Automating Rate Adequacy File Reviews for Underwriter Portfolio Audits - Property, GL/Construction, and Specialty & Marine

Automating Rate Adequacy File Reviews for Underwriter Portfolio Audits - Property, GL/Construction, and Specialty & Marine
At Nomad Data we help you automate document heavy processes in your business. From document information extraction to comparisons to summaries across hundreds of thousands of pages, we can help in the most tedious and nuanced document use cases.
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Automating Rate Adequacy File Reviews for Underwriter Portfolio Audits - Property, GL/Construction, and Specialty & Marine

Rate adequacy review is one of the toughest, most resource-intensive responsibilities on a Portfolio Manager’s plate. Books swell with years of in‑force policies, mid‑term endorsements, individual rating worksheets, and ad‑hoc underwriting notes. Every coverage tweak, deductible change, or classification adjustment can erode premium adequacy over time—yet confirming the impact across thousands of policies is still largely manual. The result: slow audits, incomplete reviews, and leakage that quietly drags down profitability.

Doc Chat by Nomad Data solves this problem at portfolio scale. Purpose‑built for insurance, Doc Chat is a suite of AI agents that ingest entire claim and policy files—thousands of pages at a time—to extract rating factors, endorsements, and coverage triggers, then assemble audit‑ready outputs in minutes. For Portfolio Managers overseeing Property & Homeowners, General Liability & Construction, and Specialty & Marine lines, Doc Chat delivers a repeatable, defensible AI review of rate adequacy files that transforms quarterly and annual portfolio audits from weeks of effort into a fast, data‑driven workflow.

The Portfolio Manager’s Challenge: Rate Adequacy at Scale

Rate adequacy deteriorates when exposure realities drift from what rating worksheets assumed, or when coverage crept via endorsements without corresponding premium adjustments. A Portfolio Manager must verify whether today’s premium reflects current risk—across a heterogeneous set of policies, historical versions, and bespoke endorsements. The issue compounds across lines: Property & Homeowners policies carry COPE and catastrophe considerations; GL & Construction hinge on class codes, payroll/receipts, additional insureds, and completed operations; Specialty & Marine involve complex clauses and unique perils, often spanning global exposures.

Traditional sampling misses systemic issues, and spreadsheets rarely capture the nuance buried in PDF attachments—especially when the PDF contains the only authoritative source of truth (dec pages, endorsement schedules, binder addenda, and signed rating worksheets). Meanwhile, filing seasons and reinsurance negotiations demand clear, portfolio‑level answers, not anecdotes. You need automation that reads policies like an experienced underwriter and assembles the data your actuarial partners and underwriting managers can trust.

Nuances by Line of Business for Portfolio Managers

Property & Homeowners

Property adequacy hinges on the alignment among COPE, coverage forms, deductibles, and cat perils:

  • Key documents: In‑force policies and dec pages (e.g., ISO CP 00 10 Building and Personal Property), Causes of Loss (CP 10 30), Ordinance or Law (CP 04 05), endorsement schedules, Statement of Values (SOV), inspection reports, rating worksheets, catastrophe modeling outputs, and schedule rating memos.
  • Critical rating factors: Construction type, occupancy, protection class (ISO PPC), age of roof and updates, sprinkler/alarms, secondary modifiers, territory codes, TIV by location, coinsurance, deductible amounts (AOP, Wind/Hail named storm percentage), valuation basis (RC/ACV), special sublimits (debris removal, ordinance or law), and schedule credits/debits.
  • Common adequacy drifts: SOV inflation lag; unrecognized ordinance or law exposure; wind/hail deductible reductions; builder’s risk extensions not priced; and location‑level endorsements that alter named storm exposure without premium re‑rating.

Assessing whether today’s rate reflects these parameters requires reading across dec pages, SOV granularity, endorsement stamps, catastrophe terms, and rating memos—which are rarely uniform across a portfolio.

General Liability & Construction

Rate adequacy in GL and construction is sensitive to classification and contractual risk transfer:

  • Key documents: In‑force policies (ISO CG 00 01), endorsement schedules (e.g., CG 20 10 and CG 20 37 additional insured endorsements; CG 21 47 Employment‑Related Practices Exclusion; CG 24 26 Amendment of Insured Contract Definition), certificates of insurance (COIs), subcontractor agreements, payroll/receipts audits, rating worksheets, and loss run reports.
  • Critical rating factors: ISO/NAICS class codes, exposure bases (payroll, receipts, units), territory, premises/operations vs. products/completed operations splits, products hazard, employee count, subcontractor percentage and controls, stop‑gap exposures, and schedule rating variables.
  • Common adequacy drifts: Class misassignment; completed operations broadened via endorsement without rating recognition; additional insured proliferation (project‑specific endorsements) compressing price; subcontractor controls weakened vs. underwriting guidelines; payroll/receipts growth not captured midterm; and experience/schedule rating slippage.

Confirming whether endorsements align with pricing requires precise extraction of form codes, AI status, waiver/primary and noncontributory language, and completed ops applicability—across multiyear policy histories.

Specialty Lines & Marine

Specialty and marine programs present bespoke wording, global perils, and floating exposures:

  • Key documents: Marine cargo policies (Institute Cargo Clauses A/B/C), hull & machinery (American Institute Hull Clauses), war & strikes endorsements, open cargo declarations, warehouse‑to‑warehouse terms, surveyed valuations, P&I riders, manuscript endorsements, dec pages, schedules of locations/ports, and rating worksheets.
  • Critical rating factors: Conveyance type, routing/geographies, storage time, values at risk in transit vs. static, theft‑attraction commodities, war risk locations, high‑risk ports, deductible structures, single and aggregate limits, and sublimits for theft, temperature deviation, or delay.
  • Common adequacy drifts: Route changes to higher‑risk corridors; increased aggregation at warehouses; sublimits increased via manuscript endorsements; per‑occurrence deductibles lowered; valuation terms broadened; and terrorism/war clauses added midterm.

Here, adequacy requires reconciling manuscript language against pricing models—work that’s nearly impossible using manual sampling and spreadsheets alone.

How the Process Is Handled Manually Today

Most Portfolio Managers rely on a patchwork of sampling, Excel templates, policy admin extracts, and PDF hunts:

  • Identify a subset of in‑force policies for quarterly or annual review (often 5–10% due to time constraints).
  • Open each policy PDF and endorsement schedule, locate the dec page, and scrape key data points into a spreadsheet.
  • Compare extracted rating factors with the rating worksheet (deductibles, class codes, exposure base, schedule credits/debits).
  • Scan endorsement schedules to confirm form codes and effective dates (e.g., CG 20 10 vs. CG 20 38), checking whether pricing recognized each material change.
  • For Property, reconcile SOV and COPE details with valuation basis and cat deductibles; for Marine, reconcile route and storage endorsements to rating memos.
  • Flag discrepancies for underwriters and actuaries; repeat request cycles for missing pages or misfiled worksheets.

This is slow, brittle, and inconsistent. Document structure varies across brokers and years. Endorsement names differ by carrier, and policy versions are scattered across email, shared drives, and imaging systems. Manual reviewers get fatigued, and crucial details slip—particularly in big books or after seasonal surges. By the time results arrive, the risk profile may have shifted again.

How Nomad Data’s Doc Chat Automates Rate Adequacy File Reviews

Doc Chat ingests full portfolios—policies, endorsement schedules, rating worksheets, SOVs, inspection reports, and even broker correspondence—and performs a comprehensive, explainable analysis. The AI agents are trained on your playbooks and rating guidelines, so they extract exactly the factors your team cares about.

What it does out of the box:

  • Bulk ingestion and classification: Load tens of thousands of policy and endorsement PDFs at once. Doc Chat classifies documents (dec page, endorsement schedule, rating worksheet, SOV) and versions them by policy and effective date.
  • Rating factor extraction: Pulls construction type, occupancy, protection class, territory, deductibles, limits/sublimits, coinsurance, valuation basis (RC/ACV), class codes, payroll/receipts, subcontractor percentages, AI/waiver/PNC language, and more.
  • Endorsement mapping: Normalizes form codes across variability (e.g., CG 20 10, CG 20 37, CG 21 47; CP 04 05; wind/hail deductible endorsements; manuscript marine riders) to a canonical dictionary and links each to the policy’s rating factors and effective dates.
  • Cross‑checks to rating worksheets: Compares endorsements and exposures to the signed rating worksheet, flagging gaps (e.g., completed ops broadened but no corresponding rate load; ordinance or law added without premium change).
  • Portfolio outputs: Generates a spreadsheet or data feed that actuaries and underwriters can pivot immediately—by territory, class, peril, endorsement type, deductible band, or COPE attribute—with page‑level citations back to the source PDF.
  • Real‑time Q&A: Ask natural‑language questions like “List all Florida homeowners policies with named storm deductibles below 2% and TIV over $2M” or “Show construction GL policies with CG 20 10 and CG 20 37 added midterm but no schedule rating change.” Get answers with instant links to supporting pages.

Unlike generic tools, Doc Chat handles the messy reality of insurance documents—where the signal is spread across dec pages, riders, binders, and footnotes. It performs inference, not just extraction, connecting the dots between wording changes, exposure shifts, and pricing assumptions. For a deeper explanation of this difference, see Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

Use Case Deep Dives

Property & Homeowners: Adequacy Across COPE and Cat Terms

Upload your in‑force policies, SOVs, endorsement schedules, and rating worksheets. Doc Chat:

  • Extracts key COPE elements and aligns them with valuation and deductibles from the dec page.
  • Normalizes wind/hail/named storm endorsements and identifies any midterm reductions to percentage deductibles.
  • Flags missing or mismatched ordinance or law terms (CP 04 05) relative to schedule ratings and replacement cost assumptions.
  • Surfaces TIV growth at the location level not reflected in rating—especially when SOV updates were emailed but not re‑rated.

Output: a portfolio‑level table with columns for construction, protection class, territory, valuation basis, deductibles by peril, cat endorsements, O&L presence, TIV change vs. last rating worksheet, and page‑level citations. This becomes your single source to quantify premium drift and rate actions by cohort.

GL & Construction: Class, Exposure, and Contractual Risk Transfer

Doc Chat reads GL policies and endorsement schedules, extracting class codes, exposure base (payroll/receipts), additional insured and waiver language, completed operations applicability, and primary/noncontributory wording. It then reconciles these with the policy’s rating worksheet and underwriting guidelines.

Typical AI flags include:

  • Completed operations AI (CG 20 37) added post‑bind without updated premium.
  • Subcontractor percentage cresting above guideline with no credit reversal.
  • Primary and noncontributory requirements added for multiple upstream parties or project‑specific endorsements but unchanged schedule rating.
  • Class code drift from light to heavy construction without payroll changes captured midterm.

The result is a clean, bulk policy review for rating factors at scale, giving Portfolio Managers a clear heat map of where GL adequacy is eroding and why.

Specialty & Marine: Manuscript Endorsements and Global Exposures

For cargo and hull, Doc Chat identifies Institute Clauses (A/B/C), war/strikes coverage, valuation terms, route and storage language, theft‑attraction sublimits, and manuscript riders. It ties these to rating worksheets and exposure declarations to surface adequacy issues, for example:

  • War risks added for named geographies without corresponding load.
  • Shift to warehouse‑to‑warehouse terms with extended storage and reduced deductibles.
  • High‑theft commodities added to the description of goods without revised pricing.

Outputs let you segment by route, port, storage duration, commodity, and clause—again, all with citations back to the precise policy page.

AI Review of Rate Adequacy Files: Example Prompts and Outputs

With Doc Chat, Portfolio Managers can interrogate entire books instantly. Sample prompts:

  • “Across Property, list policies where CP 04 05 (Ordinance or Law) is present but schedule credits remained unchanged year‑over‑year.”
  • “Find GL construction policies with CG 20 10 and CG 20 37 endorsements effective midterm and no change to completed ops rating.”
  • “Show homeowners policies in ISO PPC classes 7–10 with named storm deductibles below 2% and TIV above $2M.”
  • “In Marine Cargo, list policies that added war risk clauses for Red Sea or Black Sea routing but retained prior deductibles.”

For each query, Doc Chat returns structured rows (policy number, effective date, factor/endorsement, discrepancy, recommended action) with clickable citations to the source pages. This is an AI review of rate adequacy files that your actuarial partners can validate in minutes.

Business Impact: Time, Cost, Accuracy, and Profitability

Doc Chat converts a month‑long adequacy audit into a morning’s work—and raises the quality bar while doing it.

Illustrative impact for a 10,000‑policy book:

  • Time savings: A manual review at ~45 minutes per file is 7,500 hours of reading and data entry. Doc Chat completes extraction and cross‑checks in under a day, with Portfolio Managers spending time only on exceptions.
  • Cost reduction: Eliminating manual review hours, overtime, and external audit vendors can reduce review costs by 50–80% while enabling 100% book coverage (no more risky sampling).
  • Accuracy and consistency: The AI never tires. It applies the same rules across every policy, increasing consistency and reducing missed endorsements or mis‑read rating elements. Page‑level citations make results defensible to regulators, reinsurers, and internal audit.
  • Premium lift and leakage control: Rapidly target under‑rated cohorts (e.g., low wind deductibles in high‑cat zones; broadened completed ops AI in construction) and reprioritize midterm endorsements and renewal actions. Many carriers see immediate premium recapture and better combined ratio outlooks.

For context on scale and performance improvements in complex file reviews, see the real‑world throughput examples in The End of Medical File Review Bottlenecks and the quantifiable speed/accuracy gains described in Reimagining Claims Processing Through AI Transformation. Although those pieces focus on claims, the same platform powers Doc Chat’s policy and rate adequacy workflows.

Why Portfolio Managers Choose Nomad Data

Rate adequacy auditing requires more than OCR. It demands context, inference, and policy‑specific judgment encoded in software. Nomad Data delivers that through a combination of technology and white‑glove partnership:

  • Volume: Ingest entire books—policies, endorsement schedules, rating worksheets, SOVs—so reviews move from days to minutes.
  • Complexity: Doc Chat finds exclusions, endorsements, and trigger language hidden in dense policy packets, aligning them with your rating playbooks.
  • The Nomad Process: We train the AI on your underwriting guidelines, rating worksheets, and portfolio audit rules, producing outputs that match your exact templates.
  • Real‑time Q&A: Ask any question about endorsements, deductibles, class codes, or exposure changes and get instant, cited answers across the whole book.
  • Thorough & complete: No sampling required. Every in‑force policy is reviewed consistently, eliminating blind spots and leakage.
  • Your partner in AI: Beyond software, Nomad provides a dedicated team to co‑create solutions and evolve with your needs.

Security and auditability are first‑class citizens: SOC 2 Type II controls, least‑privilege access, and transparent citations for every output. If your portfolio workflow needs seamless integration, Doc Chat connects to policy admin systems and data warehouses with modern APIs.

Implementation: White‑Glove, 1–2 Week Timeline

Doc Chat’s implementation is designed for quick value:

  • Week 1: Intake your sample policies, endorsement schedules, rating worksheets, and audit templates. We configure extraction “presets” for Property & Homeowners, GL & Construction, and Specialty & Marine and align them to your dictionaries (e.g., endorsement code variations).
  • Week 2: Validate outputs on a subset of your book. Review exceptions and refine rules. Turn on portfolio‑scale processing. Optional API connections to policy admin or data lake.

Teams can begin using Doc Chat day one via drag‑and‑drop while IT finalizes integrations—mirroring the rapid adoption pattern described by GAIG in Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI.

How Doc Chat Translates Tribal Knowledge Into Repeatable Rules

Veteran underwriters carry nuanced rules in their heads: how to interpret manuscript wording, when an AI/waiver changes risk, what SOV shifts imply. Doc Chat helps capture and operationalize those unwritten rules. Our team interviews your experts, encodes that logic, and tests iteratively—creating an institutional memory that standardizes reviews across the portfolio. This is precisely the gap outlined in Beyond Extraction: true value comes from inference and institutional knowledge, not field scraping alone.

Bulk Policy Review for Rating Factors: From Data Entry to Decision Intelligence

Many adequacy reviews stall on “data entry”—copying exposures and endorsements into a spreadsheet before any analysis can begin. Doc Chat eliminates this bottleneck with high‑fidelity extraction and validation, turning raw PDFs into structured, analysis‑ready data in minutes. For why this step is a goldmine for ROI, see AI’s Untapped Goldmine: Automating Data Entry. The output isn’t just a table—it’s decision intelligence with citations so Portfolio Managers can act confidently.

Governance, Compliance, and Human Oversight

Rate adequacy audits are high‑stakes. Doc Chat’s design keeps humans in the loop. Portfolio Managers retain decision authority; Doc Chat assembles the evidence—fast, thoroughly, and consistently. Every field in the output is traceable to a source page, which supports regulator and reinsurer queries. We recommend periodic rule audits and sign‑offs by underwriting leads to ensure fairness and compliance with rate filings and market conduct standards—an approach aligned with our guidance in Reimagining Claims Processing Through AI Transformation.

Frequently Asked Questions for Portfolio Managers

Can we run 100% of our in‑force policies, not just samples?
Yes. Doc Chat is built for portfolio‑scale ingestion so you can replace sampling with complete coverage.

How do we handle endorsement variability?
Doc Chat normalizes endorsement codes to your canonical dictionary and highlights out‑of‑vocabulary forms for human review.

What about outdated rating worksheets or missing pages?
Doc Chat detects missing components (e.g., a rating worksheet referenced but not present) and flags files for remediation. It can also surface exposure proxies from policy language until the worksheet is recovered.

How fast can we be live?
Most teams are live in 1–2 weeks with white‑glove onboarding. Users can start with simple drag‑and‑drop on day one while integrations are finalized.

Will actuaries trust the outputs?
Outputs are fully cited to the source PDF page. Actuarial teams can validate spot checks rapidly and scale their analytics without manual re‑keying.

From Portfolio Visibility to Action

Doc Chat doesn’t just reveal where adequacy is drifting; it organizes cohorts for action—by territory, peril, class, endorsement set, or deductible bands. Portfolio Managers can push prioritized worklists to underwriters, recommend midterm endorsements or rate actions, and support filings with evidence‑based narratives. Because the data is structured, you can also connect it to BI tools for trending premium adequacy over time and stress‑testing scenarios ahead of reinsurance negotiations or market cycles.

Get Started

If your quarterly or annual adequacy review relies on sampling, spreadsheets, and long nights in PDF readers, it’s time to modernize. Run a pilot on a meaningful slice of your book and see how quickly you can move from document hunting to decision‑making.

Learn more about Doc Chat for Insurance or explore broader real‑world applications in AI for Insurance: Real‑World AI Use Cases Driving Transformation. Your next rate adequacy file review doesn’t have to take weeks—and it shouldn’t.

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