in a high-stakes, time-bound environment where time-to-model drives quote turn time, pricing discipline, and underwriter confidence.

How the Process Is Handled Manually Today

Most Exposure Analysts still perform a painstaking, manual assembly line to process property risk documents for cat model input:

Collect & triage disparate files from brokers and cedents: Excel SOVs with merged cells, PDF location schedules, scanned appraisals, emails with embedded tables, and long-form COPE survey reports.
Convert & combine PDFs to spreadsheets; copy/paste or retype fields; split multi-location rows; standardize headers (e.g., OCCUP vs Occupancy; Const vs Construction); and normalize TIV splits.
Map to model schemas (RMS EDM/UDM, Verisk Touchstone formats): assign ISO construction (1–6), align occupancy codes, transform address fields, and conform deductibles/limits to import rules.
Geocode & validate addresses: standardize street/city/ZIP/postal code, resolve PO Boxes, infer county/CRESTA, and correct lat/long anomalies (e.g., points plotted in the ocean or wrong country).
Extract COPE & secondary modifiers by reading Appraisal Reports and inspection notes for roof shape, deck attachment, opening protection, sprinkler types, alarm systems, and parapets—often buried deep in narratives.
Quality control: deduplicate across files using fuzzy matching on address + TIV + occupancy; reconcile currency and valuation basis; and track assumptions for unknown fields.
Finalize import files: populate model templates, fill unknowns with defaults, and document data lineage manually in spreadsheets or emails for audit and compliance.

This manual workflow burns days per submission, invites inconsistency across analysts, and often leads to compromises: partial ingestion, default-heavy imports, or missed secondary modifiers that materially impact loss results. Even highly skilled teams struggle to hold every detail in mind across hundreds of locations and thousands of pages.

Where Manual Review Breaks Down

Manual processes aren’t just slow—they’re structurally mismatched to the inference-heavy nature of cedent documents. As Nomad Data outlines in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, document intelligence requires reading like a domain expert and synthesizing conclusions from scattered clues, not merely scraping a table. For Exposure Analysts, that means:

Inconsistent schemas across SOVs and Location Schedules make one-size-fits-all macros brittle.
Secondary modifiers are rarely in a single column; they’re described in Appraisal Reports, inspection narratives, or footnotes, requiring judgment and cross-referencing.
Geocoding & validation need context: lat/long plausibility, proximity to coastline or floodplain, or whether an address is a distribution center vs. retail.
Currency & valuation (RCV vs ACV) often change within the same packet; missing units (sq. ft. vs sq. m.) can silently distort TIV density.
Governance demands page-level provenance for every extracted field—impractical to maintain by hand at scale.

The result: backlogs, elevated loss-adjustment expenses, fatigue-driven errors, and variability between analysts—exactly the outcomes reinsurers want to avoid when pricing catastrophe risk.

How Nomad Data’s Doc Chat Automates SOV and Location Schedule Ingestion

Doc Chat transforms the exposure workflow from manual collation to end-to-end automation. It ingests entire Property Risk Submission Packages—thousands of pages at once—and produces model-ready exports, while preserving a full audit trail.

Automated Location Schedule Ingestion

Doc Chat detects, classifies, and extracts tables from SOVs and Location Schedules regardless of layout or file type. It normalizes fields (e.g., CONSTR → Construction, OCC → Occupancy), handles merged cells, and splits multi-location rows. It also identifies and resolves hidden OCR issues in scanned PDFs. With automated location schedule ingestion, analysts go from raw files to a clean, unified dataset in minutes.

extract SOV data for cat modeling AI

Beyond simple field extraction, Doc Chat applies your firm’s mapping logic to assign ISO construction class, harmonize occupancy codes, and conform valuation splits (Building/Contents/BI). It flags uncertainty (e.g., ambiguous “Mixed” occupancy) and can prompt for rules or apply playbook defaults. Every value is linked to source pages for auditability.

AI to pull property values from reinsurance cedent submissions

Valuations vary by cedent. Doc Chat standardizes currency, interprets valuation basis (RCV vs ACV), and checks TIV reasonability using area, construction, and occupancy context. When values conflict across documents, it surfaces the discrepancy with citations so an Exposure Analyst can confirm the correct figure quickly.

process property risk documents for cat model input

Doc Chat aligns extracted attributes to RMS/AIR/CoreLogic import schemas, populates secondary modifiers from Appraisal Reports and surveys, and produces ready-to-load templates (e.g., RMS EDM/UDM, Verisk Touchstone). It can auto-generate completeness scoring by peril and modifier set, so your team knows exactly which locations will be defaulted in the model—and why.

What Doc Chat Extracts and Computes for Reinsurance Property Modeling

Nomad Data trains Doc Chat on your modeling playbooks to capture the nuances your team cares about. Across SOVs, Location Schedules, Appraisal Reports, and full Property Risk Submission Packages, Doc Chat pulls and computes:

Core location attributes: standardized address, city, state/province, ZIP/postal code, country, geocoded lat/long with plausibility checks, county/FIPS, CRESTA.
Construction & occupancy: ISO class, subtype, year built, stories, square footage, roof type, roof material, roof age, roof geometry, roof deck attachment, parapets.
Protection: sprinkler presence/type/coverage, fire/central station alarms, hydrant distance, nearest fire station distance, fire district, ISO PPC where available.
Exposure metrics: distance to coast/shoreline, elevation, flood zone (FEMA/other), distance to major water body, wildfire defensible space, WUI context.
Valuations: TIV by Building/Contents/BI, currency identification and conversion, valuation basis (RCV/ACV), deductible structure, site-level sublimits and policy limits.
Secondary modifiers: opening protection, roof anchorage, roof deck attachment level, roof cover class, terrain roughness, building code era, occupancy density, and more—mapped to RMS/AIR fields.
Data quality controls: duplicate detection across packets and time periods; conflict resolution (e.g., two roof ages cited); default attribution when required; and a location-level completeness score.

The output is a clean, consistent dataset aligned to your cat modeling environment, with confidence indicators and clickable citations back to every source page.

Real-Time Q&A That Thinks Like an Exposure Analyst

Doc Chat enables Exposure Analysts to interrogate massive submission sets in seconds. Ask questions across the entire corpus and get instant, defensible answers with citations:

“Show all warehouses in Texas with Building TIV > $15M, roof age > 20 years, and no sprinklers. Provide distance to hydrant and station.”
“List locations within 1 mile of the Atlantic coastline with wood construction (ISO 1) and opening protection = unknown. Prioritize TIV > $5M.”
“Export a Touchstone-ready file for all locations with completed roof secondary modifiers; flag those needing defaults and summarize impact on AAL.”
“Which Florida properties have conflicting roof geometry between the SOV and the appraisal?”
“Identify likely duplicates across the 2022 and 2023 SOVs for the ABC cedent based on address + TIV + occupancy.”

These queries replace hours of manual hunting. Analysts move straight to decisions and model sensitivity tests with traceable, explainable data.

Integration and Model-Ready Outputs

Doc Chat delivers flexible outputs tailored to reinsurance workflows:

Templates: RMS EDM/UDM, Verisk Touchstone, CoreLogic import files, Sequel Impact/Sequel RMS-compatible CSVs.
APIs: deliver structured JSON/CSV to your exposure database, data lake, or pricing platform; push flagged records for adjudication.
Dashboards: completeness scoring by peril and modifier; location heat maps for outliers (e.g., implausible lat/long, unusual TIV density).
Audit packs: per-location PDFs with extracted fields, assumptions, and page-level citations for internal model governance and external reviews.

Because Doc Chat can ingest entire books of business, reinsurers can run portfolio-wide quality checks post-bind or pre-renewal to ensure inputs remain aligned with evolving modeling guidance.

The Business Impact: Time Savings, Cost Reduction, Accuracy Improvements

Reinsurance organizations see measurable gains from automating automated location schedule ingestion and SOV extraction:

Time-to-model reduction: Days of manual collation compress into minutes. That means faster treaty pricing, quicker facultative quotes, and the ability to run multiple sensitivity scenarios before deadlines.
Cost reduction: High-cost analyst hours shift from data wrangling to risk analysis. Overtime and external vendor fees (for rush data cleanup) drop materially.
Accuracy improvements: Consistent mapping, fewer defaults, and fuller secondary modifier capture improve modeled loss fidelity and portfolio understanding.
Scalability under surge: Seasonal spikes or event-driven surges no longer force hiring or backlog triage. Doc Chat scales to ingest thousands of pages per minute.
Governance & defensibility: Page-level citations and data lineage reduce internal friction, speed audits, and strengthen regulatory posture.

As we describe in AI’s Untapped Goldmine: Automating Data Entry, the biggest ROI often hides in repetitive document-to-dataset workflows. For Exposure Analysts, this means converting cedent SOV chaos into a competitive advantage—consistent, fast, and repeatable.

Why Nomad Data Is the Best Partner for Exposure Analysts

Doc Chat isn’t a generic OCR tool; it’s a suite of AI agents designed for insurance documents and trained on your playbooks, documents, and standards. Here’s why reinsurance teams choose Nomad Data for extracting SOV data for cat modeling and building repeatable modeling pipelines:

Volume: Ingest entire submission packages—thousands of pages at a time—without adding headcount. Reviews move from days to minutes.
Complexity: Secondary modifiers, exclusions, and nuanced COPE details hide in dense, inconsistent documents. Doc Chat surfaces them with traceable citations.
The Nomad Process: We capture your unwritten rules and encode them—just as described in Beyond Extraction—so Doc Chat mirrors how your best analysts think.
Real-time Q&A: Ask portfolio-wide questions and receive instant, source-linked answers across all cedent files.
White glove service: Our team performs configuration, tuning, and iteration with you. No DIY guesswork—just results.
Fast implementation: Typical deployments complete in 1–2 weeks, with immediate drag-and-drop usability and optional API integration soon after.
Security & trust: SOC 2 Type 2 controls, document-level traceability, and no training on your data by default.

For a broader view on how insurance leaders accelerate complex reviews, see our customer story: Reimagining Insurance Claims Management: Great American Insurance Group Accelerates Complex Claims with AI. The same speed, explainability, and auditability demanded by claims apply to reinsurance exposure management.

High-Intent Use Cases Exposure Analysts Are Searching For

We hear these needs daily, and Doc Chat is built to satisfy them:

extract SOV data for cat modeling AI: Convert heterogeneous SOVs into a unified, model-ready dataset with secondary modifiers and confidence scoring.
automated location schedule ingestion: Normalize fields, split multi-location rows, and standardize addresses at scale—no macros or brittle scripts needed.
AI to pull property values from reinsurance cedent submissions: Resolve currency, valuation basis, and conflicting TIVs with source-linked transparency.
process property risk documents for cat model input: End-to-end automation from ingestion to RMS/AIR/CoreLogic templates, including audit packs and governance trail.

A Day-in-the-Life With Doc Chat: From Submission to Model in Minutes

Here’s how an Exposure Analyst can handle a new cedent packet using Doc Chat:

Drag-and-drop intake: Upload the Statement of Values (SOV), any Location Schedules, Appraisal Reports, and the full Property Risk Submission Package.
Automated extraction: Doc Chat identifies tables, unifies columns, geocodes, and applies your mapping rules (e.g., ISO construction class, occupancy normalization).
Secondary modifiers: The system mines appraisals and inspection narratives to populate roof, opening protection, and other modifiers; it flags unknowns and provides citations.
Quality controls: Dedupe across historic submissions, surface TIV conflicts, and score completeness by peril.
Real-time Q&A: Ask targeted questions to validate outliers or identify high-impact data gaps before modeling.
Export: Generate RMS/Verisk import files and an audit pack; push structured data to your exposure database via API.

What formerly required days of spreadsheet work is now an interactive, auditable, and dramatically faster process.

Advanced Inference: From Unstructured Appraisals to Secondary Modifiers

Secondary modifiers are often the difference between coarse and confidence-inspiring model results. Doc Chat is trained to recognize and infer these details from unstructured sources:

Roof deck attachment level and roof cover class from appraisal narratives.
Opening protection (shutters, impact glass) from inspection photos and text.
Sprinkler coverage type, density, and protected areas from fire protection reports.
Distance to coast/hydrant/station from geocoding plus authoritative datasets.
Building code era and retrofit indicators from permits or appraisal notes.

As highlighted in our article Reimagining Claims Processing Through AI Transformation, AI excels when it pairs structured extraction with inference. For exposure teams, this means better modifier completeness with verifiable provenance.

Operational Governance and Model Defensibility

Reinsurers must defend model inputs to auditors, regulators, and internal committees. Doc Chat builds governance into the workflow:

Page-level citations for every field value and modifier, enabling rapid challenge/response.
Change logs that record every normalization or default applied, with user approvals when needed.
Playbook codification to ensure consistent application of rules across analysts, regions, and cedents.
Exportable audit packs to accompany model runs and portfolio rollups.

In short, you gain speed without sacrificing defensibility.

Implementation: White Glove and 1–2 Weeks to Go Live

Nomad Data’s implementation follows a pragmatic, service-led approach:

Discovery & playbook capture: We interview your Exposure Analysts to encode mapping rules, model templates, and QA steps.
Pilot with real files: Run Doc Chat on recent cedent packets; compare outputs to prior models; iterate until exact-fit accuracy.
Go live: Enable drag-and-drop usage in under two weeks; add API integration to your exposure database or modeling platform as needed.

The result is a tailored solution that mirrors how your best analysts work—at machine speed.

Frequently Asked Questions for Exposure Analysts

Q: Can Doc Chat handle scanned PDFs and images?
A: Yes. It includes robust OCR and layout detection to recover tables, footnotes, and embedded narratives from scans.

Q: How does Doc Chat validate geocodes?
A: It applies hierarchical parsing, cross-checks city/ZIP/county, and runs plausibility checks (e.g., coastal proximity). It flags anomalies for review.

Q: What about currency and valuation basis inconsistencies?
A: Doc Chat recognizes currency symbols/codes and normalizes to your base currency. It also detects RCV vs ACV references and surfaces conflicts with citations.

Q: Can it deduplicate across years or multiple cedent submissions?
A: Yes. It uses configurable fuzzy matching (address + occupancy + TIV + other signals) and highlights likely duplicates for fast adjudication.

Q: How do we trust the extracted values?
A: Every field is citation-linked to its source page. You can click through to verify instantly—no manual hunting.

Q: Is our data used to train models?
A: No, not by default. Nomad Data is SOC 2 Type 2 compliant, and data usage is governed by your controls and preferences.

Measuring Success: The Metrics That Matter

Exposure leaders typically benchmark Doc Chat against three KPIs:

Time-to-model: End-to-end processing time from files received to model import ready.
Modifier completeness: Percent of locations with key secondary modifiers populated without defaults.
Audit turnaround: Time required to answer input challenges (with citations) during reviews.

Improvements in these metrics compound: faster modeling unlocks more sensitivity testing and better pricing discipline, while improved completeness tightens AALs and EP curves. Reduced audit friction frees up analysts for higher-value analysis.

From Bottleneck to Advantage

In reinsurance, document chaos is a given—but it doesn’t have to be a bottleneck. With Doc Chat, Exposure Analysts convert SOVs, Location Schedules, Appraisal Reports, and full Property Risk Submission Packages into accurate, model-ready inputs in minutes. You gain speed, scale, and defensibility, while your teams focus on risk—not retyping.

If you’re exploring AI to pull property values from reinsurance cedent submissions or looking to process property risk documents for cat model input without adding headcount, Doc Chat delivers. Start with a single treaty, measure time-to-model, and scale across your portfolio.

Ready to see it on your files? Visit Doc Chat for Insurance and request a demonstration.