Streamline Regulatory Response: AI-Powered Compilation of Document Requests From State DOIs — Built for the Claims Data Manager

When a State Department of Insurance (DOI) issues a data call, the clock starts. For a Claims Data Manager in Property & Homeowners, Auto, and Commercial Auto, that can mean sifting through thousands of pages of claims files, policy records, loss run reports, FNOL forms, ISO claim reports, medical reports, repair estimates, police reports, and demand letters across multiple systems and vendors. The challenge isn’t just speed; it’s accuracy, defensibility, and a clear audit trail. Missed fields or inconsistent definitions can trigger back‑and‑forth rework, market conduct exams, and potential fines.

Doc Chat by Nomad Data changes that calculus. Purpose‑built for insurance documentation, Doc Chat’s AI agents ingest entire claim files and policy binders, answer natural‑language questions in real time, and export regulator‑ready datasets with page‑level citations. In short: it’s how teams automate DOI data calls, AI pull data for insurance regulatory request, and quickly respond to insurance DOI document requests without adding headcount or sacrificing quality.

Why DOI Data Calls Are So Hard for Claims Data Managers in Property, Auto, and Commercial Auto

Every state frames its DOI data call a little differently. One asks for “closed with payment” counts by coverage and peril; another wants “paid indemnity vs. ALAE” broken out by catastrophe code and county. A third seeks “average days from FNOL to coverage decision,” “reserve changes over time,” or “SIU referral dates with outcomes.” As a Claims Data Manager, you must translate that letter into field definitions, map those fields to what actually exists in your claim system(s), then mine unstructured content for everything that isn’t stored in tables.

It’s especially challenging across Property & Homeowners (peril mapping, deductible applications, ALE/additional living expense and contents subtotals), Auto (BI/PD splits, med payments, rental days, total loss and salvage details), and Commercial Auto (fleet policies, driver MVRs, cargo losses, subrogation recoveries). Key values are buried in long‑form documents—adjuster notes, coverage determination letters, ISO claim reports, demand packages, police reports, appraisals, contractor invoices, independent adjuster estimates, water mitigation logs, mold remediation reports, and medical bills with CPT/ICD codes. The data you need rarely lives in one place, in one format, or under one obvious label.

How Teams Handle DOI Data Calls Manually Today

Most carriers cobble together a tedious, risky process that consumes people and time:

• Parsing the DOI data call request into a spreadsheet, writing column‑by‑column definitions and business rules.
• Pulling initial extracts from core systems (e.g., Guidewire ClaimCenter, Duck Creek, Sapiens, Origami Risk), then reconciling data gaps found in claim notes, PDF attachments, emails, and third‑party portals.
• Manually reading claims files to find dates (FNOL, coverage decision, payment, SIU referral, subrogation notice), specific coverage triggers, endorsements, deductibles, and limits inside policy records.
• Copying values from repair estimates, medical reports, demand letters, police reports, salvage titles, and loss run reports to fill missing fields.
• Reconciling conflicting values between adjuster notes and vendor documents; annotating assumptions; building a paper trail for compliance.
• Exporting a CSV, spot‑checking by hand, answering regulator follow‑ups, and repeating the cycle when field definitions evolve mid‑stream.

Even when teams do everything right, cycle times stretch. Humans get fatigued. Turnover rises. And every iteration increases the chance of inconsistencies—exactly what you want to avoid in a regulatory response.

Automate DOI Data Call Insurance: How Doc Chat Converts DOI Letters Into Actionable, Auditable Outputs

Doc Chat’s insurance‑grade AI agents are trained to read like an experienced claims professional—at superhuman speed and scale. For a Claims Data Manager, that means the platform can:

• Read the DOI letter like a person: The agent parses the DOI data call request, recognizes each requested field, normalizes terminology (e.g., “closed with pay” vs. “CWP”), and links each item to your internal definitions.
• Map fields to your data: It aligns requests to the claim system schema plus unstructured sources—FNOL forms, ISO claim reports, adjuster notes, demand packages, medical bills, repair/IA estimates, police reports, correspondence, and policy endorsements.
• Ingest entire claim files: Thousands of pages of claims files, policy records, and loss run reports arrive in any format; Doc Chat structures the content, extracts values, and provides page‑level citations.
• Fill gaps with intelligent reading: When a value doesn’t exist as a table field, the AI reads documents to infer the correct, regulator‑ready answer (e.g., “cause of loss,” “deductible applied,” “SIU referral date,” “subrogation recovery amount”).
• Create a regulator‑ready file: Exports are delivered as CSVs, spreadsheets, or JSON with a complete audit trail linking each answer to its source page(s) and the rule used to determine it.
• Support real‑time Q&A: Ask, “List all coverage denials in commercial auto due to late notice,” or “Show average days from FNOL to first payment by catastrophe code,” and get answers instantly, with citations.

Because Doc Chat is trained on your playbooks, market conduct practices, and field definitions, it behaves like a tenured team member—one who never tires and never misses a page. For further context, see Nomad’s explainer on why advanced document scraping is not just web scraping for PDFs.

What Doc Chat Extracts for Property, Auto, and Commercial Auto DOI Data Calls

Across lines, the AI can populate common DOI call fields and line‑specific nuances, including:

• All Lines: policy number, claim number, coverage part, claim status, date of loss, FNOL date, coverage determination date, first payment date, total paid indemnity, total ALAE/LAE, reserves (initial/current/final), claim handler, SIU referral date/outcome, subrogation demanded/recovered, litigation indicator, demand letter dates, ISO claim report dates, complaint indicator, catastrophe code, county/ZIP, insured name, claimant name.
• Property & Homeowners: cause of loss/peril, dwelling vs. other structures vs. contents vs. ALE splits, deductible amounts and application, endorsements (water backup, ordinance or law), contractor estimates, water mitigation logs, mold remediation invoices, depreciation schedules, replacement cost vs. actual cash value calculations.
• Auto: BI/PD/MedPay splits, total loss indicator, ACV vs. settlement, salvage proceeds, rental days, repair estimate totals, appraisal supplements, police report numbers, medical bills (CPT/ICD if needed), provider names/TINs, medical demand packages, IME results.
• Commercial Auto: fleet policy terms, driver assignments, MVR indicators, cargo coverage, subrogation and contribution details, third‑party carrier interactions, litigation milestones, vocational reports.

If the DOI requests different groupings (e.g., “Closed Without Payment” by coverage and county; “Open Over 180 Days” by peril and reserve bucket), Doc Chat reshapes the same source data accordingly, with page‑level citations to support your answers.

Quickly Respond to Insurance DOI Document Requests: Real Examples of Speed and Scale

Doc Chat’s volume and complexity handling have been proven in the wild. In one carrier’s complex claims portfolio, massive PDFs that previously took days to read by hand were summarized in minutes with source links. For a window into how real claims teams work differently once this capability is live, review our webinar recap with GAIG: Great American Insurance Group Accelerates Complex Claims with AI.

In the context of DOI responses, that same speed translates to preparing a regulator’s workbook in hours rather than weeks—while improving completeness and defensibility.

The Manual vs. Automated Difference for a Claims Data Manager

Let’s contrast typical workflows for a Property & Homeowners catastrophe data call, plus parallel Auto and Commercial Auto asks:

Manual today:

• Export claims table extracts, then comb unstructured files for missing values (e.g., coverage determination date, deductible application details, contents vs. dwelling splits, SIU referral date).
• Open hundreds of PDFs to copy values from FNOL forms, ISO claim reports, adjuster notes, contractor estimates, police reports, medical reports, and demand letters.
• Assemble a spreadsheet, then answer iterative DOI follow‑ups (“Show proof of deductible application; provide page references; add county‑level aggregations”).

Automated with Doc Chat:

• Drag‑and‑drop the DOI data call request and upload representative claims files, policy records, and loss runs.
• Doc Chat interprets the request, maps fields to your data, locates missing values across PDFs, and exports a complete dataset with page citations.
• When regulators ask for a new slice (e.g., “closed with payment by peril and county”), re‑prompt Doc Chat and export a new tab—no manual rereads.

That’s the difference between reactive fire drills and a calm, repeatable process controlled by the Claims Data Manager.

Business Impact: Time, Cost, Accuracy, and Regulatory Confidence

State DOIs expect accuracy and speed. Doc Chat delivers both—plus consistency and transparency that build regulator confidence and reduce the risk of market conduct scrutiny.

Time savings: Response cycles drop from weeks to days or even hours. Review time on unstructured files falls by 80–95%. Generating alternate aggregations takes seconds rather than days.

Cost reduction: Overtime and temporary staffing shrink. Outside vendor spend for manual abstraction and validation declines significantly. You avoid repeated, expensive cycles of re‑work when definitions change mid‑stream.

Accuracy & completeness: Page‑level citations and consistent application of playbook rules eliminate blind spots. Every instance of a requested field is surfaced, and assumptions are logged, so nothing slips through the cracks.

Auditability & defensibility: Every exported value is tied to the document page it came from, with a change log of how values were derived. That’s exactly what regulators need when they ask, “Show me.”

Beyond Speed: Consistency Across Teams and Lines of Business

One of the most frustrating parts of a large DOI data call is inconsistency—different desks produce slightly different definitions or judgment calls, particularly across Property, Auto, and Commercial Auto. Doc Chat encodes your best practice rules into a repeatable agent workflow. Everyone works from the same definitions, so the response set is consistent regardless of who touches the file. That standardization pays dividends in ongoing market conduct monitoring and annual MCAS production, too.

How Doc Chat Learns Your Nuances

Regulatory work is filled with institutional knowledge—practices that never made it into a formal SOP. Doc Chat incorporates your nuances:

• Your definitions for “closed with payment” and “open over 180 days”.
• How you treat “deductible applied” and “endorsement triggers.”
• Which document type wins when conflicting values appear (e.g., adjuster note vs. vendor invoice).
• How to group perils, catastrophe codes, and coverage parts across Property, Auto, and Commercial Auto.

This is the “Nomad Process,” described in practice in our transformation overview: Reimagining Claims Processing Through AI Transformation.

From Zero to Live in 1–2 Weeks: White‑Glove Implementation

Unlike generic tools that leave most of the heavy lifting to your team, Nomad’s white‑glove service configures Doc Chat around your documents and workflows. Typical timeline:

1. Discovery (days 1–2): Review a recent DOI data call, your data model, and representative files (Property, Auto, Commercial Auto).
2. Playbook encoding (days 2–5): Nomad captures your unwritten rules—e.g., field precedence, overrides, and line‑of‑business nuances—and maps them to the agent.
3. Pilot run (days 5–7): Process real claims files, policy records, loss runs, and attachments; validate outputs and citations with your team.
4. Refinement (days 7–10): Address edge cases; finalize export formats; stand up secure integrations (SFTP, API, ECM).
5. Go‑live (days 10–14): Team training; optional integration to your claim system or data lake; support for the next DOI call.

During onboarding and beyond, you get a partner—not just software. For more on why this human‑in‑the‑loop approach matters, read AI’s Untapped Goldmine: Automating Data Entry.

Security, Compliance, and IT Readiness

Regulatory responses require strong controls. Nomad Data maintains SOC 2 Type 2 compliance, supports SSO/SAML, encrypts data at rest and in transit, and provides full document‑level traceability. Page‑level citations and time‑stamped logs make responses defensible to auditors and examiners.

Doc Chat integrates with claim platforms and content systems you already use: Guidewire, Duck Creek, Sapiens, Origami Risk, OnBase, SharePoint, Box, SFTP, and data lakes. Teams can start with drag‑and‑drop uploads, then add deeper automation as comfort grows.

Sample Prompts a Claims Data Manager Can Use Today

Doc Chat is conversational. Instead of hunting through PDFs, ask questions the way you’d ask a colleague:

• “Automate DOI data call insurance: Build a CSV for the Texas DOI catastrophe data call with fields: claim number, peril, county, FNOL date, coverage decision date, total paid indemnity, total LAE, deductible applied, contents vs. dwelling paid, and page citations.”
• “AI pull data for insurance regulatory request: List all Auto claims closed with payment in 2024, by county, with rental days, total loss indicator, salvage proceeds, and police report number—include source pages.”
• “Quickly respond to insurance DOI document requests: Show open Commercial Auto claims over 180 days with current reserve, last reserve change date, SIU referral status, and litigation indicator—export to Excel.”
• “Create a pivot by peril and coverage part for Property claims, including ALE vs. building vs. contents splits, and highlight any files missing deductible application evidence.”
• “For Auto BI claims, summarize medical demand letters and IME results; extract CPT/ICD codes and provider TINs where present; attach citations.”

How This Works on the Documents You Actually Receive

Claims are messy. Doc Chat is built for messy. Whether your ISO claim reports arrive as text‑heavy PDFs, your FNOL forms vary by intake channel, or your loss run reports come from a TPA in spreadsheets with nested tabs, the agent normalizes and harmonizes the content. Medical reports and demand letters in Auto bodily injury? No problem—it crawls the full stack of exhibits, attachments, and correspondence, surfacing the values your DOI request needs with precise citations. Learn why “reading like an expert” is table stakes in our post, The End of Medical File Review Bottlenecks.

Real‑Time Q&A Across Thousands of Pages

When a regulator asks a follow‑up like, “Provide average time from FNOL to first payment by county, and highlight any outliers > 60 days,” Doc Chat re‑analyzes your response set instantly and returns the numbers with links back to the pages that justify each outlier. This is what our clients mean when they say reviews moved from days to minutes; see the GAIG case study recap: Reimagining Insurance Claims Management.

From Market Conduct Exams to MCAS: One Capability, Many Wins

While this article focuses on ad‑hoc DOI data calls, the same capability improves your Market Conduct Annual Statement (MCAS) production and market conduct exam prep. Because Doc Chat institutionalizes your definitions and captures edge‑case logic, it cuts preparation time every cycle and ensures your submissions remain consistent across lines and seasons. You’ll find a broader view of end‑to‑end claims automation in AI for Insurance: Real‑World AI Use Cases Driving Transformation.

Quantifying ROI for the Claims Data Manager

Consider a multi‑state catastrophe data call covering Property & Homeowners, with 12,000 claims and 2–5 PDFs per file. Historically, a team of analysts might need 3–4 weeks to read attachments, complete spreadsheets, and validate. With Doc Chat, the ingest and extraction run in hours, and the analyst time focuses on reviewing exceptions and approving exports. The result: 70–90% labor reduction on the worst part of the work, faster regulator turn‑around, and fewer escalations.

In Auto and Commercial Auto, savings compound when you include medical records and long demand packages. As documented in our experience piece, Reimagining Claims Processing Through AI Transformation, clients see multi‑order‑of‑magnitude speedups on multi‑thousand‑page files—without sacrificing accuracy.

Why Nomad Data Is the Best Choice for DOI Responses

Volume: Doc Chat ingests entire claim files—thousands of pages at a time—so you can answer regulators in hours, not weeks.

Complexity: The agent finds coverage and trigger language hidden in endorsements, aligns values across conflicting sources, and applies your rules consistently.

The Nomad Process: We train Doc Chat on your playbooks and definitions, delivering a personalized agent that mirrors how your Claims Data Manager wants responses built.

Real‑Time Q&A: Ask follow‑ups like a colleague would; get instant answers and citations.

Thorough & Complete: The agent surfaces every reference to coverage, liability, damages, and operational dates so nothing important slips by.

White‑glove delivery: Implementation typically completes in 1–2 weeks, including encoding your business rules and standing up secure integrations.

For a deeper dive into why most organizations underestimate this problem and how to solve it, see Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

Handling Regulator‑Specific Requests and Definitions

Doc Chat makes it easy to adapt to the idiosyncrasies of each state DOI. When one jurisdiction calls for “county of risk” and another requests “insured property county,” the agent aligns or separates based on your definitions. Need closed‑with‑payment to exclude $0 recoveries and include subrogation amounts only when received? The agent will follow your rule, document the logic, and cite the source pages behind each value.

Defensible Responses: Citations, Assumptions, and Change Logs

Regulators often ask, “Show me where you got that number.” Doc Chat’s exports include:

• Page‑level citations to the specific document(s) used to populate a field.
• Assumption annotations for edge cases (e.g., competing values across notes vs. invoices and how the winner was chosen).
• Change logs that record re‑runs, schema adjustments, and definition updates—so you can explain how and why a number changed.

That transparency builds trust with regulators and simplifies internal compliance reviews and audits.

Common Data Elements Doc Chat Standardizes for DOI Calls

To help Claims Data Managers plan ahead, here’s a non‑exhaustive list of fields Doc Chat standardizes across Property, Auto, and Commercial Auto:

• Core identifiers: Policy number, claim number, coverage part, insured, claimant.
• Key dates: Date of loss, FNOL date, coverage decision date, first payment date, reserve change dates, SIU referral date, subrogation demand and recovery dates.
• Financials: Paid indemnity, LAE/ALAE, reserves (initial/current/final), deductible amounts and application evidence, ACV vs. replacement cost, salvage proceeds, subrogation recoveries.
• Operational: Adjuster, TPA involvement, litigation indicator, complaint indicator, ISO claim report date, demand letter date, IME completion date.
• Location & risk: County, city, ZIP, CAT code, peril, vehicle details (VIN, total loss), driver/MVR indicators (commercial auto).
• Document‑derived: Contents vs. dwelling vs. ALE splits (property), CPT/ICD from medical reports (auto BI), police report numbers, repair estimate totals, rental days.

Scale to the Next Surge Without Hiring

Catastrophe seasons and legislative changes create spikes in DOI data calls. With Doc Chat, you can scale reviews instantly—no overtime or new hires. The agent reads at the same speed whether you have 50 claims or 50,000. For perspective on throughput at true enterprise scale, review The End of Medical File Review Bottlenecks.

From “Read Everything” to “Ask the File”

Doc Chat replaces rote reading with targeted, answer‑first workflows. Instead of opening PDFs, adjusters and analysts simply ask the system to summarize, extract, and calculate. This change mirrors the shift documented in our client stories, where question‑driven triage replaced line‑by‑line scanning—and won back days of productive time.

Frequently Asked Questions for Claims Data Managers

Q: Can Doc Chat use our existing loss run reports and enrich missing fields from attachments?
A: Yes. The agent ingests your loss run reports, identifies missing values, and fills them by reading claim notes, repair estimates, medical reports, ISO claim reports, police reports, and demand letters—with page citations.

Q: What if a regulator changes field definitions mid‑request?
A: Update the definition in Doc Chat and re‑run. The agent will rebuild your dataset and preserve a change log so you can explain and document the update.

Q: How do we avoid hallucinations?
A: Doc Chat is grounded in your documents. Every extracted value ties back to a source page; if a value isn’t present, the agent flags it for exception review instead of guessing.

Q: Can we integrate with our claim system and ECM?
A: Yes. Many teams start with drag‑and‑drop, then add API/SFTP connections to Guidewire, Duck Creek, Sapiens, Origami Risk, OnBase, SharePoint, Box, and data lakes.

Getting Started: A Simple Path to Your Next DOI Response

Here’s how Claims Data Managers typically launch their first data call automation:

1. Choose a recent or pending DOI data call request in Property, Auto, or Commercial Auto.
2. Provide representative claims files, policy records, and loss run reports (de‑identified if needed).
3. Walk Nomad through your current column definitions and “gotchas.”
4. Let Doc Chat build the first export with citations; review exceptions together.
5. Iterate once, finalize, and ship the regulator‑ready package.

Within two weeks, most teams shift from manual scramble to a repeatable, question‑driven process. As you scale to more states and lines, your library of rules grows—and responses keep getting faster.

The Bottom Line

DOI data calls will keep coming, and they will keep changing. With Doc Chat, a Claims Data Manager in Property & Homeowners, Auto, and Commercial Auto can automate the hardest parts: reading unstructured files, aligning inconsistent definitions, and proving every number with page‑level citations. You’ll automate DOI data call insurance responses, reliably AI pull data for insurance regulatory request needs, and quickly respond to insurance DOI document requests—while reducing cost, compressing cycle time, and increasing regulatory confidence.

See how fast your next response could be. Explore Doc Chat for Insurance and reimagine your regulatory workflow.