Rapid Underwriting Data Extraction from Foreign Policy Applications – International, Specialty Lines & Marine, and Property

International underwriting teams face a constant bottleneck: broker submissions arrive in dozens of languages and formats, mixing scanned PDFs, photos, and spreadsheets. The result is a slow, error-prone grind of reading, translating, retyping, and reconciling key data before a quote can even be produced. If you have ever tried to extract data from foreign insurance application packets under a tight SLA, you know exactly how much time it adds to every quote and endorsement.

Nomad Datas Doc Chat ends that bottleneck. Built for the realities of cross-border insurance, Doc Chat ingests entire submission packs  including foreign insurance applications, multilingual supplemental forms, and risk declarations  then automatically translates, normalizes, validates, and structures the data for your rating workbench, policy administration system (PAS), and exposure management tools. International Underwriters can finally rely on an AI that can AI process non-English underwriting forms, continuously answer questions about the submission, and automate data entry cross-border property policies end-to-end. Learn more about Doc Chat for insurers here: Doc Chat by Nomad Data.

The challenge for the International Underwriter: multilingual submissions, inconsistent formats, shrinking SLAs

In International, Specialty Lines & Marine, and Property & Homeowners programs, the underwriting packet is rarely neat. A single submission might include a property Statement of Values (SOV) in Spanish, a COPE questionnaire in French, a construction certificate in Italian, an occupancy questionnaire in German, photos of security systems annotated in Arabic, and a brokers cover email in English. Marine risks might add cargo manifests, bills of lading, port inspection reports, classification society certificates, and survey notes  often in languages that match local jurisdictions.

For the International Underwriter, nuances pile up quickly:

• Language & script variety: Latin, Cyrillic, Greek, Chinese, Japanese, Korean, and right-to-left scripts (Arabic, Hebrew) appear in the same file set, sometimes on the same page as stamps and handwritten endorsements.
• Regional data conventions: Dates (DD/MM/YYYY vs. MM/DD/YYYY vs. YYYY-MM-DD), decimal separators (comma vs. period), currencies (EUR, GBP, JPY, MXN), and units (sqm vs. sqft, meters vs. feet) vary by country and document author.
• Policy wordings & coverage nuances: Admitted vs. non-admitted placements; DIC/DIL structures; country-specific perils (e.g., temblor/terremoto, typhoon, flood zoning); and local fire classes that map imperfectly to COPE.
• Inconsistent formats: Scanned applications, broker-branded forms, non-ACORD templates, image-only PDFs, photos of pages, and spreadsheets without consistent headers.
• Regulatory & privacy constraints: PII/PHI, GDPR/LGPD/POPIA considerations, and sanctions screening (OFAC, EU, UK) at quote-time.

All of this happens while SLAs compress, brokers expect faster indications, and catastrophe volatility demands tighter controls on location accuracy, occupancy, construction, protection, and exposure details.

How the work is handled manually today (and why its slowing quotes)

Most international underwriting desks still rely on manual extraction and rekeying. The steps are familiar:

• Intake & triage: A submission arrives with foreign insurance applications, multilingual supplemental forms, risk declarations, property schedules, site plans, photos, and email threads.
• Translation: An analyst or underwriter translates non-English underwriting forms using ad-hoc tools or human translators, then tries to maintain fidelity to technical insurance terms.
• Data hunting & rekeying: Staff scour each page for COPE fields, protection details, construction class, TIV by location, SOV line items, occupancy descriptions, business interruption values, deductibles, sublimits, and exclusions; they retype values into spreadsheets and internal systems.
• Normalization: They convert currencies, dates, and measurements, attempt to standardize occupancies, and align to underwriting taxonomies (e.g., occupancy codes, fire protection classes).
• Validation: Location addresses and geocodes are validated; totals are reconciled; missing fields are flagged; inconsistencies between the application and the risk declaration are checked.
• Reconciliation & filing: Values are validated against prior-year submissions and loss run reports; notes are added; a manual audit trail is maintained for peer review and compliance.

Even a well-staffed team spends hours per submission. The longer the packet and the more languages involved, the more likely key details are missed or misinterpreted. Submissions bounce back and forth with brokers to clarify fields. Meanwhile, the market moves, and binding windows close.

What makes foreign insurance applications particularly hard for automation

Traditional OCR and template-based extraction break down under real-world variability. International submissions include:

• Irregular layouts: Fields appear in different sections across carriers and countries; checkboxes and free-text notes are interspersed with tables and stamps.
• Low-quality scans & images: Fax artifacts, skew, shadows, and background patterns make clean extraction unreliable.
• Mixed-language pages: A single page might combine French labels, Spanish free-text, and English stamps  with handwritten annotations in Arabic.
• Domain-specific inference: The information needed (e.g., final occupancy code, sprinkler adequacy, fire division integrity) may not be explicitly stated; it must be inferred from context and cross-referenced with internal taxonomies.
• Regional compliance: Sensitive data must be redacted or masked based on jurisdiction; audit trails are required for every extracted field.

This is exactly the complexity addressed in Nomad Datas perspective on document inference. For a deeper dive into why document automation is not just web scraping for PDFs, see Beyond Extraction: Why Document Scraping Isnt Just Web Scraping for PDFs.

Doc Chat: purpose-built to extract structured data from multilingual underwriting packs

Doc Chat is a suite of AI-powered agents tuned for insurance documents. It ingests entire submission files  thousands of pages at once  and produces structured outputs aligned to your underwriting schema. In other words, it was built to extract data from foreign insurance application packets at speed and scale, and to AI process non-English underwriting forms without brittle templates.

Key capabilities International Underwriters rely on:

• Massive volume throughput: Ingest whole submission folders (applications, SOVs, COPE forms, engineering/risk control reports, site plans, photos, risk declarations, survey reports) and get structured outputs in minutes.
• Multilingual understanding & translation: Robust OCR for Latin, Cyrillic, Greek, CJK, and RTL scripts; inline translation preserves technical terms for insurance; supports code-switching on the same page.
• Normalization & enrichment: Standardizes currencies, dates, and units; maps free text to your occupancy, construction, and protection taxonomies; validates addresses and produces geocodes; normalizes TIV and sublimits by location.
• Real-time Q&A: Ask questions across the entire submission: List all insured locations with TIV, construction type, year built, last roof update, and distance to coast. Show all sprinkler references and NFPA-equivalent descriptions. What exclusions or endorsements mention flood or quake?
• Downstream system delivery: Exports structured JSON/CSV directly into rating engines, PAS, exposure/cat modeling (e.g., RMS/AIR inputs), sanctions checks, and data lakes.
• Citations & auditability: Every data point includes page-level citations and links, so reviewers can verify the source instantly  a critical feature for compliance and peer review.

Because Doc Chat is trained on your documents and playbooks, it understands how your team codes occupancies, which perils you care about by region, and how you structure rating and referral rules. See how insurance leaders apply these capabilities in production in our write-up with Great American Insurance Group: GAIG accelerates complex claims with AI (the same foundations power underwriting use cases).

Example workflow: From non-English submission to priced indication in under an hour

Consider a cross-border property program with 28 locations across Spain, Portugal, and Italy. The broker submits:

• Carrier-branded applications in Spanish and Italian
• A French COPE-style supplemental form signed during risk engineering
• A Spanish SOV spreadsheet with mixed currencies (EUR and USD)
• Photos of fire pumps with handwritten Spanish notes
• Risk declarations for BI/EE exposures

With Doc Chat:

1. Upload: Drag-and-drop the entire folder. No manual sorting needed.
2. Ingest & translate: Doc Chat recognizes language per page, runs OCR, translates as needed, and maintains domain-specific terms.
3. Extract & normalize: It pulls COPE, location details, TIV, sublimits, deductibles, protection features, distance-to-coast, year built, roof updates; normalizes currencies and units; and validates address geocodes.
4. Validate & reconcile: It compares SOV totals to application totals; flags missing sprinkler details for two warehouses; and highlights that one Italian location lists ascensores (elevators) with outdated fire doors.
5. Answer questions instantly: The International Underwriter asks: Summarize all locations within 2 km of coastline with their flood and wind deductibles. Doc Chat returns a table with citations.
6. Export to systems: The underwriter exports JSON into the rating engine and a CSV formatted for the cat modeling team. Values flow directly into PAS for quote creation.

What used to take a day or more now runs in minutes, enabling same-day indications with confidence and audit-ready citations for peer review.

Specialty Lines & Marine: additional document types Doc Chat handles out of the box

International and Marine submissions often include documents that add complexity beyond Property & Homeowners. Doc Chat is designed for breadth:

• Marine cargo & hull: Bills of lading, cargo manifests, charterparty agreements, classification society certificates, Port State Control reports, surveyor notes, and loss prevention recommendations.
• International casualty & specialty: Local statutory applications, vendor contracts, certificates of insurance, broker market reform slips, and sanctions questionnaires.
• Property & Homeowners: SOVs, COPE questionnaires, engineering reports, flood determinations, occupancy attestations, security system certifications, and utility diagrams.
• Cross-program artifacts: Broker cover letters, binders, endorsements, local admitted policies, master policy wordings (DIC/DIL), facultative reinsurance slips, and loss run reports from prior carriers.

For each of these, Doc Chat extracts structured fields, maps them to your schema, and makes them queryable via natural language. It also maintains traceability back to the exact page and paragraph, so underwriting leadership and compliance teams can verify any fact instantly.

Tying automation to the questions underwriters actually ask

Doc Chats real-time Q&A aligns with the way International Underwriters work. Instead of hunting through PDFs, underwriters ask a question and receive an answer with source citations. Common prompts include:

• List all construction types and roof materials per location; flag any combustible construction and older roofs (> 20 years).
• Extract BI/EE exposures with values and waiting periods by country; return in EUR.
• Identify any endorsements or risk declarations that limit flood or quake, and show the exact language.
• Summarize sprinkler types, pump test dates, and impairment procedures across all sites.
• Show addresses with incomplete geocodes and provide confidence scores.

The ability to ask and refine questions turns underwriting into an interactive conversation with the entire submission, instead of a manual search exercise. Thats why organizations use Doc Chat to extract data from foreign insurance application materials, AI process non-English underwriting forms, and automate data entry cross-border property policies for fast, defensible quotes.

Business impact: time, cost, accuracy, and scale

Teams typically see large improvements in the first weeks:

• Time savings: End-to-end extraction and structuring shift from hours per submission to minutes, enabling same-day indications and faster bind decisions.
• Cost reduction: Less time spent translating, hunting fields, and rekeying; fewer handoffs between underwriting, analyst, and operations; fewer multiples in surge periods.
• Accuracy: Consistent extraction of COPE, TIV, sublimits, and endorsements; fewer missed exclusions; cleaner geocodes and unit/currency conversions.
• Scalability: Surge volumes and new-market expansions no longer require proportional headcount increases.

In our experience with carriers handling high-document workloads, AI removes bottlenecks during triage and quoting while standardizing the output format across the team. The AIs Untapped Goldmine: Automating Data Entry analysis explains why these gains are so consistent: most underwriting intake is data entry by another name, and intelligent document processing eliminates the most time-consuming steps. For scale, speed, and accuracy benchmarks on massive files, see The End of Medical File Review Bottlenecks (the same high-volume engine powers underwriting document review).

Why Nomad Datas Doc Chat is the right fit for International Underwriters

Unlike generic tools, Doc Chat is trained on your playbooks, taxonomies, and preferred outputs. That matters in international underwriting, where local norms, peril emphasis, and referral rules are nuanced by region, line of business, and distribution agreements.

Core differentiators for underwriting operations:

• Trained on your world: We encode your COPE taxonomy, occupancy codes, protection standards, and referral thresholds so extraction maps directly to how your desk makes decisions.
• White-glove implementation: Our team interviews your underwriters, operations leads, and IT; we configure agents around real submissions, validate outputs, and iterate rapidly.
• 12 week timeline: Start with drag-and-drop upload. As you prove value, we connect to PAS, rating APIs, cat modeling, sanctions, and data lakes  typically within 12 weeks.
• Enterprise-grade governance: SOC 2 Type 2 controls, secure deployment options, role-based access, document-level traceability, and page-level citations.
• A strategic partner, not a toolkit: Our process institutionalizes your best practices while evolving with your portfolio and geographies.

For a broader view of how these principles drive measurable transformation across insurance, see AI for Insurance: Real-World AI Use Cases.

Handling complexity specific to International, Specialty Lines & Marine, and Property

International Underwriters must reconcile domain-specific nuances quickly and consistently. Doc Chats agent design tackles these details head-on:

• COPE standardization: Converts native descriptions (Muro portante, Madera, Acero, etc.) into your construction classes; normalizes protection scores and distances to hydrants/FD.
• Location fidelity: Address parsing for multi-language, multi-format addresses; geocoding with confidence scoring; distance-to-peril calculations for flood, coastal wind, and wildfire interfaces.
• Currency & unit clarity: Line-item currency detection and conversion to rating currency; unit normalization for area, height, and distances; rounding rules by program.
• Cat model readiness: Outputs cat-ready files (RMS/AIR formats), including occupancy and construction mapping, year built, number of stories, and secondary modifier extraction where present.
• Sanctions & compliance hooks: Structured outputs route to sanctions screening and KYC checks before quote issuance; agent flags missing attestations or signatures in risk declarations.
• Endorsement & wording diligence: Surfaces clauses affecting DIC/DIL, terrorism pools, and local admitted requirements; cross-checks BI/EE nuances and waiting periods by country.

Security, audit, and regulatory confidence

International data flows are governed by regional privacy laws and strict internal controls. Doc Chat addresses these realities:

• Security posture: SOC 2 Type 2; encryption in transit and at rest; least-privilege access.
• Deployment flexibility: Cloud deployment with data residency options and integration into existing identity providers.
• Audit-ready outputs: Page-level citations for every extracted field, time-stamped logs, and replayable workflows simplify internal QA, reinsurer reviews, and regulatory inquiries.
• PII handling: Configurable redaction/masking policies by jurisdiction; opt-in controls over model learning.

These controls are the backbone of adoption. As highlighted in the GAIG experience, transparency and traceability build trust quickly when paired with accuracy and speed. See the workflow transformation story here: GAIG accelerates complex claims with AI.

From pilot to production: a pragmatic implementation path

We make it simple to get started and prove value:

• Week 1: Identify top submission types (e.g., Spanish/Italian property applications, French supplemental forms, marine survey reports) and the specific output schema for rating and PAS. Drag-and-drop test files and validate extractions.
• Week 2: Tune mappings to occupancy/construction/protection taxonomies, currency/unit conversions, and sanctions/KYC hooks. Configure export to rating and cat modeling. Launch limited production with a subset of underwriters.
• Week 3+: Expand to additional languages, programs (e.g., Specialty Lines & Marine), and brokers; integrate into intake portals and automate referral rule triggers.

Because Doc Chat ships with real-time Q&A and citation-backed extraction, value materializes immediately, even before systems integration. Thats by design.

How Doc Chat answers the three highest-intent needs we hear from underwriting teams

1) Extract data from foreign insurance application without delays or rework

Doc Chat reads multi-language packets end-to-end, standardizes units/currencies, maps to your taxonomies, and produces rating/PAS-ready outputs with citations. Underwriters verify any field in seconds and move straight to triage and pricing.

2) AI process non-English underwriting forms reliably and at scale

Unlike template-bound tools, Doc Chat understands variable layouts and mixed-language pages, infers missing context (e.g., protection adequacy), and handles scripts from French and German to Japanese, Arabic, and beyond  including handwritten notations where legible.

3) Automate data entry cross-border property policies into downstream systems

Doc Chat outputs JSON/CSV directly to rating and PAS, and creates cat modeling files for RMS/AIR. It also routes structured data to sanctions screening, exposure dashboards, and data lakes, eliminating swivel-chair data entry across fragmented systems.

What your peers see after rollout

Across international desks, we hear consistent feedback:

• Underwriters in control: Real-time Q&A surfaces the exact facts needed to decide, with links back to the page. No more paging through PDFs to confirm a single coverage detail.
• Operations headaches disappear: Intake standardization and consistent output formats reduce exceptions and rework.
• Better broker experience: Questions are targeted, turnaround is faster, and quotes are cleaner on first pass.
• Fewer misses, fewer disputes: Endorsements, exclusions, and sublimits are consistently captured and visible with citations.

This mirrors the broader pattern Nomad observes across insurance: machine-scale reading paired with human judgment produces faster, more consistent, and more defensible outcomes. For context on the broader claims and document landscape we support, see Reimagining Claims Processing Through AI Transformation.

Frequently asked questions from International Underwriters

Can Doc Chat ingest spreadsheets like SOVs? Yes. It extracts location-level TIV and other line items, reconciles totals against applications or risk declarations, and flags mismatches for review.

How are languages handled? Doc Chat detects language per page and per segment, runs OCR accordingly, and uses translation tuned for insurance terminology. Mixed-language pages and RTL scripts are supported.

What about low-quality scans and photos? The system applies image cleanup and layout understanding to improve OCR and extraction. Page-level confidence metrics identify fields worth a manual glance.

Can we align outputs to our internal codes? Absolutely. We tune mappings to your occupancy, construction, protection, peril, and referral codes so outputs land in your rating engine exactly as expected.

How fast is implementation? Most underwriting teams see value in days using drag-and-drop. Typical production integrations take 12 weeks, supported by our white-glove team.

What about security and privacy? Nomad Data maintains SOC 2 Type 2. We support data residency needs, role-based access, and document-level traceability with citations and audit logs.

Putting it all together: your next step

International underwriting will only grow more document-heavy as carriers expand into new markets and brokers add more evidence to de-risk placements. The winners will be those who turn multilingual submissions into structured, trusted data instantly  not days later.

Doc Chat was built for this moment. If youre ready to extract data from foreign insurance application packets, AI process non-English underwriting forms, and automate data entry cross-border property policies straight into rating, PAS, and cat modeling, see how quickly you can move from pilot to production: Explore Doc Chat for Insurance.

Related reading:

• Beyond Extraction: Why Document Scraping Isnt Just Web Scraping for PDFs
• AIs Untapped Goldmine: Automating Data Entry
• The End of Medical File Review Bottlenecks
• AI for Insurance: Real-World AI Use Cases Driving Transformation