Streamlining Cat Model Inputs in Reinsurance (Property & Homeowners): Extracting Risk Exposures from Cedent Documents with AI for the Property Treaty Underwriter

Property treaty underwriters live in a world where speed, accuracy, and scalability determine who wins the best submissions and who prices risk with confidence. Yet the very inputs required for catastrophe modeling—location, construction, occupancy, protection, exposure (COPE), and values—arrive in wildly inconsistent formats. Statements of Values (SOVs), Location Schedules, Appraisal Reports, and large Property Risk Submission Packages are often a mixed bag of spreadsheets, PDFs, scans, and emails. The result? Days of manual wrangling before a single cat model can run, delaying quotes and obscuring true exposure.

Nomad Data’s Doc Chat solves this bottleneck. Doc Chat is a suite of purpose-built, AI-powered agents that ingest entire cedent submissions—thousands of pages and rows at a time—and instantly structure, validate, enrich, and export the exact fields your modeling team needs. From extracting building-level TIV and occupancy to geocoding addresses and flagging missing secondary modifiers, Doc Chat reduces time-to-model from days to minutes while improving accuracy and auditability for Property & Homeowners reinsurance programs.

The reinsurance reality: why Property Treaty Underwriters struggle with cat model inputs

In reinsurance, you rarely control the quality or format of incoming data. Cedents submit what their systems can produce, and each book behaves differently. For the Property Treaty Underwriter, this variability intersects with aggressive timelines, internal accumulation constraints, and competitive pricing pressure. The nuance is not just volume—it’s the messy intersection of unstructured content, inconsistent field names, and complex treaty terms that must be translated into standardized model inputs.

In Property & Homeowners treaty deals, cedent data typically includes:

Statement of Values (SOV): Building addresses, TIV, valuation basis (RCV/ACV), year built, construction class, occupancy type, number of stories, roof type/age, square footage, protection (sprinklered, alarm), and secondary modifiers (roof shape, roof covering, opening protection).
Location Schedules: Often multiple tabs or files with premises/locational identifiers, latitude/longitude (sometimes with poor precision), and separate fields for building vs. contents vs. BI/EE values.
Appraisal Reports: Narrative descriptions and tables that clarify construction details, protection systems, and replacement cost assumptions—usually locked in PDF or scan images.
Property Risk Submission Packages: A collection of SOVs, appraisals, engineering surveys, and coverage terms, sometimes accompanied by bordereaux, loss runs, and catastrophe aggregates.

These files arrive with varying levels of completeness. Addresses might be combined into a single cell, occupancy might be free text, construction types may use proprietary codes, and coordinates can be missing or incorrect. Secondary modifiers critical to cat models—roof shape, roof deck attachment, roof covering material—are often buried in PDFs or described verbally in an appraisal. If your team cannot reliably extract and normalize this information, cat model results shift from science to guesswork, increasing volatility and pricing risk.

How Property Treaty Underwriters and exposure teams handle it manually today

Most reinsurance teams still cobble together cat model input files through a sequence of manual steps that drain calendar time and attention:

Gathering files: Downloading and consolidating all SOVs, Location Schedules, Appraisal Reports, and attachments in a submission package—often in multiple formats, versions, and naming conventions.
Mapping columns: Renaming inconsistent fields (e.g., “BLDG_VAL,” “BldgValue,” “RCV Building”) and splitting combined address lines into street/city/state/ZIP. Units and currencies are normalized, and valuation basis is reconciled.
OCR and copy/paste: Converting scanned PDFs and images to text, then copy/pasting critical COPE details from appraisals and engineering reports into a working template.
De-duplication and keying: Matching premises and building numbers across tabs and files, removing duplicates, and creating consistent location keys that the cat model will accept.
Geocoding: Running batch geocodes and reviewing failures one by one. Ambiguous or incomplete addresses are escalated back to the cedent, delaying the modeling clock.
Filling gaps and assumptions: Where data is missing, analysts impute defaults (e.g., “commercial non-combustible” or “unknown roof age”)—often inconsistently, depending on who handles the file.
Exporting to templates: Pushing the final, cleaned dataset into cat model import templates and re-running the process as corrected files come in.

This is slow, exhausting, and prone to error. As file sizes grow and secondary modifiers become more predictive, the manual approach simply cannot scale. Underwriting opportunities slip while teams wrestle spreadsheets.

How Doc Chat automates SOV, location schedule, and appraisal extraction end to end

Doc Chat is designed specifically for high-volume, high-complexity insurance documents. It ingests entire submission packages—Excel, CSV, PDF, scanned images, emails—then uses AI to read, understand, cross-check, and organize the content into the canonical schema you define for cat model input.

Key automations tailored for the Property Treaty Underwriter in Reinsurance:

Universal ingestion: Upload complete Property Risk Submission Packages, including SOVs, Location Schedules, and Appraisal Reports. Doc Chat handles mixed formats and thousands of pages without added headcount.
Smart normalization: Proprietary column names and free text become standardized fields (e.g., Construction, Occupancy, Year Built, Stories, Sprinkler, Roof Covering, TIV split by building/contents/BI).
Advanced OCR + inference: Extracts COPE details locked in appraisals and engineering surveys, even when they appear as narrative paragraphs. The system turns unstructured descriptions into model-ready fields.
Deduplication and entity resolution: Matches building IDs and premises numbers across tabs/files, resolves conflicting values, and removes duplicates with a traceable audit trail.
Geocoding and address QA: Performs address parsing and geocoding, flags low-confidence results for review, and can enrich with hazard context or distance-to-coast for model validation workflows.
Secondary modifier discovery: Surfaces roof shape/covering, opening protection, roof deck attachment, and other modifiers wherever they exist—across SOVs, appraisals, and surveys—so your cat model benefits from richer inputs.
Auto-export to model templates: Outputs to your preferred cat model import structure and to portfolio roll-up sheets for accumulation and treaty pricing.
Real-time Q&A: Ask “List all locations with TIV > $50M within 5 miles of the coast” or “Which buildings lack sprinklers?” and receive instant answers with page-level citations back to source documents.

Most importantly, Doc Chat doesn’t just “read” files; it reasons across them—cross-checking an SOV against an Appraisal Report and back to a Location Schedule to resolve conflicts. As we detail in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, the real prize is not pulling data—it’s inferring the right values from dispersed references and unwritten rules that live in your underwriting playbook.

How to extract SOV data for cat modeling AI in minutes

If you’re searching for a way to extract SOV data for cat modeling AI without rebuilding your tech stack, Doc Chat gives you immediate leverage. Drag in the cedent’s SOV and attachments; Doc Chat maps fields, identifies missing items, and produces a model-ready file. It preserves a full audit trail with page-level citations, so underwriters and exposure analysts can quickly verify any field and maintain regulatory defensibility.

Automated location schedule ingestion for treaty underwriters

When it comes to automated location schedule ingestion, Doc Chat reads inconsistent columns, splits combined address lines, normalizes coordinates, and resolves duplicate premises/building IDs. It flags low-confidence geocodes or suspect addresses (e.g., PO boxes or mismatched ZIP codes), queues them for quick review, and then completes the export to your cat model template—no tedious spreadsheet engineering required.

Using AI to pull property values from reinsurance cedent submissions

Doc Chat specializes in AI to pull property values from reinsurance cedent submissions, even when totals are split across building/contents/BI and sprinkled across multiple tabs and files. It reconciles conflicting values and highlights where TIV inconsistencies might distort cat model outputs. You get the right values in the right fields the first time, with the traceability auditors and retro partners expect.

Process property risk documents for cat model input with Doc Chat

Whether you need to process property risk documents for cat model input pre-bind or refresh a monthly bordereaux, Doc Chat standardizes the pipeline. The result is consistent, repeatable, and fast—allowing the Property Treaty Underwriter to move from submission to modeled view in minutes, not days.

What changes when your ingestion and extraction are automated

Automating SOV and appraisal extraction does more than save time. It transforms the underwriting workflow and elevates the role of the Property Treaty Underwriter:

Speed-to-quote improves dramatically. Your team can model more submissions per week and respond faster to brokers, without sacrificing diligence. Data completeness rises because secondary modifiers and protection details get systematically harvested from narratives that humans rarely have time to mine. Pricing confidence increases as geocoding precision improves and TIV inconsistencies are surfaced early. And portfolio discipline strengthens as accumulation constraints are monitored with better, more standardized inputs.

These improvements align with what we see across insurance document-heavy workflows. In our piece, AI's Untapped Goldmine: Automating Data Entry, we show why “simple” document data entry hides the biggest ROI—because consistency and scale are everything when thousands of pages and rows must be made decision-ready.

Business impact for the Property Treaty Underwriter and exposure modeling teams

Underwriting is a race against the clock—especially in property cat programs. Carriers and reinsurers that compress time-to-model gain negotiating leverage, reduce opportunity cost, and avoid expensive rework when data issues surface late in the process. Doc Chat drives measurable impact on four fronts:

1) Time savings

What previously took days of manual normalization, copy/paste from appraisals, and iterative geocoding dries up into minutes. Teams routinely report reductions from 8–20 hours of manual effort per submission to under 30 minutes of AI-enabled processing and light validation. Large multi-file submissions that once demanded a week of back-and-forth become same-day work. This mirrors the order-of-magnitude improvements reported in other complex, document-centric environments; see Reimagining Claims Processing Through AI Transformation for time compression examples in claims that map 1:1 to exposure prep.

2) Cost reduction

Manual preprocessing represents a hidden underwriting tax—analysts spend high-value time on low-value tasks. Doc Chat eliminates repetitive transformation work, reducing reliance on temporary staffing or overtime during cat season spikes. With consistent, export-ready outputs, reinsurers also trim the cost of rework, re-runs, and late-stage corrections that ripple through treaty pricing committees.

3) Accuracy and completeness

Machines don’t fatigue. Doc Chat applies the same rigor to page 1 and page 1,001, surfacing every reference to construction, occupancy, protection, and exposures that should feed a cat model. It flags inconsistencies and missing fields early, and preserves full page-level citations for auditability. Better inputs lead to more stable model results, tighter reserves, and pricing decisions you can defend to brokers, cedents, and retrocession partners.

4) Scalability without friction

In surge periods—renewals, cat seasons, portfolio roll-ups—Doc Chat scales instantly. Teams handle more submissions without adding headcount, while underwriting leaders maintain consistent output quality. This alleviates burnout, improves retention, and channels expertise toward strategic judgment rather than rote data prep.

Why Nomad Data’s Doc Chat is the best fit for Reinsurance (Property & Homeowners)

Doc Chat isn’t generic OCR dressed up as AI. It’s built for insurance, tuned for claims and underwriting documents, and deployed with white-glove service. Five differentiators matter for Property Treaty Underwriters:

Volume at speed: Doc Chat ingests entire cedent submission packages—including huge SOVs and 10,000+ page attachments—so reviews move from days to minutes.
Mastery of complexity: Exclusions, endorsements, secondary modifiers, and nuanced COPE language hide in dense policies and appraisals. Doc Chat digs them out, enabling better model inputs and fewer surprises.
The Nomad Process: We train Doc Chat on your playbooks, your canonical schema, and your model import templates. Output formats and data checks match your exposure analyst workflow out of the gate.
Real-time Q&A and citations: Ask portfolio questions across massive document sets and get instant answers with links back to the exact page in the SOV or appraisal for fast verification.
Partner, not just software: Our white glove approach, rapid iterations, and 1–2 week implementation mean value lands quickly. We evolve the solution with you as your treaty mix and accumulation strategy change.

For a deeper look at why this approach outperforms keyword-driven tools, read Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs. The heart of the problem isn’t files; it’s inference—and that’s where Doc Chat excels.

What Doc Chat extracts and validates for cat model input

Doc Chat targets the fields Property Treaty Underwriters and exposure analysts rely on every day:

Location: Full address parsing, city/state/ZIP, country, latitude/longitude, and geocoding confidence scores.
Values: TIV broken out by building, contents, and BI/EE; currency normalization; valuation basis (RCV vs. ACV) and appraisal effective dates.
Construction: Frame, joisted masonry, non-combustible, masonry non-combustible, fire resistive, and detailed descriptors captured from appraisals.
Occupancy: Retail, hospitality, habitational, industrial, healthcare, public entity, education, and nuanced sub-occupancies.
Protection: Sprinkler presence, alarm types, distance to hydrant and fire station (where stated), ISO PPC where available, and water supply notes from appraisals.
Secondary modifiers: Roof covering and shape, opening protection, roof deck attachment, roof age, roof geometry, cladding, and elevation details if documented.
Keys and hierarchies: Policy/location/building identifiers reconciled across SOV tabs and Location Schedules; duplicate detection and resolution.

Every extracted field is linked back to the source page for audit. Conflicts are highlighted for quick human review, with suggested resolutions based on confidence and frequency across documents.

Real-world underwriting workflows supercharged by Doc Chat

Pre-bind triage and time-to-quote

As soon as a Property Risk Submission Package lands, underwriters load it into Doc Chat. Within minutes, the team receives a standardized model input file plus a gap report listing missing secondary modifiers, suspect coordinates, and any TIV inconsistencies. The underwriter asks targeted follow-ups of the cedent instead of fishing through tabs, accelerating quote turnaround.

Portfolio accumulation checks

Exposure teams roll up newly standardized data into portfolio views and instantly ask Doc Chat for key concentrations—by region, occupancy, or distance-to-coast. This supports capacity allocation and retro decisions with better, faster situational awareness.

Bordereaux refresh and monthly updates

For quota share or surplus share programs, Doc Chat standardizes monthly bordereaux and appends to your accumulation database. It flags changes in TIV, occupancy drift, and address corrections that materially impact cat posture—so you can re-run models and take action early.

Due diligence on books of business

When assessing an acquisition or a new cedent’s binder, Doc Chat rapidly reads all SOVs and Appraisal Reports, creating a consolidated exposure dataset with COPE, secondary modifiers, and TIV breakouts. Underwriters and cat modelers jump straight to sensitivity testing rather than spending weeks on data hygiene.

Security, explainability, and compliance for reinsurance

Reinsurers operate under strict data governance. Doc Chat meets that bar with enterprise-grade security, document-level traceability, and page-level citations that underpin every extracted value. Internal reviewers, auditors, regulators, and retro partners can verify any field with one click back to its source. As highlighted in our client story, Great American Insurance Group Accelerates Complex Claims with AI, this visibility builds trust and speeds adoption across teams that demand defensibility.

Implementation: white glove delivery in 1–2 weeks

Doc Chat is not a months-long IT project. We start by capturing your underwriting playbook and cat model template requirements. Then we configure “presets” that define your output schema, QA checks, and exception routing. Most reinsurers are live within 1–2 weeks, beginning with drag-and-drop uploads and stepping into API integrations as comfort grows. Our white glove team partners with your Property Treaty Underwriters and exposure analysts to refine prompts, fields, and exports—so the tool fits like a glove.

As your needs evolve, we iterate quickly. New secondary modifiers, bespoke risk factors, or treaty-specific field variations become part of your preset library. You’re not adopting generic software; you’re co-creating a living system that captures and scales your team’s best practices.

Answers at the speed of underwriting: examples of real-time Q&A

Because Doc Chat goes beyond extraction to reasoning, underwriters can interrogate entire submissions with natural-language questions. Examples:

“List all buildings with TIV > $25M within 3 miles of the coastline; include construction class and sprinkler status.”
“Which habitational locations are missing roof age or opening protection?”
“Summarize all discrepancies between the SOV and appraisal values by location.”
“Show the top 10 single-location TIVs and provide geocode confidence for each.”
“Roll up TIV by occupancy and state; export to the model template.”

Every answer includes citations back to the SOV cell or appraisal page that supports the conclusion. This turns document-heavy due diligence into fast, defensible analysis.

Quantifying the ROI for cat modeling workflows

Based on observed results across complex insurance document pipelines, carriers and reinsurers implementing Doc Chat typically see:

70–90% reduction in time spent preparing cat model inputs (from multi-day efforts to sub-hour runs on large submissions).
30–50% drop in rework due to late-surfacing data quality issues; inconsistencies are flagged up front.
10–20% uplift in data completeness for secondary modifiers harvested from appraisals and engineering reports.
Immediate scalability during surge periods without additional hiring or overtime.

These gains mirror the productivity improvements documented in our broader insurance work, where AI-driven document processing turns backlogs into minutes-long tasks while improving accuracy and standardization.

From brittle rules to teachable inference

Traditional document tools fail on variable formats because they rely on brittle, position-based rules. Reinsurance submissions defy those assumptions. Doc Chat’s advantage is that it incorporates your underwriting logic and learns where critical facts “live” across diverse documents—then proves every conclusion with a citation. This is the difference between extraction and inference, and it’s why the approach scales across cedents and treaty structures without constant maintenance.

A practical blueprint to get started

Turning on automated ingestion for the first submission is straightforward:

Identify your target schema: Provide your model import template and any portfolio roll-up fields.
Share sample submissions: Include varied SOVs, Location Schedules, Appraisal Reports, and full Property Risk Submission Packages.
Define QA rules: Minimum acceptable geocode precision, currency normalization, and validation checks (e.g., TIV totals by policy).
Establish exception routing: Determine what triggers human review (e.g., unknown occupancy code, low-confidence geocoding).
Launch in 1–2 weeks: Start with drag-and-drop ingestion and exports; add API integration to your modeling and accumulation systems when ready.

From there, your presets evolve. You’ll add new fields, incorporate cedent-specific patterns, and continue to raise the bar on data quality without ever adding manual toil.

Why now

Catastrophe risk will only get more complex. Secondary modifiers matter more, coastal density is rising, and submissions are getting bigger. Manual approaches won’t keep pace. With Doc Chat, Property Treaty Underwriters in Reinsurance (Property & Homeowners) can finally standardize and accelerate what used to be the longest pole in the tent—turning cedent SOVs, Location Schedules, Appraisal Reports, and full Property Risk Submission Packages into cat model inputs you trust, at the speed your market demands.

Incorporating your high-intent workflows and search

If you’ve been searching for practical solutions to “extract SOV data for cat modeling AI,” “automated location schedule ingestion,” “AI to pull property values from reinsurance cedent submissions,” or “process property risk documents for cat model input,” this is precisely what Doc Chat delivers—end-to-end, explainable, and live in weeks.

Ready to streamline cat model inputs and win back your underwriting day? Visit Doc Chat for Insurance to see it in action.