Streamlining Cat Model Inputs: Extracting Risk Exposures from Cedent Documents with AI for Property Treaty Underwriters

Property treaty underwriters live and die by time-to-model. When cedents send sprawling Statement of Values files, multi-tab location schedules, appraisal reports, and ad-hoc property risk submission packages, the clock starts. The faster you can normalize, validate, and enrich those exposures into RMS or AIR-ready templates, the sooner you can price the layer and win the deal. The challenge is that every cedent structures documents differently and key COPE details hide across PDFs, spreadsheets, emails, and attachments. That has traditionally meant hours or days of manual data wrangling before a catastrophe modeler can even push the first run.

Nomad Data's Doc Chat changes that. It is a suite of purpose-built, insurance-grade AI agents that ingest entire cedent submissions at once, extract SOV data for cat modeling, normalize occupancy and construction, validate valuations against appraisals, and output a clean, model-ready file. Instead of re-keying and crosswalking by hand, teams ask plain-language questions such as 'list locations missing lat/long' or 'summarize TIV by state and occupancy' and get instant, page-cited answers across thousands of pages. With Doc Chat for Insurance, Property Treaty Underwriters compress days of document work into minutes and reduce risk through consistent, defensible extraction.

The property reinsurance nuance: why ingestion is the bottleneck for the Property Treaty Underwriter

In reinsurance across Property & Homeowners, cedents deliver exposure data in wildly variable formats. One cedent might send a clean CSV SOV with lat/long and COPE secondary modifiers; another will email a 37-tab Excel plus scanned appraisal PDFs and a freeform cover letter. As a Property Treaty Underwriter, you still need the same things, reliably and quickly:

• Accurate location data: street address, city, state, ZIP, lat/long, county, FIPS, distance to coast, fire station, and hydrant
• Valuation and limits: replacement cost value, market value when relevant, TIV, PD vs BI split, deducible structures, and sublimits
• COPE and secondary modifiers: construction class, occupancy, protection, exposure, roof age and geometry, number of stories, year built/renovated, sprinkler and alarm status, WUI classification, flood zone, defensible space
• Perils alignment: wind/hail, flood, earthquake, wildfire, winter storm; peril-specific fields for RMS RiskLink/RiskModeler or Verisk AIR Touchstone
• Version control: most-current SOV versus legacy versions hidden in email threads or archived links

Yet cedent submissions rarely conform to one schema. The Property Treaty Underwriter is expected to evaluate portfolio-level characteristics quickly for layers, facultative carve-outs, and proportional placements. You must also satisfy increasingly strict model governance and auditor expectations around data lineage: where did this occupancy code come from; which appraisal report supports that change in TIV; why did geocode accuracy change from ZIP centroid to rooftop?

Complicating matters further, the documents themselves are heterogeneous:

• Statement of Values (SOV) in XLS, CSV, or embedded tables in PDFs
• Location Schedules with nested building-level tabs and inconsistent column naming
• Appraisal Reports with property condition, valuation rationale, and photographs
• Property Risk Submission Packages containing cover letters, engineering surveys, loss control reports, and policy excerpts
• Exposure bordereaux sent periodically, each with its own formatting quirks
• Loss run reports that must be tied back to specific locations or accounts

Catastrophe modelers and exposure analysts can process all of this, but the manual effort throttles the underwriting cycle. In peak cat season or event response, throughput collapses and promising treaties go stale while teams wrestle with formatting.

How manual ingestion works today (and why it is slow, expensive, and error-prone)

Most Property Treaty Underwriters and Exposure teams still rely on spreadsheet macros, manual copy-paste, shared drives, and institutional memory to get cedent data into cat model input templates.

• Collect documents from email threads, portals, and SFTP, then save disparate versions to shared folders
• Open each SOV tab to identify column semantics, e.g., 'Occ' vs 'Occupancy', 'Const' vs 'ConstrClass'
• Manually crosswalk cedent-specific codes to RMS or AIR-required enumerations
• Re-key or VLOOKUP missing fields from Appraisal Reports or Risk Control PDFs
• Standardize addresses and batch geocode; chase down ambiguous or P.O. Box entries
• Reconcile valuation differences between SOV and appraisals; adjust for BI vs PD splits
• De-duplicate locations across multiple SOV versions or sub-schedules; decide which is authoritative
• Write email back to cedent asking for missing fields, wait for responses, then repeat merges
• Load the result into RMS or AIR; investigate errors; iterate on template formatting

Some teams build clever macros and templates, but those brittle solutions break each time a cedent adds a new column or embeds a table inside a PDF. Human fatigue sets in, and omissions happen: missing roof geometry for wildfire modeling, incorrect occupancy mapping for industrial risks, or geocoding at ZIP centroid instead of rooftop for coastal exposures. Those small misses propagate into model outputs, undermining confidence and pricing precision. The opportunity cost is just as painful: a Property Treaty Underwriter and Catastrophe Modeler spend precious hours on data entry instead of strategy, negotiation, and portfolio steering.

Automating the pipeline with Doc Chat: from messy cedent files to model-ready exposures

Doc Chat by Nomad Data is designed for exactly this problem: automated location schedule ingestion at scale, even when the 'schedule' is actually a patched-together bundle of spreadsheets and PDFs. It ingests entire submission packages, classifies document types, and extracts structured fields with page-level citations. Then it normalizes, validates, and exports model-ready files tailored to your RMS or AIR schema.

How it works, end-to-end

Doc Chat executes a complete ingestion and validation loop so the Property Treaty Underwriter receives a file that is ready to model and ready to defend.

• Mass ingestion with structure awareness Doc Chat ingests SOVs, location schedules, appraisal PDFs, engineering surveys, and submission emails all at once. It reads embedded tables, multi-tab Excels, and scanned attachments via OCR and advanced vision models.
• Document classification and field mapping The agent determines whether a file is an SOV, appraisal, or survey, and selects the right playbook. It maps cedent-specific headers to your standard schema, handles construction and occupancy enumerations, and auto-detects BI/PD fields, year built, stories, and protection attributes.
• Geocoding and enrichment Doc Chat standardizes addresses, converts to rooftop lat/long where possible, and enriches with county, FIPS, distance to coast, ISO fire class proxies, and peril-specific indicators such as WUI. If your workflow prefers your internal or third-party geocoder, Doc Chat integrates via API.
• Cross-document validation It reconciles valuation conflicts across SOV and appraisal reports, flags missing fields, and highlights inconsistencies such as construction class mismatch between survey text and SOV codes. It detects duplicates and version drift across multiple SOVs.
• Secondary modifier extraction The agent pulls roof geometry, roof age, sprinkler/alarm status, defensible space notes, flood zone tags, and other secondary modifiers that dramatically affect cat loss estimates.
• Audit-ready outputs Every extracted field includes a citation back to page and paragraph, so model governance, actuaries, and auditors can verify the source instantly. Outputs are delivered as CSV, XLSX, or JSON that align with RMS RiskLink/RiskModeler and AIR Touchstone templates.
• Real-time Q&A Ask: 'which 100 locations have the highest TIV within 1 mile of the coast' or 'list all occupancies mapped to industrial and show their original cedent values'. Doc Chat returns answers in seconds with links to the supporting source text or cells.

Use case spotlight: extract SOV data for cat modeling AI

When teams search for 'extract SOV data for cat modeling AI', they want more than simple OCR. Doc Chat combines extraction with inference. If a cedent uses a custom occupancy code, Doc Chat applies your underwriting playbook to map that code into your RMS/AIR enumerations and documents the rationale with citations. If TIV is missing for a building but appraised value is present, Doc Chat can compute or flag the gap, driving a consistent, defensible approach across treaties.

Use case spotlight: AI to pull property values from reinsurance cedent submissions

'AI to pull property values from reinsurance cedent submissions' must account for nuanced valuation logic. Doc Chat does not just copy numbers; it:

• Identifies PD versus BI splits, applies your allocation rules, and reports subtotals by peril zone
• Recognizes currency markers and performs conversions when needed
• Pulls appraised replacement cost from Appraisal Reports to validate SOV TIV and flags material variance
• Tracks valuation effective dates so modelers can understand staleness and trend, and flags records outside your acceptable age window

Use case spotlight: process property risk documents for cat model input

'Process property risk documents for cat model input' implies multi-document orchestration. Doc Chat stitches together Statement of Values, Location Schedules, Appraisal Reports, Property Risk Submission Packages, and even loss run reports and exposure bordereaux. It creates a single, deduplicated exposure dataset with complete COPE fields and an audit trail that can be handed to actuaries, model governance, and reinsurer partners with confidence.

What this means for the Property Treaty Underwriter: measurable business impact

When the ingestion bottleneck disappears, underwriting speed and quality change overnight.

• Time-to-model collapses Reviews that consumed hours or days now run in minutes. Doc Chat ingests thousands of pages per minute and can summarize and normalize multi-document submissions near-instantly. One of Nomad Data's core capabilities, highlighted in our coverage of medical file throughput, processes on the order of hundreds of thousands of pages per minute while maintaining page-level fidelity.
• Higher underwriting capacity With manual data entry removed, a Property Treaty Underwriter handles more treaties without adding headcount. That means more quotes issued before deadlines and higher hit ratios.
• Accuracy and consistency improve Doc Chat does not tire at page 1,500. It applies the same mapping rules every time, cutting leakage from misclassified construction or missing secondary modifiers.
• Auditability enhances model governance Page-level citations address internal model governance and external auditor questions about data lineage. Every material field can be verified in seconds.
• Lower loss-adjustment and operating expense Less overtime and fewer manual touchpoints reduce cost. As outlined in our perspective on data-entry automation, intelligent document processing routinely yields rapid ROI, with organizations often realizing triple-digit returns in year one.

The intangible benefits matter too. Teams experience less burnout from repetitive re-keying and more time for judgment-driven work: selecting scenarios, calibrating vulnerability settings, negotiating terms, and advising cedents on data quality improvements that reduce basis risk for both parties.

From manual to automated: where Doc Chat specifically outperforms spreadsheets and macros

Spreadsheets are superb for known, stable formats. Cedent documents are neither. Doc Chat turns a messy environment into a standardized, repeatable process that the whole underwriting organization can trust.

Key differentiators:

• The Nomad Process We train Doc Chat on your playbooks, preferred occupancy and construction mappings, RMS/AIR templates, and governance thresholds. This institutionalizes your best underwriters' discretion so outputs are consistent across desks.
• Surfaces every reference Doc Chat searches across all pages and tabs to extract every reference to coverage, liability, and damages-like values in appraisal narratives that impact valuation confidence.
• Real-time Q&A Underwriters and exposure analysts can interrogate the file with questions like 'show all risks in FEMA Special Flood Hazard Areas with TIV over a set threshold' or 'which locations lack sprinklers but show high TIV in wildfire-prone counties'.
• Scale on demand Spikes in submissions near renewals or following catastrophe events no longer create backlogs. Doc Chat scales instantly without added headcount.

Why Nomad Data is the best partner for reinsurance exposure ingestion

Many tools promise extraction. Few deliver inference, standardization, and underwriting-grade auditability at the speed and scale demanded by reinsurance. Nomad Data's Doc Chat stands out:

• White-glove implementation We do the heavy lifting. Our team interviews your Property Treaty Underwriters, Catastrophe Modelers, and governance leaders, then encodes your rules so Doc Chat mirrors your process.
• Fast time to value Typical implementations complete in 1–2 weeks for initial playbooks and RMS/AIR schemas. Drag-and-drop usage can start on day one, with deeper API integration following after quick wins.
• Security and compliance Nomad operates with rigorous controls and page-level traceability. We meet the standards of carriers and reinsurers who require defensible audit trails and robust data governance.
• Results, not toolkits You are not buying a generic platform and hoping it fits. We deliver a customized solution that outputs the exact fields you need to run your models and bind treaties faster.
• A partner in AI We co-create with you and evolve Doc Chat to new cedent formats, new perils, and new regulatory expectations as they emerge.

For a deeper look at why document work requires inference, not just extraction, see our perspective in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs. If you are exploring ROI, our view on the scale of data-entry automation is captured in AI's Untapped Goldmine: Automating Data Entry.

What does automated location schedule ingestion look like in practice

Consider a typical mid-season cedent submission:

• SOV in Excel with five tabs and inconsistent column naming across tabs
• PDF appraisal reports for 40% of the top TIV locations
• A risk engineering summary emailed as a PDF containing notes on roof geometry and protection
• Exposure bordereau for commercial lines with separate BI and PD tabs
• A cover letter with underwriting context and a list of exclusions and recent renovations

With Doc Chat, the Property Treaty Underwriter or Exposure Analyst drags the entire package into a secure workspace. In a few minutes, the agent produces:

• A consolidated, deduplicated exposure file with addresses standardized and rooftop geocodes
• Mapped occupancy and construction fields to your RMS/AIR enumerations, with any exceptions flagged
• Secondary modifiers extracted from appraisals and engineering text, tied back to citations
• A 'missing and ambiguous' report listing locations lacking year built, roof type, or sprinkler status
• Summaries of TIV distribution by state, peril zone, occupancy, and construction
• Export files ready for upload to RMS RiskLink/RiskModeler or AIR Touchstone

Then, using real-time Q&A, the underwriter asks: 'which top 25 TIV locations within 2 miles of the coast lack window protection' and 'list all buildings where appraised replacement cost differs from SOV TIV by more than 15%'. Within seconds, Doc Chat returns answers and links to the exact appraisal page or SOV row where the discrepancy originates.

Answers to high-intent search questions, tailored for reinsurance

How do I extract SOV data for cat modeling AI without re-keying everything

Use a purpose-built agent that reads the cedent's SOVs, recognizes header variability, applies your mapping rules, and validates with cross-document checks. Doc Chat goes further by attaching a citation to every extracted value so auditors can confirm the source in seconds.

What does automated location schedule ingestion need to handle for Property & Homeowners

The agent must parse multi-tab Excels and PDF tables; normalize addresses and geocodes; map construction and occupancy; deduplicate locations; reconcile valuations with appraisals; extract secondary modifiers; and output RMS/AIR-ready files. Doc Chat was engineered for exactly this problem profile.

Can AI pull property values from reinsurance cedent submissions and preserve audit trails

Yes. Doc Chat captures PD/BI splits, valuation effective dates, and appraised replacement cost references, and attaches citations to every number it extracts. That means you can defend the data lineage during model governance reviews.

How do I process property risk documents for cat model input when half the secondary modifiers live in narrative PDFs

Doc Chat composes the picture from SOV rows plus narrative signals in appraisal and engineering PDFs. It extracts roof geometry, protection systems, and wildfire defensible space references, then records the exact page and paragraph where each value came from.

Data quality checks and model governance built in

Auditors and model governance leaders increasingly demand traceable, consistent processes. Doc Chat standardizes QA with checks such as:

• Outlier detection for TIV by occupancy within geographies
• Missing mandatory field detection by peril template
• Construction class and occupancy cross-consistency checks
• Geocode precision thresholds with rooftop prioritization and explicit fallbacks
• Currency detection and conversions when cedent files contain mixed currencies
• Version control logs to ensure the latest SOV supersedes outdated files

The result is a fully auditable pipeline where the Property Treaty Underwriter can show not only the final model input but also how each field was derived and from which cedent source.

Implementation in 1–2 weeks and workflows that match your stack

Getting started is straightforward:

• Discovery Nomad's white-glove team reviews your current RMS and AIR templates, mapping tables, and governance criteria. We also analyze representative cedent submissions to identify variability patterns.
• Playbook setup We configure Doc Chat with your rules and preferred enumerations. Where you have existing macros or mapping tables, we incorporate them so the transition is smooth.
• Pilot and calibration You drag and drop real cedent packages into Doc Chat and compare results with your last modeled file. We calibrate edge cases quickly.
• Integrate Once trust is established, we connect via API, SFTP, or your document management system so submissions flow automatically and outputs land where your modelers need them.

Underwriters can begin ad hoc usage on day one, and deeper integrations typically complete in as little as two weeks. That fast path to value matters in renewal season.

Security, scale, and reliability for insurance-grade document processing

Reinsurers operate under strict data governance and confidentiality obligations. Doc Chat is built for enterprise insurance environments: document-level traceability, role-based access, and a transparent audit trail for every answer. As highlighted in our client stories, Doc Chat links each result to the source page, enabling instant verification by compliance, legal, or reinsurer partners.

On scale, Doc Chat ingests entire claim or submission files—thousands of pages at a time—without adding headcount. Reviews shift from days to minutes, a capability we documented in our client case work where large, complex files were processed in seconds with consistent accuracy across the entire document set. For end-to-end performance, Doc Chat removes triage bottlenecks, maintains consistent extractions, and handles surge volumes during renewals or event-driven spikes.

Metrics that matter to the Property Treaty Underwriter

Nomad clients typically track:

• Cycle time Time from cedent submission to first model run
• Coverage of secondary modifiers Percentage of locations with required peril-specific fields
• Error and rework rate Model load errors and number of cedent clarifications requested
• Win rate and quote volume Quotes issued before deadlines and subsequent bind ratios
• Audit closure time Time to produce data lineage and satisfy governance reviews

Because Doc Chat automates data entry and standardizes QA, teams often see dramatic reductions in cycle time and rework, with corresponding gains in quote volume and hit ratios. The cost side improves through fewer manual touchpoints and lower overtime, unlocking rapid ROI as documented in our data-entry automation analysis.

Frequently asked questions for reinsurance and Property & Homeowners use

Does Doc Chat handle cedent-specific occupancy and construction codes
Yes. We load your mapping tables and also learn cedent conventions over time. When the agent encounters a new code, it proposes a mapping with a confidence score and cites the evidence behind the classification.

Can it export directly into RMS or AIR templates
Yes. Outputs are delivered in your RMS RiskLink/RiskModeler or AIR Touchstone schema with the right headers, enumerations, and field ordering. If you maintain custom templates, we configure to match them exactly.

How does Doc Chat reduce back-and-forth with cedents
It produces a 'missing and ambiguous' report up front so your first response to the cedent is precise and complete. As new documents arrive, Doc Chat re-runs checks and merges updates into the exposure file.

What about model governance and auditor scrutiny
Every extracted field is source-cited. You can click from a model input directly to the page in the Appraisal Report or SOV row where that value originated, satisfying data lineage requirements.

Is this just OCR
No. As we outline in Beyond Extraction, Doc Chat performs inference across documents, applies your underwriting rules, and creates consistent outputs that reflect institutional knowledge—not just text scraping.

Putting it all together: win more treaties with cleaner, faster, safer data

Reinsurance success increasingly depends on how quickly and confidently you can turn messy cedent documents into cat model inputs. Manual ingestion was never a core competency for the Property Treaty Underwriter, yet it has become the rate-limiting step. With Doc Chat, you eliminate the bottleneck:

• Instantly extract SOV data for cat modeling AI with audit-ready citations
• Run automated location schedule ingestion that normalizes and validates across spreadsheets and PDFs
• Use AI to pull property values from reinsurance cedent submissions and reconcile against appraisals
• Process property risk documents for cat model input and export into RMS/AIR schemas in minutes

The payoff is faster time-to-model, higher underwriting capacity, fewer errors, and a defensible governance posture. Your Property Treaty Underwriters and Catastrophe Modelers get back to the work only they can do: judgment, negotiation, and portfolio optimization.

Ready to compress days of ingestion into minutes and increase your quote throughput this renewal season? Explore Doc Chat for Insurance and see how quickly your team can move from submissions to modeled insight.