Automating Rate Adequacy File Reviews for Underwriter Portfolio Audits — A Blueprint for the Actuarial Analyst

Actuarial Analysts face an increasingly urgent challenge: validating rate adequacy across large portfolios when the underlying data is buried in inconsistent in-force policies, sprawling endorsement schedules, and varied rating worksheets. The result is slow audits, sampling bias, and missed signals on where pricing is drifting from the company’s risk appetite. This is exactly where Nomad Data’s Doc Chat changes the equation. Built for high-volume, multi-format document analysis, Doc Chat performs an AI review of rate adequacy files at portfolio scale, surfacing every rating factor and endorsement you need for decision-ready actuarial analysis.

Instead of weeks of manual abstraction from policy folders, Doc Chat automates bulk policy review for rating factors across Property & Homeowners, General Liability & Construction, and Specialty Lines & Marine. It reads what humans read—dec pages, schedules, forms, binders, endorsements, rate notes—then extracts and normalizes the variables you care about. Ask natural-language questions like, “List all wind deductibles above 2% for coastal ZIPs” or “Show all accounts with CG 20 10 but missing CG 20 37” and get instant, citation-backed answers. Learn more about Doc Chat’s insurance capabilities here: Doc Chat for Insurance.

The Actuarial Nuance: Rate Adequacy Is Different by Line of Business

Rate adequacy reviews are not one-size-fits-all. Actuarial Analysts must interpret rating constructs across heterogeneous policy forms, endorsements, and rating manuals. The nuances multiply across three complex domains—Property & Homeowners, GL/Construction, and Specialty & Marine—each requiring different data points, forms, and validation logic.

Property & Homeowners

For Property & Homeowners books, the core questions focus on TIV (Total Insured Value), peril deductibles, construction and protection features, and catastrophe exposure:

• Documents: Dec pages (e.g., ISO HO-3), Statement of Values (SOV), schedule of locations, COPE data (Construction, Occupancy, Protection, Exposure), inspection reports, catastrophe (CAT) modeling outputs, loss run reports, endorsement schedules (e.g., wind/hail percentage deductibles, roof ACV endorsements), and commercial property forms (e.g., CP 00 10 Building and Personal Property Coverage Form; CP 10 30 Causes of Loss – Special Form).
• Rating variables: TIV by location, construction class, roof year and type, sprinkler status, fire protection (ISO PPC), distance to coast, secondary modifiers (shutters, roof attachments), coinsurance, AOP and wind/hail deductibles, business income limits, occupancy.
• Actuarial checks: Are wind deductibles aligned with coastal exposure? Are CAT loadings reflected in written premium? Are coinsurance penalties correctly modeled in the indicated premium? Do SOVs reconcile with dec page limits and rating worksheets?

General Liability & Construction

Across GL and construction risks, the rating truth lives in exposure bases, class codes, and AI endorsements (especially Additional Insured and Completed Ops). The downstream loss cost depends on getting these right:

• Documents: ISO CG 00 01 Commercial General Liability Coverage Form, CG 20 10 and CG 20 37 Additional Insured endorsements, waiver of subrogation endorsements, schedule rating worksheets, payroll and receipts documentation, subcontractor cost schedules, OCIP/CCIP wrap-up documentation, experience modification factors for related lines, and endorsement schedules.
• Rating variables: Class codes, payroll/receipts/subcontractor costs, number of employees, project types, geographic exposure, AI and primary/non-contributory endorsements, residential vs. commercial split, claims-made vs. occurrence triggers for relevant specialty extensions, limits and aggregates.
• Actuarial checks: Are subcontractor costs correctly broken out and surcharged? Where AI endorsements materially change exposure, is premium adequate? Do schedule debits/credits align with UW guidance? Are residential exposures properly identified and priced?

Specialty Lines & Marine

Specialty and Marine portfolios hinge on operational details and warranties living within bespoke wordings, slips, and schedules:

• Documents: Hull & Machinery policies, P&I certificates, ocean cargo policies and declarations, project cargo schedules, inland marine floaters, valuation clauses, trading warranties, class society inspections, lay-up warranties, loss run reports, and broker slips.
• Rating variables: Vessel age/tonnage, class status, trading area, cargo type and stowage, accumulation limits, conveyance splits, theft/temperature-control warranties, lay-up terms, deductible structures, valued policy amounts.
• Actuarial checks: Do declared trading areas match rate assumptions? Are lay-up warranties enforced? Are accumulation management and peak limit controls reflected in portfolio rate? Are large deductible structures properly captured in indicated relativity?

How the Manual Process Works Today (and Why It Breaks at Scale)

Most actuarial portfolio audits still rely on manual abstraction from large folders of PDFs, spreadsheets, and email attachments. Even with strong analysts, the process is slow and prone to sampling bias.

Typical steps:

1. Gather: Download policy files from the policy admin system or data lake, often spanning dec pages, endorsements, rating worksheets, SOVs, and correspondence.
2. Classify: Manually separate in-force policies, binders, endorsements, and renewals; identify which documents are authoritative.
3. Extract: Open each file and transcribe key fields into spreadsheets—limits, deductibles, AI forms (CG 20 10 vs CG 20 37), payroll/receipts, wind %. Use VLOOKUPs or macros to reconcile across locations and forms.
4. Normalize: Map divergent form names and custom endorsements to standard actuarial variables, creating fragile crosswalks that break with each new endorsement variant.
5. Validate: Spot-check against underwriting guidelines and prior terms; reconcile SOV totals to dec page TIV; cross-check deductibles and coinsurance in rating worksheets against endorsements.
6. Analyze: Build pivot tables or GLM inputs; estimate indicated relativity vs written; produce heat maps by territory, class, or peril.

Where it fails:

• Volume vs. time: Books with thousands of policies and dozens of pages per policy force sampling. Important outliers are missed.
• Inconsistent forms: The same concept (e.g., roof condition) appears in many formats. Manual crosswalks degrade after every product update or broker-specific endorsement.
• Human fatigue: Accuracy drops after dozens of files. Endorsements that materially change exposure are easy to overlook at page 300.
• Latency: By the time a portfolio audit finishes, the renewal cycle has moved on. Corrective rate actions arrive late.

Doc Chat Automates the Hardest Parts of Rate Adequacy Reviews

Doc Chat by Nomad Data replaces the repetitive steps of reading, extracting, normalizing, and cross-checking, delivering a defensible, portfolio-wide view in minutes—not weeks. Instead of treating this as simple OCR, Doc Chat uses purpose-built agents trained on your rating playbooks and endorsement conventions to perform a true AI review of rate adequacy files across your full portfolio.

What Doc Chat does out of the box:

• Bulk ingestion and classification: Drag-and-drop or connect to S3, SharePoint, or a data lake to ingest entire policy libraries—in-force policies, dec pages, endorsement schedules, rating worksheets, SOVs, slips, binders, and correspondence.
• Precise extraction at scale: Pulls all rating variables you define (TIV, COPE, wind %, coinsurance, AI endorsements, payroll/receipts/subcosts, vessel age/tonnage, trading areas, warranties), with page-level citations back to source documents.
• Normalization and crosswalks: Harmonizes synonyms and custom endorsements to your standard actuarial schema. “Loss settlement – roof surfacing ACV” and “Roof surfacing limited ACV endorsement” land in the same field.
• Rules and reason codes: Applies your rating rules (e.g., coastal wind minimums, subcontractor surcharge triggers, lay-up warranty compliance). Flags exceptions with explicit reason codes for auditability.
• Portfolio outputs: Generates structured datasets (CSV/Parquet), Excel summaries, dashboards, and line-of-business extracts for modeling in R/Python, Radar/Earnix, or BI tools.
• Real-time Q&A: Ask, “Which GL accounts show CG 20 10 at inception but missing CG 20 37 at completion?” or “List cargo accounts with refrigeration warranties but temperature claims in the loss run,” and get instant answers with source citations.

Doc Chat isn’t a generic summarizer. As discussed in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, true document intelligence requires inference across inconsistent files, plus your unwritten rules. Doc Chat captures those rules—your best analysts’ judgment—and operationalizes them consistently at scale.

Purpose-Built for Each Line of Business

Property & Homeowners: From SOVs to Wind Deductible Adequacy

Doc Chat reads CP 00 10, CP 10 30, ISO HO-3, and carrier-specific forms, extracting location-level TIV, construction class, roof year, sprinklers, protection class (ISO PPC), and deductible structures (AOP and wind/hail percent). It reconciles SOV totals with dec page limits and validates CAT-zone alignment against stated wind percentages and minimums. It flags misalignments like high coastal exposure with sub-minimum wind deductibles or coinsurance mismatches relative to values reported on SOVs. Outputs include:

• Portfolio view of TIV by territory, occupancy, and construction with wind % distributions
• Exception lists for undervalued locations or missing roof updates/inspection evidence
• Endorsement coverage diffs showing where roof surfacing ACV language impacts indicated premium

General Liability & Construction: Endorsement Rigor and Exposure Accuracy

For GL/Construction, Doc Chat pulls class codes, payroll, receipts, and subcontractor costs and detects AI endorsements like CG 20 10 and CG 20 37, plus primary/non-contributory and waiver of subrogation clauses. It validates whether residential exposures are flagged (and priced) where required, and it identifies accounts with extensive subcontractor usage but missing surcharge factors. The system also reconciles schedule credits/debits against underwriting guidance and flags out-of-band credits. Outputs include:

• Exposure base reconciliation (payroll/receipts/subcosts) by class and territory
• AI/PN/Waiver endorsement presence, effective dates, and gaps across the policy term
• Residential exposure flags and required rating surcharges by state/regulatory regime

Specialty Lines & Marine: Trading Warranties and Accumulation Awareness

Doc Chat extracts vessel characteristics (age, tonnage, class society), trading areas, lay-up warranties, deductible structures, valued limits, and cargo type/stowage requirements. It reconciles accumulation limits and identifies temperature-control warranties that conflict with loss runs showing spoilage. For project cargo and inland marine floaters, it checks valuation clauses and monitoring requirements vs. declared exposures. Outputs include:

• Warranties and exclusions index with portfolio-level compliance scores
• Accumulation map highlighting exposed peaks by port or transit corridor
• Exception lists for lay-up noncompliance or trading beyond rate assumption

From Documents to Decisions: End-to-End Actuarial Data Products

Doc Chat doesn’t stop at extraction. It turns messy policy documents into consistent, analysis-ready data, then packages the insights for actuarial workflows:

• Structured exports: CSV/Parquet feeds for Snowflake/Databricks; Excel for rapid desktop analysis; JSON APIs for direct ingestion to rating platforms (e.g., Guidewire, Duck Creek) or actuarial tools.
• Indication scaffolding: Optional calculations to pre-compute indicated vs. written relativities based on your rate plan and rating worksheets, with transparent reason codes and links to source pages.
• Interactive audit views: Each field includes a citation to the exact page and paragraph; reviewers click to verify the source in seconds.
• Exception-first dashboards: Rank policies by severity of misalignment (e.g., CAT exposure with sub-minimum wind %), so analysts prioritize where corrective action pays off most.

Business Impact: Time, Cost, and Accuracy

Doc Chat’s impact compounds across the portfolio. By automating the heavy lifting, your team moves from manual reading to judgment and action.

Expected outcomes for an Actuarial Analyst team:

• Time savings: Portfolio reviews that previously took weeks compress to hours. As highlighted in our client story with Great American Insurance Group, document answers that took days to find now surface in seconds, with page-level citations (read the GAIG experience).
• Cost reduction: Fewer manual touchpoints and less overtime for rate audits. Analysts cover more of the book without adding headcount, enabling broader and more frequent audits.
• Accuracy and completeness: The system reads every page of in-force policies and endorsement schedules with consistent rigor, eliminating fatigue-driven misses. Exception lists come with reason codes and citations for rapid validation.
• Speed to action: Earlier detection of drift (e.g., underpriced coastal exposures, missing subcontractor surcharges) enables mid-year corrections, not just next-year fixes.
• Morale and retention: Analysts spend less time transcribing and more time modeling and advising underwriting—exactly the work they were hired to do.

Why Nomad Data: The Best Partner for Portfolio-Scale Rate Adequacy

Nomad Data’s Doc Chat is not another generic “document AI.” It’s a suite of insurance-focused agents engineered for the complexity and scale of carrier-grade portfolios:

• Volume without new headcount: Ingest entire policy libraries—thousands of files and tens of thousands of pages—so audits move from sampling to full-pop analyses.
• Complexity mastery: Endorsements, exclusions, triggers, and warranties often hide in dense, inconsistent language. Doc Chat finds and normalizes them, so your analysis reflects the coverage that actually exists.
• The Nomad Process: We train Doc Chat on your playbooks, rating worksheets, and endorsement conventions. Your unwritten rules become consistent, scalable workflows.
• Real-time Q&A: Ask portfolio-wide questions and get instant answers—with citations—so your team iterates quickly and defensibly.
• White-glove service: Our experts partner with your actuarial and underwriting leaders to define fields, rules, and outputs. We deliver the solution, not a toolkit you need to assemble.
• Fast implementation: Typical implementations go live in 1–2 weeks, starting with drag-and-drop uploads and progressing to API integrations as needed.
• Security and governance: SOC 2 Type II controls and page-level explainability provide audit-ready transparency across every field extracted and every decision made. See more context on the operational rigor in AI’s Untapped Goldmine: Automating Data Entry.

How Doc Chat Delivers an AI Review of Rate Adequacy Files

Here’s how an AI review of rate adequacy files works from kickoff to actionable output:

1. Scope the variables and rules: Together we define the portfolio, documents to ingest (in-force policies, endorsement schedules, rating worksheets, SOVs, inspection reports), and the exact fields and rules that drive adequacy (e.g., minimum wind %, subcontractor surcharge triggers, lay-up compliance).
2. Bulk ingestion: Drop your files into Doc Chat or connect a repository. The agent classifies documents and prepares extraction pipelines.
3. Extraction and normalization: Doc Chat reads every page and populates a standardized schema, mapping variants to your canonical variables.
4. Rules and exceptions: The agent applies your adequacy checks with reason codes and page citations. Exception lists are ready for reviewer action.
5. Portfolio outputs: Receive Excel and CSV extracts, BI dashboards, and an API feed if needed. Outputs align to actuarial tooling for immediate modeling.
6. Iterate via Q&A: Ask follow-ups—“Which GL policies show residential exposure but no schedule debit?”—and get instant answers linked back to sources.

Examples of Bulk Policy Review for Rating Factors (by LOB)

Property & Homeowners

Doc Chat can execute a bulk policy review for rating factors such as:

• TIV by location with roof year/type and wind % by distance-to-coast band
• Coinsurance presence and percentage matched to SOV adequacy
• Roof surfacing ACV endorsement presence and effective dates vs renewal terms
• CAT model factor presence and alignment with premium relativities
• Protection class (ISO PPC) and sprinkler status, including missing inspection evidence

General Liability & Construction

Typical extraction includes:

• Class codes, payroll, receipts, and subcontractor cost splits
• AI endorsements (CG 20 10, CG 20 37), primary/non-contributory, and waivers
• Schedule rating credit/debit details vs. UW authority limits
• Residential exposure indicators and state-specific surcharges

Specialty Lines & Marine

Common variables extracted:

• Vessel year/tonnage, class society status, and trading areas
• Lay-up warranties, navigation limits, and valued limits
• Cargo types, stowage requirements, and temperature-control warranties
• Accumulation controls and peak location checks

Integrations and Data Flow

Doc Chat fits into your data ecosystem without disruption. Start with file uploads. Then connect to policy admin and data platforms as you scale:

• Policy platforms: Guidewire, Duck Creek, Sapiens
• Data lakes/warehouses: S3, Azure Blob, SharePoint, Snowflake, Databricks
• Actuarial/analytics: R/Python notebooks, Emblem/Radar, Earnix, Power BI/Tableau

The system’s explainability—page-level citations for every extracted field—supports model governance and rate-filing evidence. That level of defensibility mirrors lessons from complex-claim environments where GAIG teams validated Doc Chat’s accuracy on thousands of pages.

Governance, Controls, and Audit Readiness

Rate adequacy decisions are regulatory-grade. Doc Chat provides the governance features your auditors expect:

• Traceable lineage: Each field references back to exact page-and-paragraph citations.
• Change management: Versioned extraction schemas and rules; controlled updates to endorsement mappings.
• Security posture: SOC 2 Type II controls; least-privilege access; encrypted data in transit and at rest.
• Human oversight: Analysts review exceptions and approve outputs before downstream use—machines assist, humans decide.

From Reactive Audits to Proactive Portfolio Steering

With Doc Chat, actuarial work moves from reactive clean-up to proactive steering:

• Quarterly adequacy sweeps: Full-book reviews identify drift early—before renewal season.
• Targeted remediation: Exception lists direct underwriting actions (endorsement corrections, deductible changes, exposure clarification).
• Filing-ready evidence: Transparent extraction plus rule explanations create a defensible backbone for filings and internal reviews.

Why “Beyond Extraction” Matters for Rate Adequacy

Rate adequacy hinges on implicit logic—the rules experienced analysts carry in their heads. As we outlined in Beyond Extraction, the work isn’t finding fields; it’s inferring intent from scattered signals and applying your institution’s unwritten conventions. Doc Chat codifies those conventions through collaborative interviews and iteration, then enforces them consistently at portfolio scale. The result: fewer misses, faster cycles, better rate integrity.

Implementation: 1–2 Weeks to Value

Doc Chat is designed for quick wins:

1. Week 1: Define scope, ingestion approach, key variables, and exception rules. Provide a sample of policies across in-force policies, endorsement schedules, and rating worksheets.
2. Days 5–10: First-pass extraction delivered with citations; review exceptions and fine-tune mappings/rules.
3. End of Week 2: Portfolio outputs, dashboards, and Q&A live. Optional API/warehouse integration if needed.

We provide white-glove service throughout—our insurance specialists co-create the playbook, validate early outputs with your analysts, and adjust rapidly. As your documents evolve, Doc Chat evolves with them.

Getting Started: A Fast Pilot for Bulk Policy Review of Rating Factors

For an initial bulk policy review for rating factors pilot, we recommend:

1. Select 1,000–5,000 policies across the target LOB(s) with representative endorsement diversity.
2. Provide rating and adequacy rules (e.g., minimum wind deductibles by ZIP band; subcontractor surcharge thresholds; lay-up warranty checks).
3. Ingest documents (drag-and-drop or repository connection). Include in-force policies, dec pages, endorsement schedules, rating worksheets, SOVs, slips, and loss runs.
4. Review outputs—exception lists, structured extracts, and Q&A. Validate top exceptions via links back to the source pages.
5. Scale up to the full book and add continuous monitoring (monthly/quarterly sweeps).

Teams that start here typically move from sampling to full-pop adequacy, catching issues that would otherwise slip through. For broader AI opportunities across insurance, explore AI for Insurance: Real-World Use Cases.

FAQ: What Actuarial Analysts Ask Us Most

How does Doc Chat handle carrier-specific endorsements?

We map your proprietary endorsements to canonical actuarial variables. The mapping is transparent, versioned, and auditable. When brokers introduce a new variant, Doc Chat learns it and updates the crosswalk so downstream analytics remain stable.

Can Doc Chat compute indications?

Yes—if you provide rating worksheets or rules, Doc Chat can pre-compute indicated relativities vs. written premium and supply reason codes. Many clients prefer Doc Chat to deliver the structured inputs and apply indication logic in their actuarial models; we support either path.

How do we ensure governance and trust?

Every extracted field includes a page citation. Analysts can click through to the exact wording. SOC 2 Type II controls and role-based permissions protect sensitive documents and outputs. You remain in control; Doc Chat assists, you decide.

What about non-policy documents, like inspections or loss runs?

Doc Chat ingests inspection reports, loss runs, CAT model outputs, and broker correspondence. These sources often drive adequacy exceptions (e.g., missing roof updates or conflicting trading areas) and are critical to a complete review.

Conclusion: Make Rate Adequacy a Continuous Advantage

Rate adequacy reviews do not have to be slow, manual, or reactive. With Doc Chat, Actuarial Analysts can execute an AI review of rate adequacy files across entire portfolios and perform true bulk policy review for rating factors with page-level defensibility. The payoff is not just efficiency—it’s better decisions, earlier corrections, and a portfolio that continuously aligns with your risk appetite.

See how quickly you can go from documents to defensible insights. Explore Doc Chat for Insurance and turn portfolio audits from a bottleneck into a strategic advantage.