Automating Rate Adequacy File Reviews for Underwriter Portfolio Audits — Property & Homeowners, General Liability & Construction, Specialty Lines & Marine

Rate adequacy audits are mission critical for a Portfolio Manager. Yet the reality across Property & Homeowners, General Liability & Construction, and Specialty Lines & Marine is the same: too many in‑force policies, too many endorsements, and too many rating worksheets to review by hand. Endorsement drift, missing surcharges, and inconsistent application of filed rating factors hide inside dense, unstandardized documentation. Backlogs grow, and remediation lags behind exposure changes.

Nomad Data’s Doc Chat for Insurance changes that. Doc Chat is a suite of purpose‑built, AI‑powered agents that ingests entire books of business, reads in‑force policies, endorsement schedules, and rating worksheets at scale, extracts every relevant rating factor, and flags where current premium doesn’t match indicated rate. With real-time Q&A and portfolio-level analytics, Portfolio Managers can move from manual sampling to comprehensive, defensible rate adequacy reviews across every policy on the books.

Why Portfolio Managers Struggle With Rate Adequacy in Today’s Market

As a Portfolio Manager, you are accountable for profitable growth and portfolio quality across multiple lines. Market conditions change quickly—contracting markets in some geographies, inflation in repair/rebuild costs, weather volatility, social inflation and litigation trends in GL & Construction, and navigational and supply chain shifts in Marine. Rate filings, rating worksheets, and endorsements evolve, but in-force policies often lag behind. A portfolio that looked adequate six months ago can drift out of tolerance without continuous, granular review.

Complicating matters, rate adequacy isn’t dictated by a single field on a declarations page. It’s the composite of nuanced factors buried throughout the policy file:

Property & Homeowners: COPE data, protection class (ISO PPC), construction/occupancy, roof age/material, distance to coast, wildfire hazard scores, replacement cost basis, catastrophe peril deductibles (e.g., wind/hail %, named storm), ordinance or law limits, schedules of locations, and endorsements like HO 04 20 (Other Structures), HO 04 61 (Scheduled Personal Property), and special sub-limits.
General Liability & Construction: ISO class codes, operations descriptions, payroll/receipts, subcontractor exposure and certificates, additional insured requirements, primary/non‑contributory and waiver of subrogation endorsements, per-project aggregate, residential exclusions, action-over exclusions, CG 00 01 coverage terms, CG 20 10/CG 20 37 AI forms, CG 24 04 waivers, CG 22 94/95 subcontractor endorsements, wrap-up/OCIP/CCIP exceptions, completed ops triggers.
Specialty Lines & Marine: Hull/Cargo/P&I limits and deductibles, navigational warranties, lay-up and trading warranties, crew count, declared TIV, inland marine equipment schedules (installation/builder’s risk/equipment floater), testing coverage, delay in start-up (DSU), catastrophe accumulations at ports or yards, Institute Cargo Clauses, AAIS and ISO inland marine schedules.

Each factor can influence territory relativities, debits/credits, catastrophe loads, or rating surcharges. Without automation, verifying that all of those elements are reflected correctly in the rating worksheet for each in-force record is prohibitively slow—which is why many organizations resort to small samples and hope that patterns generalize. That’s a risky assumption in volatile markets.

How Rate Adequacy Reviews Are Handled Manually Today

Many Portfolio Managers describe a manual, spreadsheet-driven process built around scattered files and tribal knowledge:

Collect documents: Pull in-force policies, endorsement schedules, and rating worksheets from the policy admin system, file share, or email. Supplement with ACORD apps (ACORD 125, 126, 140), schedule of locations/equipment, and when evaluating profitability, loss run reports and ISO claim reports. For construction risks, retrieve subcontractor certificates and contracts to validate subcontractor factors.
Normalize and read: Open policy PDFs, compare declarations with schedule attachments, then check endorsements for additional insured, per-project aggregate, changes to perils, or deductible adjustments. For property, validate COPE details against the worksheet; for marine, confirm navigational and lay-up warranties. For GL, verify class codes, operations, and basis (payroll/receipts).
Reconcile to rating worksheet: Manually check whether each factor in the worksheet matches the policy and endorsements. For example, confirm hurricane deductible percent matches the dec page and territory table, that roof age credit matches evidence, and that AI/waiver endorsements have the appropriate surcharges applied.
Compute indicated versus written: Using actuarial guidance or a rating manual, recompute the indicated premium by applying correct relativities, territory factors, CAT loads, and surcharges. Compare to written premium; flag variances beyond tolerance thresholds.
Roll up to portfolio insights: Export findings to spreadsheets or BI tools, segment by geography/class code/occupancy/build type/navigational area, and prepare a remediation plan (endorsement cleanup, renewal uplift strategies, mid-term corrections, or filing refinements).

This process can require dozens of hours per segment and still only scratch the surface of the book. Seasonal surges or a large acquisition can make comprehensive review impossible without adding headcount or delaying decisions. Human fatigue introduces inconsistency—common rating elements get missed, especially in 1,000+ page policy files with multiple mid-term endorsements.

AI Review of Rate Adequacy Files: How Doc Chat Automates the Entire Workflow

Doc Chat was designed for precisely this kind of high-volume, high-variability document work. It ingests complete in-force policy files (including binders and mid-term endorsements), endorsement schedules, and rating worksheets—often thousands of pages per file—then extracts exactly the rating data a Portfolio Manager needs for a defensible rate adequacy review.

Here’s how it works end-to-end:

Bulk ingestion and classification: Drag-and-drop entire books of business or connect to your DMS/ECM to stream policy PDFs, spreadsheets, and emails. Doc Chat automatically classifies document types (dec pages, CP 00 10, CP 10 30, CG 00 01, HO 3/HO 5, Institute Cargo Clauses, endorsement forms), and associates mid-term endorsements to the correct policy term.
Rating factor extraction: The agent reads declarations and endorsements to surface all rating-relevant data: COPE, PPC, occupancy, roof age, deductible structures, territory codes, AI/waiver/primary non-contributory endorsements, per-project aggregate, subcontractor usage, navigational warranties, crew counts, and equipment schedules. It also reads rating worksheets and cross-checks that every factor present in the policy package appears—and matches—on the worksheet.
Real-time Q&A across the portfolio: Ask questions like, “List all Property policies within 5 miles of the coast with wind/hail deductibles below 2%,” or “Show GL policies with CG 20 10 and waiver of subrogation but without the associated surcharge.” Doc Chat returns answers with page-level citations you can click to verify instantly.
Indicated vs. written comparison: Using your company’s rating playbooks and tolerance thresholds, Doc Chat computes indicated premiums from the extracted factors, compares them against written premium, and flags under- or over-collections. You can export results to spreadsheets or push them into BI/portfolio systems.
Exception handling and auditability: Every finding includes citations back to the source page(s), creating a defensible audit trail for internal review, regulators, reinsurers, or rating agencies.

Because Doc Chat is trained on your playbooks, forms, and filing nuances—the Nomad Process—the system mirrors your rules, not generic market defaults. It scales to ingest entire portfolios in hours, not quarters, turning rate adequacy from a point-in-time exercise into an ongoing rhythm.

Bulk Policy Review for Rating Factors: What Doc Chat Extracts by Line of Business

Property & Homeowners

For Homeowners and Commercial Property schedules (e.g., CP 00 10 Building and Personal Property Coverage Form, CP 10 30 Causes of Loss – Special Form, CP 12 32 Wind/Hail Deductible), Doc Chat pulls and normalizes:

COPE data (Construction, Occupancy, Protection, Exposure), ISO PPC, BCEGS
Roof material/age, sprinkler and alarm details, distance to hydrant/station, distance to coast
Territory codes, ISO or proprietary
Replacement cost method and valuation basis, coinsurance
Catastrophe deductibles by peril (wind/hail, named storm, hurricane), earthquake/flood endorsements
Ordinance or Law coverage limits
Endorsements: HO 04 20, HO 04 61, special sublimits, scheduled items
Schedule of locations and TIV rollups

Doc Chat confirms that each element is reflected in the rating worksheet, applies your filed relativities, and highlights variances between indicated and written premium.

General Liability & Construction

For ISO GL and construction risks (CG 00 01 and related endorsements), Doc Chat extracts:

ISO GL class codes and operations descriptions
Rating basis (payroll, receipts, area, units) with annualization logic
Subcontractor exposure, hold-harmless clauses, and certificates
Additional insured endorsements (CG 20 10, CG 20 37), primary/non‑contributory, waiver of subrogation (CG 24 04)
Per-project aggregate, residential exclusions, action-over, independent contractors exclusions (CG 22 94/95)
Wrap-up/OCIP/CCIP participation and exceptions

It checks whether AI/waiver/per-project endorsements and subcontractor utilization are properly surcharged, identifies missing or mismatched factors, and flags accounts where the worksheet basis or classes don’t match the policy narrative or operations.

Specialty Lines & Marine

For Marine and Inland Marine schedules (e.g., Institute Cargo Clauses, AAIS/ISO inland marine forms, contractors’ equipment floater, installation/builder’s risk):

Hull, Cargo, and P&I limits/deductibles
Navigational and trading warranties, lay-up periods, crew counts
Equipment and cargo schedules, testing coverage, DSU/soft costs
Port accumulation exposures and catastrophe aggregates

Doc Chat reconciles navigational/lay‑up warranties and deductibles with the rating worksheet and ensures the correct navigational zone relativities and accumulation loads are applied.

The Nuance Portfolio Managers Need: From Policy Text to True Rate Drivers

Rate adequacy isn’t only about what’s printed on a declarations page. The drivers often hide in endorsement language and attachments. A few common examples Doc Chat is designed to surface:

Property: A mid-term endorsement lowered the wind/hail deductible but the surcharge never updated; roof replacement documentation was added without changing the roof age factor; ordinance or law limit increased at renewal without corresponding debit.
GL & Construction: A blanket additional insured endorsement plus a primary/non-contributory requirement is present, but the AI/waiver surcharges are missing; payroll basis excludes subcontractor labor; per-project aggregate endorsement is in force, but aggregate factor remains at policy-level default.
Specialty & Marine: A new trading warranty expands into higher-risk waters but rating doesn’t reflect the zone change; crew count increased and P&I limits were endorsed up mid-term, while premium basis lagged.

Doc Chat connects those dots automatically, at scale, and with page-level proof so remediation is fast and defensible.

From Sampling to 100% Review: Business Impact for Portfolio Managers

Moving from manual sampling to AI‑driven, 100% portfolio review drives tangible outcomes:

Time savings: Doc Chat ingests and analyzes entire books—thousands of policy files and rating worksheets—within hours. Adjusters and underwriters no longer spend days reconciling files. In The End of Medical File Review Bottlenecks, Nomad demonstrates throughput on the order of hundreds of thousands of pages per minute; the same underlying platform powers policy/rating analysis for underwriting portfolios.

Cost reduction: Eliminating manual re-keying and reconciling lowers loss-adjustment and administrative expenses. Teams redeploy from spreadsheet cleanup to strategy and remediation. Our experience mirrors the outcomes in AI’s Untapped Goldmine: Automating Data Entry, where automation delivered rapid ROI by replacing repetitive document work at enterprise scale.

Accuracy and leakage control: Fatigue and inconsistency vanish. Doc Chat applies your rules the same way every time, surfacing mismatches, missing surcharges, and territory mis-assignments that drive premium leakage. Lessons from Reimagining Claims Processing Through AI Transformation carry over: consistent extraction and page-level explainability reduce error rates and make audits faster and safer.

Portfolio agility: When a catastrophe model updates or a filing changes, you can re-run the entire book in minutes to quantify impact and target remediation. That’s impossible with manual, sample-based methods.

Doc Chat in Practice: Examples of Questions Portfolio Managers Ask

Doc Chat’s real-time Q&A lets you interrogate your book like a data warehouse—without waiting for a data engineering sprint:

“Show all Homeowners policies with PPC > 6, roof age > 15 years, and wind/hail deductible < 2%.”
“List GL policies with CG 20 10 and waiver of subrogation where no AI/waiver surcharge is present on the worksheet.”
“Identify Marine hull policies that expanded navigational warranties in the last 12 months without corresponding zone relativity changes.”
“Which Property policies have increased Ordinance or Law limits mid-term without an updated debit?”
“Provide a CSV of per-project aggregate endorsements and the applied aggregate factor by policy.”

Doc Chat answers with citations to the exact endorsements, schedules, or worksheets where each factor appears, so you can verify immediately and take action.

How Doc Chat Fits Into Your Existing Workflow

Doc Chat is purpose-built to meet insurance teams where they are:

Flexible ingestion: Drag-and-drop files, point to S3/SharePoint/ECM, or integrate via API. The platform handles PDFs, scanned documents, spreadsheets, and emails.
Schema‑on‑use extraction: Doc Chat is trained on your filing language and worksheets, not just generic templates. It adapts to carrier-specific formats and endorsements.
Structured outputs: Export to CSV/Excel, stream to data lakes/warehouses, or push to your portfolio analytics tools. Create dashboards on top of Doc Chat outputs to monitor rate adequacy continuously.
Audit trails and explainability: Every extracted value is traceable to the source page. That transparency makes model governance and regulatory reviews straightforward.

Why Nomad Data’s Doc Chat Is the Best Solution for Portfolio-Level Rate Adequacy

Built for volume and complexity: Doc Chat ingests entire claim or policy files—thousands of pages per file—and remains reliable at portfolio scale. It is engineered for the messy, inconsistent reality of insurance documentation, a capability unpacked in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs. Your rating factors rarely sit in a neat table; Doc Chat finds them wherever they are, and infers what’s missing based on your rules.

The Nomad Process and white-glove service: We train Doc Chat on your playbooks, rate manuals, and worksheets. Our hybrid team of underwriting domain experts and AI engineers conducts working sessions to encode unwritten rules—the “if‑this‑then‑that” heuristics that live in senior underwriters’ heads. You get a personalized agent aligned to your book, not a generic tool. Expect a white-glove experience and rapid, collaborative iteration.

Speed to value: Most teams are live in 1–2 weeks for initial use cases. Start with drag‑and‑drop; integrate with core systems later. We’ve repeatedly seen adoption surge after a single hands-on session, mirroring outcomes in GAIG’s AI claims transformation, where page-level citations built instant trust.

Security and governance: Nomad Data maintains enterprise-grade security controls (including SOC 2 Type 2). Document-level citations provide defensibility for regulators, reinsurers, and internal audit. Data residency and access controls align with your IT policies.

What About Claims Data and Profitability Context?

While the core of a rate adequacy file review is the policy/rating comparison, Portfolio Managers often round out the picture with loss data. Doc Chat can ingest loss run reports, bordereaux, and ISO claim reports, then align those data with the extracted rating factors. That lets you:

Quantify frequency/severity by factor cohort (e.g., roof age bands, GL class codes, navigational zones).
Compare indicated rate relativities to empirical loss experience for credibility-weighted refinement.
Prioritize remediation where loss emergence and under-collection intersect.

This alignment turns Doc Chat into a single lens for underwriting and profitability, not just documentation hygiene.

Putting It Together: A Sample Portfolio Remediation Sprint

Imagine a Property & Homeowners portfolio across three coastal states showing CAT loss pressure and reinsurance cost increases. The Portfolio Manager runs Doc Chat across all in-force policies and rating worksheets:

Doc Chat extracts wind/hail deductibles, distance to coast, PPC, roof age/material, and territory codes; it reconciles the latest mid‑term endorsements.
It flags policies with deductibles below filed minimums for their territories; identifies roof age credits taken without documentation; and surfaces territory miscoding.
It computes indicated vs. written premium deltas by cohort and exports a ranked remediation list.
The team pushes mid-term endorsement corrections where permissible, and readies renewal increase strategies for the highest-leakage cohorts.
Within two weeks, the portfolio shows a measurable uplift in indicated collection—without adding staff.

Run the same playbook for GL & Construction (AI/waiver/per‑project surcharges; subcontractor factors) and Specialty & Marine (navigational zones; lay‑up warranties; crew changes). Doc Chat’s repeatable workflow standardizes rate adequacy across your enterprise.

Addressing Common Concerns: Implementation, Accuracy, and Change Management

Implementation: Start simple. Upload a representative sample of policies and worksheets and validate the extraction against known answers. Doc Chat’s citations make spot-checking fast. Expand to full books of business once trust is built. Most teams transition to API integration in weeks, not quarters.

Accuracy and “hallucinations”: Doc Chat reads and extracts from your actual documents and returns cited evidence. When the task is “find this field and show me where it came from,” large language models are exceptionally reliable—especially when tuned to your playbooks. The platform is engineered to avoid unsourced assertions.

Change management: Treat Doc Chat like an elite junior analyst that never gets tired. The human stays in the loop—reviewing exceptions, setting thresholds, and making final calls. Over time, you’ll codify more of your tacit rules into the agent, standardizing best practices across desks and regions.

Measuring ROI for Rate Adequacy Automation

To quantify value, Portfolio Managers commonly track:

Coverage of book reviewed: Increase from sampled audits to 100% of policy terms.
Leakage recapture: Indicated vs. written deltas corrected through endorsement cleanup and renewal adjustments.
Cycle-time reduction: Weeks or months of manual review reduced to hours with portfolio-wide re-runs after filing changes or model updates.
Audit and compliance efficiency: Time saved generating evidence for internal audit, regulators, or reinsurers, thanks to page-level citations.
Talent leverage: Underwriters and analysts shift from data wrangling to strategy, improving retention and throughput.

These gains mirror the broader automation benefits documented in Nomad’s articles on AI for Insurance and data entry automation.

Technical Highlights That Matter to Portfolio Managers

Cross-document reconciliation: The agent triangulates data across dec pages, schedules, and endorsement forms to derive the authoritative value for each rating factor.

Normalization and mapping: It maps carrier- or MGA-specific terminologies to your standard factor taxonomy (e.g., aligning proprietary territory codes or marine zones to rating tables). For GL, it normalizes class code descriptions and automatically flags multi‑class exposures missing from worksheets.

Portfolio queries at scale: You can run complex multi-factor filters on the entire book and export structured results instantly, enabling targeted remediation campaigns.

Explainable outputs: Page-level citations maintain trust with underwriting leadership, actuaries, reinsurers, and auditors.

Expanding Beyond Rate Adequacy: Adjacent Portfolio Use Cases

Once Doc Chat is in place for rate adequacy, Portfolio Managers often extend it to:

Policy wording audits: Surface exclusions and endorsements that drive unexpected accumulations or litigated claim patterns.
Reinsurance readiness: Produce evidence packages for treaty negotiations—accumulation profiles, deductible distributions, endorsement penetration, and remediation plans.
M&A diligence: Read all policies in a target book, extract risk drivers, and score rate adequacy pre-close.
Continuous portfolio surveillance: Re-run the book weekly or monthly as filings, models, or market conditions change.

Getting Started: A 1–2 Week Path to Portfolio-Level Insight

Here’s a proven path to value:

Discovery (Days 1–3): Share representative in-force policies, endorsement schedules, and rating worksheets across Property & Homeowners, GL & Construction, and Specialty & Marine. Provide your rating playbooks and tolerance thresholds.
Configuration (Days 3–7): Nomad trains Doc Chat on your rules, endorsements, and worksheet formats. We define output schemas for indicated vs. written comparisons and cohort analyses.
Pilot (Days 7–10): Run Doc Chat on a subset of the book, validate extractions with underwriting and actuarial reviewers, and fine-tune edge cases.
Scale (Week 2): Expand to full portfolios. Stand up dashboards and remediation worklists. Optionally integrate via API with your DMS/ECM and BI stack.

Because Doc Chat is available from day one via drag-and-drop, teams begin seeing answers in hours, not months. Integration can follow after trust is established—this was a key adoption lesson in our GAIG case study noted in the webinar replay.

Your Next Best Step

If “AI review of rate adequacy files” and “bulk policy review for rating factors” are on your roadmap, the fastest way to evaluate impact is a live test on your policies and worksheets. Bring five to ten representative accounts from each line of business. Ask Doc Chat the hardest questions you can think of. Verify results on the page. Then scale to the full book.

Rate adequacy is too important to leave to samples and spreadsheets. With Doc Chat, Portfolio Managers in Property & Homeowners, General Liability & Construction, and Specialty Lines & Marine can operationalize continuous, comprehensive portfolio review—closing leakage, accelerating decisions, and building resilient, profitable books in any market.

Appendix: Representative Documents and Forms Doc Chat Handles

Property & Homeowners: Declarations, CP 00 10, CP 10 30, CP 12 32, HO 3/HO 5, HO 04 20, HO 04 61, schedule of locations, valuation reports, catastrophe deductible endorsements, inspection reports.
GL & Construction: CG 00 01, CG 20 10, CG 20 37, CG 24 04, CG 22 94/95, per-project aggregate endorsements, subcontractor agreements and certificates, OCIP/CCIP documentation, ACORD 125/126.
Specialty & Marine: Hull/Cargo/P&I policies, Institute Cargo Clauses, navigational warranties, lay-up warranties, inland marine equipment schedules, builder’s risk/installation floater forms.
Cross-portfolio context: Rating worksheets, underwriting guidelines, bordereaux, loss run reports, ISO claim reports, and renewal correspondence.

Every extraction is accompanied by page-level citations, enabling transparent, defensible reviews that withstand internal and external scrutiny.

Note: For a deeper dive into the engineering and change-management principles that make this possible, explore Nomad’s thought leadership: Beyond Extraction, AI’s Untapped Goldmine, and AI for Insurance: Real‑World Use Cases.