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

Exposure Analysts across Reinsurance and Property & Homeowners lines face a common, high-stakes challenge: transforming messy cedent submissions into model-ready exposure data fast enough to keep up with catastrophe modeling timelines and renewal cycles. Statements of Values, location schedules, appraisal reports, and sprawling Property Risk Submission Packages frequently arrive as mixed-format PDFs, spreadsheets, emails, and attachments that must be reconciled, normalized, and validated before a single RMS or Verisk AIR run can begin. The cost of delay is real: slower time-to-model means slower time-to-quote, fewer pricing iterations, and less negotiating leverage.

Nomad Data’s Doc Chat was built for exactly this kind of document-intensive work. Doc Chat is a suite of AI-powered agents that ingests entire cedent packages, extracts structured exposure fields, standardizes taxonomies, and outputs model-ready files in minutes. Unlike rigid templates or brittle scripts, Doc Chat reads like an analyst, cross-checking values across documents and surfacing gaps and inconsistencies before they derail your catastrophe modeling workflow. Exposure Analysts can ask dynamic questions, iterate in seconds, and proceed to modeling with confidence. Learn more about the product here: Doc Chat for Insurance.

The Exposure Analyst’s Reality in Reinsurance and Property & Homeowners

For an Exposure Analyst, the clock starts the moment a cedent’s submission lands. The initial review often reveals dozens or hundreds of locations with incomplete or conflicting details. The files might mix a Statement of Values (SOV) in multiple tabs, separate Location Schedules for different regions or MGAs, and scanned appraisal reports with critical construction, occupancy, protection, and exposure (COPE) information. Property Risk Submission Packages may include binders, coverage schedules, endorsements, risk engineering narratives, valuation memos, and emails clarifying whether building, contents, and business interruption (BI) values should be allocated per location or aggregated at the account level.

In Property & Homeowners reinsurance, this nuance matters. Catastrophe model inputs are unforgiving. A small mapping error between occupancy codes, a currency mismatch, or a missing year-built field can force rework, delay model runs, and reduce the number of pricing scenarios you can evaluate. Even when cedents do provide latitudes and longitudes, they may be inconsistent with postal addresses or derived in ways that do not meet your geocoding standards. Secondary modifiers relevant to wind, quake, flood, or wildfire often hide across pages of appraisals and engineering reports: roof age and covering, roof deck attachment, number of stories, foundation type, cladding, glass type, sprinkler status, fire alarm and monitoring details, distance to coast, brush exposure, defensible space, flood zone, and elevation. Finding these at scale within tight renewal windows is the Exposure Analyst’s daily reality.

Why It Is So Hard: The Nuances Behind Cat Model Inputs

Exposure data for catastrophe modeling is uniquely complex because it combines data fidelity, taxonomy mapping, and treaty context. A few of the many nuances an Exposure Analyst must manage include:

• Document format diversity: SOVs as Excel, CSV, or embedded in PDF scans; Location Schedules split by business unit; valuation details in Appraisal Reports; COPE data in engineering narratives within Property Risk Submission Packages.
• Non-standard fields and naming: Total Insurable Value (TIV) broken into building, contents, and BI in some files and combined in others; missing or misnamed fields like OCC, CONST, Sprinklered, PCB, or Fire Alarm that need normalization.
• Taxonomy alignment: Mapping cedent occupancy and construction codes to your internal standards or to RMS and AIR model taxonomies, including handling local descriptors that do not map one-to-one.
• Currency, valuation date, and inflation: Converting currencies, aligning valuation as-of dates, and optionally applying indexation or inflation assumptions before modeling.
• Geocoding fidelity: Reconciling conflicting address and lat/long data; deduplicating overlapping or near-duplicate locations; flagging PO boxes or centroid-quality coordinates not appropriate for hazard assignment.
• Secondary modifiers: Extracting roof, elevation, protection, and vulnerability attributes from free text in appraisals and engineering reports; documenting assumptions where fields are missing.
• Policy terms and structure: Aligning per-location limits and deductibles with the cedent’s policy forms; handling sublimits and endorsements; reflecting per-peril or all-risk structures; ensuring the model input reflects how losses will attach to the treaty.
• Auditability and governance: Maintaining page-level traceability from source to model-ready row, so actuaries, underwriters, and auditors can confirm the derivation of every field.

Each of these factors compounds with scale. A single cedent may send a 50,000-row SOV and hundreds of supporting pages. A treaty can aggregate dozens of cedents. When renewals cluster, Exposure Analysts are asked to perform near-impossible feats of speed without sacrificing rigor.

How the Process Is Handled Manually Today

The prevailing process relies on skilled Exposure Analysts doing painstaking, repetitive work that does not scale. Typical steps include:

First, an analyst unzips a cedent package and inventories documents: SOV workbook, Location Schedules, Appraisal Reports, Property Risk Submission Package PDFs, certificates, endorsement lists, and policy forms. Next comes format normalization: converting PDFs to spreadsheets; standardizing column names; copying and pasting rows between tabs; removing header rows and footers; and reorganizing merged cells. The analyst cleans addresses, executes a batch geocode, and manually reviews failures and low-confidence matches. They reconcile lat/longs against addresses, chase down missing postal codes, and flag PO boxes or duplicate locations.

Then comes mapping and enrichment. The analyst translates occupancy and construction descriptions into model-recognized categories. Where possible, they pull secondary modifiers from appraisals or engineering reports. They validate totals (for example, that building plus contents plus BI equals TIV), confirm currency and valuation dates, and compute derived fields. A round of data QC follows: checking for outliers (suspiciously high or low values), missing fields, and inconsistent coding across regions or MGAs. Queries back to the cedent follow, creating another cycle of waiting, rework, and version control headaches.

Finally, the analyst prepares import files for catastrophe models like RMS or AIR, exports CSVs with the right schema, and holds their breath while running the first tests. If any required field is missing or misaligned, it is back to step one. Across a renewal season, weeks are lost. Opportunities to run more scenarios, test alternate terms, or adjust to real-time market dynamics vanish because too much time was consumed by manual SOV ingestion and cleanup.

Doc Chat Changes the Game: Automated Location Schedule Ingestion, Normalization, and Validation

Nomad Data’s Doc Chat replaces many of these manual steps with an AI-powered workflow engineered for high-volume, high-variance document sets. Doc Chat ingests entire claim and underwriting files out of the box, and for exposure work it ingests entire cedent submission packages as-is: multi-tab SOVs, nested PDFs, attachments, and email text. It reads everything, identifies document types, and extracts structured fields into a single, governed data frame. Because Doc Chat understands context and intent, it is not thrown off by inconsistent column names or layouts. That means it can handle your automated location schedule ingestion end to end.

With real-time Q&A, you can ask the system to enumerate missing fields by location, list conflicting values (for example, sprinkler status marked Yes in one tab and No in a later appraisal), and map occupancy and construction to target taxonomies. Doc Chat cross-checks totals and validates that TIVs reconcile with their building, contents, and BI components. It flags currency inconsistencies, applies your inflation rules if desired, and prepares model-ready output files aligned to your RMS or AIR schema, complete with audit trails back to the page and cell of origin.

For the Exposure Analyst, this removes the repetitive friction that dominates the calendar. It also institutionalizes best practices so results are consistent regardless of who handles the file. Nomad’s volume capacity means Doc Chat can ingest thousands of pages and hundreds of thousands of rows in minutes, shaving days off time-to-model.

High-Intent Use Cases and How Doc Chat Addresses Them

extract SOV data for cat modeling AI

Exposure teams searching for ways to extract SOV data for cat modeling AI need accuracy and explainability. Doc Chat extracts building, contents, and BI values by location, normalizes location IDs, and preserves the relationship between rollups and per-site records. It maps cedent-specific occupancy and construction terms to the selected RMS or AIR taxonomy and assembles required fields for vendor import specifications. Every extracted field includes citations back to the source tab and cell or the page and paragraph in a PDF, enabling quick verification during peer review or audit.

automated location schedule ingestion

Submissions often include multiple Location Schedules split by country or program. Doc Chat recognizes these fragments, merges them into a unified table, deduplicates overlapping rows, and resolves conflicts using your rules-of-precedence. When SOVs are embedded in scanned PDFs, Doc Chat applies robust OCR, then validates numeric fields and format consistency before accepting the data. You get an automated location schedule ingestion pipeline that does not break when cedents deviate from templates.

AI to pull property values from reinsurance cedent submissions

When analysts look for AI to pull property values from reinsurance cedent submissions, they need more than simple extraction. Doc Chat reads supporting Appraisal Reports and engineering narratives to supplement fields that are not in the SOV, like roof covering or elevation. It aligns valuation dates and currencies, calculates per-location TIV components if only aggregate values are provided, and highlights where assumptions are required. The result is a complete, model-ready dataset with a clear documentation trail.

process property risk documents for cat model input

To process property risk documents for cat model input, Doc Chat orchestrates the entire pipeline: ingest, classify, extract, normalize, validate, enrich (through integrations), and export. It can produce multiple output flavors for different platforms, or tailored CSVs for internal risk engines. Most importantly, it doesn’t just hand you a file; it gives you a structured set of quality checks, exceptions, and recommended queries back to the cedent, so your follow-ups are focused and minimal.

What Doc Chat Extracts From Property & Homeowners Cedent Packages

Doc Chat captures far more than just basic SOV fields. For Exposure Analysts, the most valuable outputs include:

• Core identification: Location ID, policy number, site name, site address, city, state, postal code, country, latitude and longitude (if provided), geocode confidence notes.
• Values and terms: Building, contents, and BI values; TIV; coverage A/B/C equivalents where relevant; per-location limits and deductibles; sublimits; coinsurance; valuation date; currency and conversion assumptions.
• COPE and secondary modifiers: Primary occupancy, sub-occupancy, construction class, year built, number of stories, floor area, roof type, roof age (if stated), roof covering, roof deck attachment details if available, sprinkler presence, fire alarms and monitoring, ISO protection class or equivalent, distance to hydrant or station when specified, and presence of flood defenses or defensible space if documented.
• Hazard context via integrations: Distance to coastline, elevation, flood zone, brush index, or fire hazard scoring where your organization provides a connection to an approved data source.
• QC validation outputs: Reconciled totals; percentage and count of missing fields by category; anomaly flags for outliers or conflicting entries; deduplication reports and match rationales.

These extractions are not generic. They are shaped by the Nomad Process, where Doc Chat is trained on your exposure playbooks, model schemas, taxonomies, and acceptance criteria. Outputs are consistent across cedents and renewal cycles, raising data quality while cutting cycle time.

Real-Time Q&A for Exposure Analysts

Doc Chat’s interactive experience is a force multiplier for Exposure Analysts. Rather than skating line-by-line through a 40,000-row SOV, analysts can ask targeted, portfolio-level questions like: list all locations missing sprinkler status; show locations where TIV declined more than 20 percent versus last year; identify duplicate coordinates with conflicting addresses; or enumerate occupancy values that do not map to the RMS taxonomy. Doc Chat answers in seconds and provides the linkage back to source data, letting analysts move from ingestion to modeling with only the critical exceptions in view.

Beyond ad hoc checks, analysts can store recurring prompts as presets for consistent pre-model QC. This standardizes the intake procedure so the same checks run for every cedent and treaty, making peer reviews faster and more defensible.

Data Quality, Consistency, and Auditability Built In

Between cedent heterogeneity and compressed timelines, quality often suffers in manual workflows. Doc Chat enforces consistency by applying your rules the same way every time. It:

• Normalizes column names and field types across files and tabs.
• Maps occupancies and construction to your chosen taxonomy, with exceptions flagged for manual attention.
• Checks that building plus contents plus BI equals TIV, warns on discrepancies, and suggests fixes when derivable.
• Aligns valuation dates and currencies, applying your conversion rules where permitted.
• Detects duplicates within and across files, scoring potential matches and presenting rationale.
• Generates a completeness and anomaly dashboard so analysts see risk hotspots immediately.
• Maintains page-level and cell-level citations for traceability from derived fields back to exact sources.

This level of transparency matters for reinsurers operating under tight governance, audit, and regulatory expectations. When management, actuaries, or auditors ask how a field was derived, Doc Chat presents a clear, defensible trail in seconds.

From Documents to Model-Ready: Output That Meets Your Cat Platform Requirements

Exposure Analysts need outputs that drop into RMS, AIR, or internal engines without additional massaging. Doc Chat produces:

• Validated CSVs aligned to your vendor import schema.
• Standardized code values for occupancy and construction based on your mapping tables.
• Separate site, coverage, and terms tables when your workflow splits these structures for modeling.
• Exception files indicating which rows require cedent follow-up, the reason code, and suggested clarifying questions.
• Change logs comparing this submission to prior years for the same cedent to fast-track year-over-year analyses.

Doc Chat also integrates with your exposure management systems or data lakes through modern APIs, so outputs can flow directly into downstream modeling pipelines. Many teams begin with drag-and-drop document processing and graduate to API-driven automation within the first two weeks.

Business Impact: From Days to Minutes, With Fewer Errors and More Scenarios

Doc Chat’s impact on Exposure Analyst productivity is immediate. As Nomad has demonstrated across complex claims and medical file reviews, reading and summarizing thousands of pages can move from days to minutes without sacrificing accuracy. In document-heavy property submissions, we routinely see a similar transformation: model prep that once took multiple analysts a week can be executed in under an hour, with better completeness and consistency. The throughput increases are especially powerful during renewals and catastrophe season, when time-to-model determines how many pricing scenarios and treaty structures you can evaluate.

Key outcomes for Exposure Analysts and their reinsurance partners include:

• Faster time-to-model: With automated SOV ingestion and validation, analysts can run models earlier, re-run more scenarios, and respond to cedent or broker updates the same day.
• Lower cost per submission: Reducing manual data entry and QC trims loss-adjustment-like expenses on the analytics side and minimizes overtime or staffing spikes during renewals.
• Improved accuracy and consistency: AI does not fatigue, and standardization reduces leakage from mapping mistakes or missed secondary modifiers.
• Greater negotiating leverage: More modeling cycles and clearer documentation of assumptions strengthen pricing and terms discussions.
• Higher analyst engagement: Analysts spend time on investigative and strategic work instead of copying and pasting between spreadsheets.

For broader context on the economics of automating document-heavy work, see Nomad’s perspective in AI's Untapped Goldmine: Automating Data Entry and how AI transforms complex document analysis in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs. The same principles that accelerated medical review bottlenecks, described in The End of Medical File Review Bottlenecks, apply to high-volume exposure ingestion.

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

Doc Chat stands apart because it goes beyond basic optical character recognition or rigid templates. It is a purpose-built system for high-volume, high-variance document review that mirrors the way an experienced Exposure Analyst thinks. A few differentiators matter most for reinsurance and property:

• Volume and speed: Doc Chat ingests entire claim files and submission packages at scale, moving review from days to minutes without adding headcount.
• Complexity handling: It sees through inconsistent labels, buried endorsements, and variable structures to surface the coverage, values, and attributes that matter.
• The Nomad Process: Your playbooks, taxonomies, and modeling schemas drive the outputs, so the system feels like it was built expressly for your team’s workflows.
• Real-time Q&A: Analysts can interrogate thousands of pages instantly to find missing fields, conflicting entries, or opportunities for enrichment.
• Thorough and complete: Doc Chat surfaces every reliable reference to exposure, terms, and modifiers across the cedent package, minimizing blind spots.
• White-glove service: Nomad acts as a partner, co-creating solutions, training the system on your standards, and supporting your team through renewals.
• Fast implementation: Typical implementations run 1–2 weeks to productive use, with drag-and-drop available on day one and APIs layered in as you scale.

Security and governance are first-class concerns. Nomad maintains enterprise-grade controls, including SOC 2 Type 2, and provides page-level and cell-level citations for defensibility with auditors, reinsurers, and internal governance. For more on the breadth of insurance use cases and how implementation unfolds, see AI for Insurance: Real-World AI Use Cases Driving Transformation.

How Doc Chat Automates Your End-to-End Exposure Workflow

Doc Chat’s automated pipeline for Exposure Analysts typically includes the following steps:

• Ingestion: Drag-and-drop or API upload of SOVs, Location Schedules, Appraisal Reports, Property Risk Submission Packages, policy forms, and related emails. Doc Chat recognizes document types automatically.
• Structure and normalization: Extraction of tables from Excel and PDFs; normalization of headers; consolidation across tabs; type coercion for numeric fields.
• Mapping: Automated mapping of occupancy, construction, and other categorical fields to your target taxonomy or model vendor codes, with flagged exceptions.
• Geocoding support: Reconciliation of provided lat/long versus address; exception lists for problematic addresses; optional routing to your preferred geocoding service via integrations.
• Secondary modifier extraction: Pulling COPE attributes from appraisals and engineering narratives; associating to the correct location IDs; documenting assumptions when data is absent.
• Validation: Reconciliations of TIV against components; checks for currency consistency; valuation date alignment; duplicate detection and resolution logs.
• Enrichment via integrations: Connecting to your hazard or enrichment vendors to append fields like distance to coast, elevation, or flood zone where permitted and desired.
• Output: Generating vendor-specific or internal model-ready CSVs; exception files with reason codes; and dashboards summarizing completeness and anomalies.
• Q&A and iteration: Real-time queries to refine the dataset, produce exception summaries, and generate cedent questionnaires only for the gaps that remain.

This end-to-end flow compresses the time between receiving a cedent package and running your first reliable model. It also standardizes the process across analysts and seasons, drastically improving consistency.

A Day in the Life With Doc Chat: Exposure Analyst Edition

Morning: You receive a Property Risk Submission Package containing a 30,000-row SOV, three PDF Location Schedules, and five Appraisal Reports. Within minutes of uploading to Doc Chat, you see a unified table with initial QC metrics and a short list of blocked rows: missing postal codes for 62 sites and ambiguous street addresses for 14.

Midday: You run a prompt that identifies all occupancy values not mapped to your RMS schema. Doc Chat shows three cedent-specific labels and recommends mappings based on prior work for the same cedent. You accept two mappings and mark the third for cedent follow-up. You then ask for all locations where TIV changed more than 25 percent year-over-year. Doc Chat lists 219 rows, with links to source cells and appraisals that documented renovations or disposals.

Afternoon: You trigger the RMS-ready export, generating site, coverage, and terms CSVs along with an exception file. You forward a concise questionnaire to the broker about the 14 ambiguous addresses and the one occupancy mapping still in question. When answers arrive, you paste them into Doc Chat, update the rows, and re-export. You run your first RMS scenarios before close of business instead of tomorrow.

Implementation: White-Glove, Fast, and Tailored to Exposure Work

Nomad’s implementation model is designed for rapid value. Most Exposure Analyst teams begin handling live cedent packages in 1–2 weeks. The steps are straightforward:

• Discovery: We learn your exposure schemas, taxonomy mappings, geocoding standards, and rejection criteria.
• Configuration: We encode your playbook, rules-of-precedence, and QC checks into Doc Chat presets; connect to your enrichment and geocoding providers where desired.
• Pilot: You process real cedent packages side-by-side with your current workflow; we tune mappings, exception handling, and outputs.
• Scale: We integrate with your exposure management or modeling platform via API; your analysts adopt Q&A and preset-driven checks for consistency and speed.

The experience is white-glove by design. You are not buying a generic tool; you are partnering with a team that co-creates a solution aligned to your renewal calendar, treaty structures, and modeling requirements. We maintain a close feedback loop through peak periods so improvements land when they matter most.

Security, Controls, and Defensibility for Reinsurers

Data protection and defensibility are non-negotiable in reinsurance. Doc Chat provides page-level and cell-level citations that make it easy to verify any field’s provenance. Outputs include versioned change logs and full audit trails so that leadership, compliance, and auditors can retrace steps from model-ready files back to the exact source documents. Nomad adheres to rigorous security practices, including SOC 2 Type 2, and aligns to your data governance standards. Answers can be verified in a click, echoing the trust model that carriers appreciate in other high-stakes workflows, as discussed in our client story Reimagining Insurance Claims Management with GAIG.

Beyond Ingestion: Portfolio Insight and Continuous Improvement

Once ingestion and validation are automated, Exposure Analysts can turn to higher-order questions. Doc Chat can compare current and prior-year submissions to surface structural changes in the portfolio, geographic drift, concentration risk around high-hazard zones, and changes in secondary modifiers that impact vulnerability. With faster and more reliable inputs, modelers and underwriters can iterate on terms and layers, test alternative structures, and coordinate reinsurance capacity decisions with greater confidence.

This is where the compounding benefits appear. Automation converts ingestion from a bottleneck into a capability. The ability to re-run models quickly changes how teams negotiate, how they respond to breaking weather events, and how they prepare post-event exposure snapshots. For a broader view of how AI modernizes the insurance value chain, see Reimagining Claims Processing Through AI Transformation and the cross-functional use cases in AI for Insurance.

Frequently Asked Questions for Exposure Analysts

Can Doc Chat map our cedent-specific occupancy to RMS or AIR codes? Yes. During setup, we encode your mapping tables and rules for handling unknown or ambiguous values. Doc Chat suggests mappings for new values, and exceptions are flagged for analyst review.

How does Doc Chat handle addresses and geocoding? Doc Chat reconciles provided addresses and coordinates, highlights discrepancies, and generates exception lists. Many clients integrate their preferred geocoding service so Doc Chat can route unresolved addresses for external resolution while preserving an audit trail.

What about secondary modifiers like roof type or sprinkler status? Doc Chat extracts these from SOVs, Appraisal Reports, and engineering narratives when documented. Where fields are missing, it flags gaps and prepares a cedent questionnaire so you only ask for what you truly need.

Can it output model-ready files? Yes. Doc Chat generates RMS- or AIR-aligned CSVs and any internal variants you require, along with exception files and QC dashboards.

How quickly can we start? Most teams begin drag-and-drop processing within days and reach steady-state use within 1–2 weeks. API integrations to your exposure management systems typically follow soon after.

Getting Started: A Pragmatic Path to Impact

The fastest way to see impact is to run Doc Chat on a few live cedent submissions from your current cycle. We recommend selecting packages that reflect your typical challenges: mixed-format SOVs, location schedules split across regions, and appraisals with useful secondary modifiers. In a short pilot, you will see how Doc Chat’s automated location schedule ingestion and normalization reduces rework, and how its Q&A capabilities let you focus on exceptions instead of manual scanning.

If your team is evaluating solutions explicitly to extract SOV data for cat modeling AI, AI to pull property values from reinsurance cedent submissions, or to process property risk documents for cat model input, Doc Chat gives you an enterprise-grade, auditable foundation that scales. Exposure Analysts keep control, enhance their craft, and deliver more insights, faster.

When you are ready to move from bottlenecks to leverage, visit Doc Chat for Insurance and see how quickly your exposure workflow can transform.