Unlocking Cross-Carrier Prior Losses in Submission Documents for Property & Homeowners, Commercial Auto, and General Liability & Construction — A Risk Analyst’s Playbook

Risk Analysts across Property & Homeowners, Commercial Auto, and General Liability & Construction face the same stubborn challenge on every new submission: piecing together an accurate, comparable picture of historical losses from a stack of loss run reports, broker submissions, and claims history attachments originating from multiple prior carriers and TPAs. Different formats, conflicting paid/incurred figures, varying deductibles or self-insured retentions, and gaps in coverage periods make it hard to see what really happened—especially when submissions mask gaps or omit high-severity claims. That lack of clarity undermines underwriting decisions, pricing, and portfolio risk selection.

Nomad Data’s Doc Chat is specifically built to fix this. As a suite of purpose-built, AI-powered agents for insurance, Doc Chat ingests entire submission packets, compares prior-carrier loss runs across time and entities, normalizes key metrics, and instantly highlights undisclosed gaps or repeated large losses that drive loss ratio deterioration and leakage. If you’ve been searching for “AI compare prior carrier loss runs” or ways to “automate undisclosed loss detection underwriting,” this is your blueprint.

The Risk Analyst’s Challenge: Why Prior-Carrier Loss Reconciliation Is So Hard

For Property & Homeowners, Commercial Auto, and General Liability & Construction, prior losses are central to selection, pricing, and retention strategy. Yet they’re buried across inconsistent documents: scanned PDFs, spreadsheets, broker-prepared summaries, and terse email addenda. In the real world, no two carriers structure loss runs the same, and older policy years often hide behind name changes, mergers, and DBA variations. Risk Analysts have to navigate:

Line-of-Business nuances that complicate comparison:

Property & Homeowners: Cat vs non-cat attribution, water vs fire trend separation, roof age and construction type shifts, changing deductibles (flat vs percentage wind/hail), prorated catastrophe allocations, and differences in what carriers include as ALAE. Schedules of Values (SOVs) evolve, risk profiles migrate, and peril codes differ by carrier.
Commercial Auto: Fleet growth or contraction, driver turnover, garaging shifts, and mixed policy structures. Loss runs may blend liability and physical damage, or split by unit/VIN with inconsistent cause-of-loss coding. MVR issues may not be clearly tied to loss events, and subrogation/recovery handling can vary.
General Liability & Construction: Claims-made vs occurrence confusion, completed operations tail, wrap-up (OCIP/CCIP) versus practice policies, subcontractor indemnification interplay, and construction defect long-tail emergence. One carrier might roll ALAE into incurred; another breaks it out. Retro dates and aggregate eroding retentions complicate comparability.

Across all three lines, seemingly minor differences—like whether reserves are carried as case vs bulk, or whether claims are summarized at the claim or occurrence level—can materially change the picture. Unspotted coverage gaps, unreported high-severity incidents, or recurring claim patterns (hail, rear-end collisions, slip-and-falls) can skew projected loss picks, burn pricing credibility, and degrade portfolio performance.

How the Process Is Handled Manually Today

Most teams still brute-force the work. A Risk Analyst pulls the prior five years of loss run reports and claims history attachments from multiple carriers or TPAs, and then spends hours reconciling them with the broker submission. The manual steps are tedious and error-prone:

Convert scanned PDFs to text, manually key loss totals into spreadsheets, and reclassify cause-of-loss codes into a standard taxonomy.
Reconcile policy periods, renewals, mid-term endorsements, and entity name changes (DBAs, mergers, FEIN updates).
Normalize incurred, paid, and outstanding reserves, including whether ALAE is included, excluded, or split; separate indemnity versus expense when possible.
Map deductibles or SIRs per year, per peril, and distinguish aggregate erosion versus per-claim application; adjust for cat vs non-cat in Property.
Identify missing months/years in the timeline and chase brokers or insureds for the absent carrier years.
Cross-check the loss runs against supporting documents like ACORD forms (125/126/140), SOV spreadsheets, driver schedules, OSHA 300/300A logs, Certificates of Insurance (COIs), MVR summaries, FNOL references, ISO ClaimSearch reports, and broker emails.
Summarize by policy year, loss cause, severity band, and frequency trend; produce a narrative of recurring patterns and open large claims.

This takes days for a complex account—longer if you must reconcile wrap-ups in construction or separate Auto Liability from Physical Damage. It also burns time on follow-up for clarifications, reopening spreadsheets after fresh documents arrive, and re-running the math. Meanwhile, quote deadlines don’t move, and the pressure to make a call grows.

What Makes Cross-Carrier Loss Comparison Hard for Software

Traditional OCR or keyword-based tools excel when fields are consistent and predictable. Prior loss runs are the opposite: formats change by carrier and year; key facts are scattered across tables, free text, footnotes, and adjuster narratives. As explained in Nomad Data’s perspective on the complexity of document inference, the problem is not just extraction—it’s reasoning across messy inputs to produce normalized, decision-ready insight. For a deeper dive into why this is more than “PDF scraping,” see Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

In practice, Risk Analysts don’t just read; they infer unstated business rules. They figure out whether a $500k reserve is an outlier or a near-certainty, whether a frequency trend is noise or a leading indicator, and when missing months are actually a hint of undisclosed carrier movement. Encoding that nuanced, cross-document reasoning is what most off-the-shelf tools fail to do.

How Nomad Data’s Doc Chat Automates Cross-Carrier Loss Run Comparison

Doc Chat by Nomad Data solves this end to end. It ingests entire submission packages—loss run reports, broker submissions, claims history attachments, plus supporting schedules—and applies a series of purpose-built, insurance-trained agents to read, reconcile, and reason about the content like a top-performing Risk Analyst would. Key capabilities include:

Volume: Ingests thousands of pages at a time, including mixed PDFs, spreadsheets, and images, so nothing sits outside the review window.
Normalization & Entity Matching: Aligns policy periods, recognizes DBAs and entity name changes, and unifies carrier-year views even when formatting and terminology diverge.
Cause-of-Loss Taxonomy: Maps proprietary or carrier-specific loss codes into a standard taxonomy, per LOB, enabling apples-to-apples trend analysis.
Deductible/SIR Logic: Interprets year-over-year changes in deductibles, SIRs, aggregates, and perils—especially crucial in Property cat vs non-cat and in GL construction retentions.
Claims-Made vs Occurrence: Distinguishes triggers in GL & Construction, accounts for retro dates and tails, and flags mismatches with the submission narrative.
Gap & Omission Detection: Compares the timeline across carriers to spot missing months/years and highlights potential undisclosed carrier relationships.
Repeated Large Loss Detection: Surfaces recurring high-severity claims (hail, severe BI, construction defect clusters, catastrophic fire/water) and links them across carriers and years.
Real-Time Q&A and Citations: Ask natural-language questions like “Show frequency and severity by cause for the last five policy years” and receive precise answers with page-level citations to the source documents.
Audit-Ready Outputs: Produces standardized summaries and spreadsheets you can hand to Underwriting, Actuarial, or Reinsurance—with every figure traceable to source.

Unlike generic tools, Doc Chat is trained on your playbooks and output formats. It doesn’t just summarize; it applies your rules for what counts as cat, how to allocate ALAE, and how to categorize causes and severities for each line of business.

AI Compare Prior Carrier Loss Runs: Example Workflows Across LOBs

Below are representative workflows Risk Analysts use to operationalize “AI compare prior carrier loss runs” with Doc Chat. Each example shows how the system moves beyond extraction into reasoning that speeds decisions and boosts confidence.

Property & Homeowners

Upload five years of loss run reports from three prior carriers plus SOVs across the same interval. Doc Chat automatically:

Builds a unified policy-year timeline, flags a six-month gap between Carriers B and C, and cites the missing dates.
Separates cat vs non-cat, mapping hail, wind, and water intrusion consistently across carriers—even when codes differ.
Normalizes incurred/paid/ALAE metrics and recalculates severity bands to your standards.
Highlights repeated, high-severity hail events at two properties with aging roofs and identifies that deductibles changed from flat to percentage wind-only last year—potentially understating retained loss exposure in the submission narrative.

Commercial Auto

Load loss runs with VIN-level details, driver rosters, and MVR summaries. Doc Chat:

Aggregates by unit, driver, and location; correlates recurring rear-end collisions with specific drivers and a high-turnover terminal.
Splits Auto Liability vs Physical Damage trends and normalizes repair cost coding across carriers.
Spots a three-month gap before a large BI event appears under a new carrier—flagging potential undisclosed time with an interim market.
Summarizes frequency/severity by cause, cites the specific pages, and suggests targeted loss control themes tied to garaging and route mix.

General Liability & Construction

In a construction account with both practice policies and OCIP/CCIP participation, Doc Chat:

Parses claims-made vs occurrence years, identifies retro dates, and aligns completed operations tails.
Normalizes defect claims across carriers that classify them differently, extracting patterns by subcontractor trade.
Flags repeat high-severity BI claims on slip-and-fall with a single GC across multiple job sites, with cited claim narratives.
Identifies an open, large reserve carried over three policy years at two carriers, with different ALAE handling, and quantifies the impact on projected loss picks.

Where Doc Chat Fits in the Risk Analyst’s Workflow

Doc Chat plugs into underwriting and risk analysis exactly where you struggle with speed and consistency:

Pre-Triage: Intake massive submission packets and instantly generate a concise, LOB-specific view of the prior-loss picture—complete with gaps and red flags you can address with the broker on day one.

Pre-Bind Due Diligence: Reconcile discrepancies between broker summaries and carrier loss runs, test different deductible or SIR structures, and stress-test cat vs non-cat breakouts to inform pricing and terms.

Reinsurance & Portfolio: Create standardized extracts for facultative submissions, reinsurance presentations, and portfolio dashboards. Identify concentration risks and recurring patterns that impact aggregate management.

Renewal Hygiene: Ensure that submitted loss histories align with your in-force book’s experience, and surface any misalignments that might warrant adjusted terms or additional documentation.

Automating Undisclosed Loss Detection in Underwriting

Undisclosed losses are often a product of time gaps, entity renaming, wrap-up transitions, or a carrier change that leaves telltale holes in the timeline. Doc Chat is designed to “automate undisclosed loss detection underwriting” by:

Timeline Integrity Checks: Verifying that the loss run periods cover the entire requested window without month or year gaps, and citing exactly which dates are missing.
Entity & Alias Resolution: Matching DBAs and historical FEINs across documents to catch records that belong to the same insured but appear under different names.
Cross-Carrier Pattern Linking: Connecting repeated, similar losses (e.g., multiple high-value water losses at one condo building) across carriers and time.
Claims-Made/Occurrence Sanity Tests: Identifying when the declared policy type, retro date, or tail coverage does not line up with the timing of losses or the broker’s narrative.
External Cross-Checks: Incorporating references to ISO ClaimSearch, OSHA logs, MVRs, and internal claims data when provided, to highlight potential discrepancies or missing disclosures.

Every flag comes with citations back to the original documents and pages, enabling rapid broker follow-up and defensible underwriting notes.

The Business Impact: Time, Cost, Accuracy, and Better Decisions

The results compound quickly across a busy new-business queue and renewal cycle:

Time Savings: What previously took a Risk Analyst eight hours of manual reconciliation now collapses to minutes. Teams move from document wrangling to analysis and decision-making. In complex, multi-carrier files that once stretched to days, Doc Chat completes reviews in near real-time. For a perspective on speed at scale, see The End of Medical File Review Bottlenecks.

Cost Reduction: Eliminating manual re-keying and reconciliation trims loss-adjustment expense for pre-bind analysis and reduces reliance on specialized vendor reviews. As outlined in AI’s Untapped Goldmine: Automating Data Entry, consistent automation delivers rapid, measurable ROI.

Accuracy and Consistency: Machines do not tire or lose track of the tenth policy year. Doc Chat reads page 1,500 with the same rigor as page 1, normalizes per your playbook, and preserves an audit trail with page-level citations—critical for internal audit, reinsurers, and regulators. GAIG’s real-world experience underscores the quality leap; read Reimagining Insurance Claims Management.

Risk Selection and Pricing Confidence: Catching undisclosed gaps or repeated large losses early prevents quoting missteps, protects hit ratios, and preserves credibility with brokers. Better prior-loss intelligence yields better terms, tighter deductibles/SIRs, and more accurate loss picks—ultimately improving portfolio performance.

Why Nomad Data Is the Best Partner for Risk Analysts

Doc Chat isn’t a generic summarizer; it’s an insurance-native set of agents trained to operate the way Risk Analysts do:

Purpose-Built for Claims and Coverage: Volume, complexity, and nuance are table stakes. Doc Chat ingests entire claim files and inconsistent policies, finds exclusions and endorsements, and applies custom logic to prior-loss comparisons.
The Nomad Process: We train Doc Chat on your playbooks, document types, and standards—so the output mirrors your team’s workflows and vocabulary.
Real-Time Q&A: Ask questions like “List total incurred (indemnity + ALAE) by policy year and cause for Property” or “Show all BI claims over $250k in the last 5 years for GL,” and get instant, cited answers.
White-Glove Service: From discovery to rollout, Nomad’s experts co-create the solution with your team. We capture unwritten rules and encode them into repeatable, auditable steps—an approach we’ve articulated in Beyond Extraction.
Fast Implementation: Typical timelines run 1–2 weeks to functional deployment, with immediate value via drag-and-drop usage and rapid integration via modern APIs. For a broader view of transformation speed, visit Reimagining Claims Processing Through AI Transformation.
Security and Trust: Built for the demands of insurers, with SOC 2 Type 2 controls and page-level explainability to support compliance, reinsurance reviews, and internal audit.

What Doc Chat Produces for Risk Analysts

Doc Chat outputs decision-ready artifacts for underwriting and portfolio teams, including:

Unified Prior-Loss Timeline by carrier and entity, with highlighted gaps and suspected undisclosed periods.
Standardized Loss Summaries with incurred/paid/ALAE splits, across Property, Commercial Auto, and GL/Construction.
Cause-of-Loss Trend Views (cat vs non-cat for Property, liability vs physical damage for Auto, completed ops vs premises for GL).
Severity-Frequency Profiles by year and LOB, with notes on large open reserves or repeated high-severity events.
Deductible/SIR Reconciliation across years and carriers, including percentage wind/hail transitions and aggregate erosion insights.
Broker Follow-Up Packets with precise questions and page-cited discrepancies for rapid remediation.

Deep Dive: From Documents to Decisions

To show how Doc Chat moves from paper to pricing, consider a mid-market construction account with mixed practice and wrap-up exposures:

1) You drop in five years of loss run reports from two previous carriers, a broker summary, OSHA logs, and several claims history attachments detailing pending litigation. Doc Chat reconciles claims-made vs occurrence, locates retro dates and tails, and tags defect allegations by trade.

2) It then aligns incurred and ALAE treatments, flags a year where a carrier rolled ALAE into total incurred without disclosure, and quantifies the normalization impact on loss picks.

3) It spots a cluster of high-severity BI claims from slip-and-falls at multifamily sites handled by the same GC, then cites adjuster notes indicating recurring housekeeping issues.

4) It generates a standard output table for Underwriting and a narrative for Reinsurance, with cited pages and a summary of conditions you might impose (e.g., subcontractor management protocols, documented housekeeping plans, and a revised SIR).

The same workflow applies to Property, where Doc Chat separates cat/non-cat, normalizes peril codes, flags large repeat hail losses with older roofs, and highlights the deductible evolution from flat to percentage wind-only—then proposes how that change affects retained exposure and pricing strategy.

Prompts Risk Analysts Use Every Day

In addition to automated summaries, Risk Analysts lean on Doc Chat’s Q&A to accelerate diligence. Common prompts include:

“Summarize total incurred by policy year and cause for Property; split cat vs non-cat and cite pages.”
“List all GL completed operations claims over $250k with open reserves and show retro date alignment.”
“Show all Commercial Auto BI claims with payouts over $100k by driver and terminal; include any MVR risk indicators.”
“Identify any timeline gaps across carriers for the last five policy years; cite missing months and potential undisclosed markets.”
“Normalize incurred and ALAE treatments across carriers and quantify the delta versus broker’s summary.”

Measuring the Impact in Numbers

While outcomes vary by complexity and volume, Risk Analysts typically report:

70–90% reductions in time spent on loss-run reconciliation per account.
Major increases in detection of timeline gaps and undisclosed losses—issues that manual review often misses under deadline pressure.
Fewer back-and-forth cycles with brokers due to page-cited questions and standardized extracts.
Improved underwriting confidence, faster no-quote/decline decisions when warranted, and clearer rationale for conditions and pricing when moving forward.

These benefits mirror broader insurer gains seen when moving off manual review. For claims teams handling massive files, Nomad clients have cut multi-day reviews to minutes, as discussed in Great American Insurance Group’s story. The same acceleration applies upstream in underwriting and risk analysis.

Security, Governance, and Auditability

Insurers operate under strict data protection and audit requirements. Doc Chat supports page-level traceability for every figure and flag. Answers link directly to the source page so a Risk Analyst, underwriter, or reinsurer can verify in seconds. The platform aligns to rigorous security practices, including SOC 2 Type 2, and respects client data boundaries and governance standards. This combination of speed, accuracy, and explainability is essential for sustainable, compliant AI adoption.

Implementation: A White-Glove, 1–2 Week Timeline

Nomad’s team meets you where you are. Most Risk Analysts begin with drag-and-drop usage: upload the loss run reports, broker submissions, and claims history attachments you’re wrestling with this week and watch Doc Chat create reconciled outputs and gap flags in minutes. As adoption grows, we integrate with your core systems via APIs to automate intake and output. Because Doc Chat is trained to your playbooks and checklists, the first “win” typically arrives in days, not months.

Getting Started Checklist for Risk Analysts

To maximize value right away, gather a starter set of typical files:

5 years of loss run reports from all prior carriers/TPAs (PDFs, spreadsheets, scans).
Broker submissions including ACORD 125/126/140, SOV spreadsheets, driver rosters, and COIs.
Claims history attachments such as open claim summaries, adjuster narratives, and litigation status.
Any supplemental verification sources: ISO ClaimSearch, MVR summaries, OSHA 300/300A logs.
Your internal categorization rules (cat/non-cat, ALAE treatments, severity bands) and report formats.

We’ll configure Doc Chat to your taxonomy, outputs, and LOB-specific rules. Within 1–2 weeks, your team is producing standardized, audit-ready prior-loss comparisons on every incoming account.

Frequently Asked Questions

Q: Can Doc Chat distinguish claims-made vs occurrence and account for retro dates and tails?
A: Yes. Doc Chat is trained to identify policy triggers, retro dates, and tail coverage references in both loss runs and broker narratives. It flags inconsistencies and normalizes timing for GL & Construction where this is critical.

Q: We see wildly different ALAE handling across carriers. Can Doc Chat normalize this?
A: Absolutely. The system recognizes whether ALAE is included, excluded, or split, and redrafts summaries to your standard. It also cites the source pages where the treatment is implied or stated.

Q: How does Doc Chat detect undisclosed losses?
A: It verifies timeline completeness, resolves entity aliases, links recurring patterns, and cross-references any provided external sources (ISO ClaimSearch, OSHA, internal claim data). Gaps and anomalies are cited for quick broker follow-up.

Q: Will Doc Chat work with our unique severity bands and cause-of-loss taxonomy?
A: Yes. We train the agents on your definitions, so outputs reflect your standards and flow straight into underwriting memos, rating models, and reinsurance packets.

Q: How fast can we get to value?
A: Most teams begin generating reconciled, cited outputs within days. Full rollout commonly completes within 1–2 weeks, including training on prompts and outputs.

The Bottom Line for Risk Analysts

Every decision you make—from whether to quote to what terms to offer—depends on a trustworthy view of the insured’s prior losses. In Property & Homeowners, Commercial Auto, and General Liability & Construction, that view is only as good as your ability to reconcile cross-carrier loss runs, normalize messy details, and call out the gaps. Doc Chat eliminates the drudgery and risk of manual reconciliation, delivering fast, transparent answers that withstand broker scrutiny and internal audit—while unlocking better selection, pricing, and portfolio outcomes.

See It on Your Files

If you’re actively looking for “AI compare prior carrier loss runs” or tools to “automate undisclosed loss detection underwriting,” it’s time to see Doc Chat on a real submission. Upload your next packet of loss run reports, broker submissions, and claims history attachments and watch a prior-loss narrative appear with clear citations, normalized metrics, and flagged gaps. Learn more or request a walkthrough at Doc Chat for Insurance.