Streamline Regulatory Response in Property, Auto, and Commercial Auto: AI‑Powered Compilation of DOI Document Requests for DOI Response Coordinators

When a State Department of Insurance (DOI) issues a data call or launches a market conduct exam, the clock starts ticking for the DOI Response Coordinator. In Property & Homeowners, Auto, and Commercial Auto, the required fields span thousands of pages across claims files, policy records, loss run reports, and correspondence. Gathering the right facts, validating them, and packaging them into each states template is a high‑stakes, high‑pressure exercise. One missed exclusion, a mis-keyed loss amount, or an incomplete submission can prolong the examor worse, trigger penalties.

Nomad Datas Doc Chat eliminates the scramble. Doc Chat for Insurance is a suite of purpose-built, AI-powered agents that ingest entire claim files, policies, and loss runs, then instantly surface every field regulators requestwith page-level citations back to the source. For DOI Response Coordinators tasked with compiling complex datasets in Property & Homeowners, Auto, and Commercial Auto, Doc Chat transforms the process from weeks of manual work to minutes of defensible, audit-ready output.

The DOI challenge: complex, multi-source, and unforgiving

State DOIs request different data, in different formats, on different timelines. In Property & Homeowners, the focus might include catastrophe exposure, peril-level severity, claim reopen rates, salvage and subrogation recoveries, and counts/amounts by ZIP code or coastal county. Auto and Commercial Auto add their own wrinkles: bodily injury (BI), property damage (PD), med pay, PIP, total losses and ACV, rental days, diminished value, cargo losses, MCS-90 endorsements, and FMCSA/DOT incident linkages.

For a DOI Response Coordinator, the nuance isnt just the fieldsits the evidence. Requests commonly reference:

• Claims files (FNOL forms, adjuster notes, ISO claim reports, recorded statements, police reports, repair estimates, appraisal photos, medical bills for Auto, litigation correspondence, SIU notes)
• Policy records (declarations, limits and deductibles, forms/endorsements like HO-3, HO-5, HO-4, special provisions, named insured schedules, MCS-90, cargo endorsements for Commercial Auto)
• Loss run reports (ACORD formats or carrier-specific outputs with paid/indemnity/ALAE/ULAE, open/closed, reopen status, recovery details)
• Underwriting submissions (garaging ZIPs, safety programs, driver lists, VINs, radius of operation, property occupancy and construction details, protection class)

Add in TPA-administered files, scanned PDFs, variable state templates, and multi-jurisdictional requirements, and its easy to see why DOI Response Coordinators describe the work as relentless. Everything must reconcile: reserves vs. paid, policy limits vs. indemnity, deductibles applied vs. invoiced, catastrophe tags vs. peril codes, and moreall while proving provenance for every number.

How its handled manually today (and why thats risky)

Most carriers still tackle DOI data calls with spreadsheets, shared drives, and heroics. Even with modern claim systems, the critical details regulators ask for often live in unstructured documents or in inconsistent fields across vintages and lines. A typical manual cycle looks like this:

• Download state DOI data call requests and map requested fields to internal data sources.
• Extract claims-level metrics from the claims system, then hand-check the numbers against PDF claims files (adjuster notes, reserve changes, settlement letters, SIU referrals, litigation status).
• Open policy records to confirm the exact forms, endorsements, limits, deductibles, and effective dates in force as of loss date (e.g., HO-3 with a special wind/hail deductible; Auto BI/PD limits; Commercial Auto cargo sublimits).
• Pull loss run reports (ACORD or proprietary), then reconcile with claim detail to tie out paid vs. incurred indemnity and ALAE/ULAE; tag subrogation and salvage, recoveries, and reopen events.
• Manually compute time-to-FNOL, cycle times to coverage decision and payment, rental days (Auto), total loss indicators, and ACV calculations using appraisal and valuation documents.
• Standardize peril codes and loss causes for Property & Homeowners (e.g., wind/hail/hurricane/flood exclusions or endorsements), geo-tag by ZIP/county, and validate catastrophe coding.
• Request missing documents from TPAs or field teams, then iterateoften multiple timesto fill gaps, while maintaining a running audit trail.
• Paste values into a state-specific template or workbook, create footnotes for exceptions, and compile a binder of supporting exhibits for the examiner.

Even elite teams face unavoidable downside risk with this manual approach: long cycle times, high loss-adjustment expense, and inconsistent outcomes if one coordinator interprets a document differently from another. Fatigue creeps in; details get missed. In a market conduct exam, that can mean escalations, extended sample reviews, and reputational harm.

Automate DOI data call insurance with Doc Chat

Doc Chat changes the game for DOI Response Coordinators by reading like your most seasoned analystacross every page, at any volume. It is built precisely for situations where the answers arent neatly located in one table, but instead are scattered across thousands of pages and years of formats.

Heres what it does out of the box for Property & Homeowners, Auto, and Commercial Auto data calls:

• Ingests entire claim files (thousands of pages), policy records, and loss run reports at onceincluding scanned PDFs and mixed formats.
• Extracts every requested field from DOI templates and your internal mapping, from peril codes to BI/PD splits, ACV and total-loss flags, time-to-FNOL, and reserve/paid histories.
• Cross-checks claims figures against policy terms, endorsements, and declarations to confirm limits/deductibles align with paid/indemnity, and flags discrepancies.
• Normalizes terminology across lines, TPAs, and document styles (e.g., emergency board-up vs. temporary repairs; motor vehicle record vs. MVR) so you deliver consistent outputs.
• Builds the regulators workbook automatically and provides page-level citations and links back to the source page for every populated field.
• Provides real-time Q&A so you can ask: List all HO-3 policies with wind/hail deductibles over 2% in Harris County, or Show all Commercial Auto claims with MCS-90 implicated and cargo sublimits applied.
• Surfaces missing items and produces a document chase list (e.g., missing appraisals, police reports, endorsements, or medical bills) before you submit.

Under the hood, Doc Chat combines LLM-powered reading with carrier-specific playbooks so it follows your standards. As Nomad explains in Beyond Extraction: Why Document Scraping Isnt Just Web Scraping for PDFs, the real lift isnt just pulling valuesits applying unwritten rules to infer conclusions that arent located in a single field. Thats exactly what DOI data calls demand.

AI pull data for insurance regulatory request: purpose-built for evidence and speed

When examiners ask, Where did this number come from? Doc Chat answers with a link and a page number. That defensibility matters. It also means review attorneys and compliance leaders can validate outputs in minutes, not days. In fact, Nomad has documented cases where complex multi-thousand-page reviews are performed in seconds, as highlighted in Great American Insurance Group Accelerates Complex Claims with AI and The End of Medical File Review Bottlenecks (Doc Chat processes approximately 250,000 pages per minute).

Line-of-business nuances Doc Chat handles automatically

Property & Homeowners

State DOIs often want catastrophe segmentation (wind/hail/hurricane/wildfire), peril-level severities, coverage type (HO-3, HO-5, HO-4), sublimit triggers (e.g., mold), and deductibles by ZIP/county. They may request:

• Counts and incurred/paid amounts by peril and geography
• Time to FNOL and time to first payment
• Salvage, subrogation, and recovery offsets
• Reopen rates and supplement counts for contractor invoices
• Endorsements that modify wind/hail or named storm deductibles

Doc Chat extracts all of the above from policy records, loss runs, and claim file narratives; normalizes peril coding; ties out reserves vs. paid; and cites the policy form page showing the wind/hail deductible percentage.

Auto

Auto DOI requests frequently require BI/PD breakdowns, PIP/med pay application, total loss indicators, valuation basis (ACV), rental days, injury severity descriptions (from medical bills), and SIU referral flags. Doc Chat reads across:

• FNOL and police reports for causation, fault indicators, and impact location
• Appraisal/valuation documents for ACV and total loss status
• Rental invoices for rental days and rate reasonableness
• Medical bills and narratives for injury mapping (when requested)
• Litigation correspondence for filing dates, counsel, and status

The output delivers a regulator-ready datasetwith citations back to the exact page where the ACV or total-loss decision was recorded, or where PIP applied.

Commercial Auto

Commercial Auto layers on fleet dynamics, driver rosters, garaging practices, cargo endorsements, and sometimes MCS-90. Doc Chat:

• Extracts VINs, garaging ZIPs, radius of operation, and driver/MVR history from underwriting files
• Identifies MCS-90 and cargo sublimits in the policy stack
• Captures DOT/FMCSA references in loss files and reconciles them to claim events
• Aggregates claim counts and incurred/paid by vehicle class or operation type

If the DOI requests BI/PD segmentation by geography or driver role (employee vs. contractor), Doc Chat assembles it from underwriting submissions, declarations, and claim narratives.

Quickly respond to insurance DOI document requests: whats actually automated

With Doc Chat, DOI Response Coordinators dont just speed up the compilation. They eliminate blind spots and standardize their playbook across the team. Heres what the automation includes:

Document intake and normalization

Multiple file types are ingested at once: claims PDFs, images, spreadsheets, and email attachments. Doc Chat classifies, organizes, and stitches relevant sections, so endorsements and declarations line up with the right claim and loss date. If a loss run report doesnt reconcile with an adjusters note, the system flags the discrepancy.

Field extraction and validation

Doc Chat is trained on your data dictionary and DOI templates. It extracts the exact fields required (e.g., policy effective/expiration dates, limits/deductibles, coverage form, peril codes, claim status, reserve changes, paid amounts, recoveries, reopen flags, rental days, total loss status, ACV). It then validates consistency across sources: declarations vs. settlement letters vs. system exports.

Workbook assembly with citations

Outputs are produced directly into state-specific workbooks or your master templates. Every cell can reference the source page and document for defensibility, enabling fast review by compliance, legal, and external auditors.

Real-time Q&A across the entire corpus

In the midst of a regulator call, you can ask Doc Chat: Show all claims with HO-3 forms and hurricane deductibles above $5,000. Or, List Commercial Auto BI claims in Miami-Dade with paid indemnity over $50,000 and an MCS-90 implication. Answers arrive instantly with links to supporting evidence.

Business outcomes: faster, cheaper, more accurate, more defensible

Doc Chat is engineered for the core pains DOI Response Coordinators experience: speed, cost, accuracy, and defensibility. Across Property & Homeowners, Auto, and Commercial Auto, organizations see:

• Dramatic time savings. What previously took weeks of manual review drops to minutes. Nomad has documented thousand-page claim summaries in roughly a minute, with multi-thousand-page packages processed in seconds, as highlighted in Reimagining Claims Processing Through AI Transformation.
• Lower loss-adjustment expense and overtime. Coordinators and analysts focus on exceptions and strategy instead of re-keying values and reconciling PDFs.
• Consistency at scale. The same rules apply across desks and regions. This standardization is critical when different DOI reviewers ask similar, but not identical, questions.
• Defensible, regulator-friendly outputs. Page-level citations reduce back-and-forth and shrink the window for additional samples or re-requests.
• Proactive risk visibility. Hidden gaps and inconsistencies surface earlybefore submissionreducing the risk of errors that extend an exam.

Just as important, teams report better morale: specialists do high-value work (analysis, narrative context, strategy), not swivel-chair data entry. That translates to lower turnover and better institutional memory over time.

Why Nomad Data is the best partner for DOI Response Coordinators

Doc Chat isnt generic AI. Its purpose-built for insurance documentation and proven in production with claims teams who manage massive, complex files daily. A few reasons DOI Response Coordinators choose Nomad:

The Nomad Process: built around your playbooks

We train Doc Chat on your DOI data call mappings, policy stacks, loss run formats, and internal rules (for example, how your organization standardizes peril codes or calculates cycle-time metrics). We codify unwritten practices and institutionalize expertise, as described in Beyond Extraction. The result: a system that thinks like your best coordinator.

White glove service and a 1 2 week implementation

Doc Chat is a partner, not a toolkit. Nomads team stands up an initial workflow in 12 weeks, typically starting with a drag-and-drop interface and quickly layering in workbook outputs and system integrations. This mirrors the fast, low-friction rollouts featured in GAIGs case study.

Volume and complexity without headcount

Doc Chat ingests entire claim files and portfolio document sets in minutes, and it thrives on complexity (endorsements, exclusions, trigger language, and disparate templates). Reviews move from days to minutes, and surge events dont require overtime or new hires.

Real-time questions, page-level answers

Ask Doc Chat anything about the file setWhich claims were reopened? Where was the wind/hail deductible disclosed? Which Auto total losses used CCC vs. Mitchell?and get immediate answers with citations.

Security and defensibility

Nomad maintains enterprise-grade security controls, including SOC 2 Type 2. Doc Chat delivers document-level traceability and transparent reasoning so compliance, legal, and audit teams trust the results.

Detailed examples: how Doc Chat compiles DOI packages

Property & Homeowners: catastrophe and wind/hail deductibles

A coastal state DOI requests: claim counts and paid/indemnity by ZIP; hurricane-tagged claims with wind/hail deductibles; reopen rates; and salvage/subrogation totals. Doc Chat ingests policy declarations and endorsements (HO-3/HO-5), loss runs, adjuster notes, and catastrophe tags. It outputs the workbook with:

• Peril breakdowns normalized across files
• Deductible and sublimit application dates matched to loss date
• Reopen flags and supplement counts linked to invoice pages
• Salvage/subrogation recoveries tied to recovery letters

Every value is citation-backed. If an examiner asks, Show the page where the hurricane deductible is 2%, you click the link.

Auto: PIP application, total loss, and cycle times

A state DOI asks for a BI/PD breakdown with PIP application specifics, total loss identification, ACV method, rental days, and time-to-FNOL and time-to-first payment. Doc Chat reads police reports, FNOL forms, appraisals, valuation reports, rental invoices, medical bills, and claim notes to:

• Determine PIP application and benefit amounts
• Identify total losses and extract ACV method (ACC reports, CCC/Mitchell)
• Compute rental days from invoices and adjuster approvals
• Calculate cycle times from timestamped events

It then auto-populates the regulators workbook and flags any files missing rental invoices or valuation docs.

Commercial Auto: MCS-90 and cargo endorsements

A market conduct exam targets Commercial Auto policies with cargo exposures. The DOI seeks claims implicating MCS-90, cargo sublimits, and fleet garaging data. Doc Chat:

• Extracts MCS-90 presence from policy stacks and ties it to the claim list
• Finds cargo endorsements and applies sublimit logic by loss date
• Compiles VIN, garaging ZIP, and driver/MVR status from underwriting files
• Breaks down BI/PD by county and vehicle class

The output is a single, consistent dataset that spans underwriting, policy, and claim evidence.

Operations: what changes for the DOI Response Coordinator

With Doc Chat, the DOI Response Coordinator moves from playing search-and-reconcile to managing exceptions and narrative context.

Before submission, the coordinator reviews an exception summary: missing endorsements for three Property claims, unclear ACV method for one Auto total loss, and a cargo sublimit not explicitly referenced in an older Commercial Auto policy. The coordinator launches quick Q&A to verify each item, requests the missing exhibits, and adds short narratives where a policy change mid-term altered coverage.

The result: a clean workbook with hyperlinks to each source page and a short appendix explaining legitimate exceptions.

Accuracy without burnout: how Doc Chat sustains quality

Human accuracy falls as page counts rise; fatigue is real. AI doesnt fatigue. As documented in Nomads AI Transformation article, consistency improves as volume grows because the machine reads page 1,500 with the same attention as page 5. That matters in DOI work where a single overlooked endorsement can reverse a coverage determination and force resubmission.

Governance, compliance, and audit readiness

Regulators expect transparency. Doc Chat provides it:

• Page-level citations for every populated value
• Versioned outputs so you can show exactly what changed, when, and why
• Standardized rules that encode your best practices and reduce desk-to-desk variability
• Traceable exceptions and footnotes automatically appended to templates

This audit trail shortens examiner follow-up and decreases the chance of extended sample requests. Its also invaluable for internal model governancelegal, compliance, and audit can verify what Doc Chat extracted and how it made inferences based on your documented playbook.

Implementation: from pilot to production in 114 days

Nomads white glove approach meets DOI Response Coordinators where they are. Many teams begin with a drag-and-drop pilot using a historical data call; within days, the same workflow is producing the regulators workbook. Typical steps:

1. Discovery (2 3 hours). Share the most recent DOI data call, example workbooks, policy stacks, and representative claim files for Property & Homeowners, Auto, and Commercial Auto.
2. Playbook capture (1 2 days). We encode your field mappings, naming conventions, cycle-time logic, and exception rules.
3. Pilot configuration (3 5 days). Doc Chat ingests sample data, auto-builds your workbook, and adds citations.
4. Validation and tuning (2 3 days). Side-by-side review with your coordinators, compliance, and legal; we refine outputs to match your standards.
5. Production rollout (within 1 2 weeks). Expand to the full DOI request; optionally integrate with your claims system, ECM/DMS, or data warehouse.

This mirrors the fast adoption patterns described in GAIGs real-world experience: immediate productivity with minimal disruption.

FAQ for DOI Response Coordinators

Can Doc Chat handle scanned documents and messy PDFs?

Yes. It routinely processes mixed file types and scanned PDFs, and it is trained to normalize inconsistent terminology (e.g., HO forms, PIP, med pay, ACV, cargo sublimits) across carriers, TPAs, and document vintages.

Does Doc Chat support Automate DOI data call insurance use cases out of the box?

Yes. It is purpose-built to AI pull data for insurance regulatory request scenarios and can instantly quickly respond to insurance DOI document requests with citation-backed outputs. We load your state templates and immediately populate them from the ingested files.

How are security and privacy handled?

Nomad maintains enterprise-grade controls, including SOC 2 Type 2. Each answer is traceable back to the exact source page. Outputs can be retained in your environment per your retention policy.

Can it identify missing documentation?

Yes. Doc Chat produces a chase list (e.g., missing endorsements, valuation reports, rental invoices) and flags inconsistencies (e.g., paid amounts exceeding deductible logic, peril tags not aligning with policy language).

What if my states DOI changes the template?

Nomads team updates the mapping and presets quickly. Because Doc Chat is playbook-driven, changes are straightforward and dont require rewriting code or rebuilding rigid extractors.

Does it work for TPA-managed claims or multi-jurisdiction portfolios?

Yes. Doc Chat was designed for volume and variability. It normalizes across TPAs, regions, and years of policy forms and claim formats.

A coordinators checklist to get started

• Pick one recent state data call for Property & Homeowners, one for Auto, and one for Commercial Auto.
• Assemble 25100 representative claims per line with the complete file (FNOL, adjuster notes, ISO reports, police reports, appraisals, valuation documents, medical bills when applicable, settlement letters).
• Include policy stacks (declarations, endorsements, forms like HO-3/HO-5, MCS-90 for Commercial Auto, cargo endorsements).
• Provide sample loss run reports and any internal reconciliation sheets.
• Share your cycle-time definitions, peril coding standards, and exception handling rules.
• Define success criteria: submission time reduced by X%, exceptions resolved pre-submission, citation coverage target (e.g., 95%+ fields with page-level links).

Transforming regulatory response from bottleneck to advantage

DOI data calls will keep coming. The portfolios you manage will keep growing in volume and complexity, especially across Property & Homeowners, Auto, and Commercial Auto. The question is whether your organization will keep scaling manual effortor shift to a defensible, automated approach that empowers the DOI Response Coordinator to lead with speed and certainty.

Doc Chat by Nomad Data is built for this moment: the only way to reliably compile scattered facts from thousands of unstructured pages, validate them against policy language, and deliver regulator-ready outputs with citations in minutes. Its why leading carriers rely on Nomad to eliminate backlogs, standardize responses, and reduce risk. And its why your next DOI data call can be your teams fastest and cleanest submission yet.

Next steps

Ready to see how quickly you can automate DOI data call insurance workflows, AI pull data for insurance regulatory request templates, and quickly respond to insurance DOI document requests across Property & Homeowners, Auto, and Commercial Auto?

Schedule a short working session. Bring one recent DOI request and a small sample of files. Well load them into Doc Chat together and produce a citation-backed workbook live. From there, a 1 2 week rollout is typical for initial production use.

For more on how modern insurers are reimagining document-intensive tasks with AI, explore these resources:

• Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI
• Beyond Extraction: Why Document Scraping Isnt Just Web Scraping for PDFs
• The End of Medical File Review Bottlenecks
• AIs Untapped Goldmine: Automating Data Entry

The fastest, most defensible DOI responses arent produced by bigger teams. Theyre produced by smarter workflows. With Doc Chat, your next regulator package is minutes away.