Supercharging Loss Run Analysis for Complex Submissions with Doc Chat — Risk Analyst

Complex submissions arrive with dense, multi-year loss run reports, prior carrier claims summaries, and broker submissions—often hundreds of pages across multiple carriers and lines. For a Risk Analyst supporting Commercial Auto, General Liability & Construction, and Property & Homeowners, the challenge is immediate: separate signal from noise fast enough to advise pricing and appetite, while catching the outliers that drive loss costs. That’s where Nomad Data’s Doc Chat changes the equation.

Doc Chat by Nomad Data is a suite of purpose‑built, AI‑powered agents that turn unstructured loss runs into structured insight in minutes. It automates end‑to‑end document review, normalizes inconsistent fields across carriers, computes frequency/severity and trends, flags anomalies, and provides real-time Q&A down to the page citation. If you’re searching for “loss run report automation for underwriters” or evaluating “AI review of complex broker submission loss runs,” this guide details how Risk Analysts can modernize their workflow—without adding headcount and without waiting months for integration.

Why Loss Runs Are the Bottleneck for Risk Analysts

Loss run reports are simultaneously the most essential and the most inconsistent artifacts in a complex submission. Risk Analysts navigate carrier-by-carrier differences in columns, codes, coverage labeling, and reserve accounting; merge partial policy periods; reconcile paid vs. outstanding (OS) reserves; and separate indemnity from LAE, defense, and expense. For Commercial Auto, General Liability & Construction, and Property & Homeowners, the nuances shift by line of business—and so do the pitfalls that can distort a view of loss performance.

In Commercial Auto, fleet composition and exposure measurement (units, power units, trailers, or mileage) fluctuate year to year; attorneys and medical build-ups skew severity; police crash reports, ISO claim reports, and FNOL forms might reference different claim numbers than the loss runs; and subrogation/recovery can be buried in footnotes or appended spreadsheets. For General Liability & Construction, OCIP/CCIP wrap-ups blur policy boundaries; OSHA 300/300A logs, incident reports, and site safety notes introduce parallel data that may or may not be reconciled with the loss runs; and products/completed ops claims surface years after installation. For Property & Homeowners, SOV and COPE attributes (construction, occupancy, protection, exposure) must be aligned with CAT-coded losses, schedule credits, and changing deductibles (AOP vs. named storm/wind/hail) to interpret severity trends correctly.

Even the basics—frequency and severity—aren’t really basic. Did the insured change deductibles mid-term? Did a TPA reclassify expenses in the last quarter, making prior years look artificially favorable? Are large losses truly one-offs or do they mask a pattern (e.g., repeat water damage on the same riser, recurring rear-end collisions on the same route)? Without a defensible way to normalize and interrogate loss runs across carriers and years, Risk Analysts spend their cycles reconciling data instead of assessing risk.

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

Most Risk Analysts still execute a highly manual, multi-step process when loss runs arrive embedded in a broker submission:

Step one is document prep. Loss run reports, prior carrier claims summaries, and broker submissions arrive as PDFs, Excel files, and email attachments—with different column headers like “Paid,” “Incurred,” “Outstanding,” “Expense,” “Indemnity,” and “OS LAE.” The analyst copies and pastes into a master workbook, adding formulas to calculate frequency, severity, paid-to-incurred ratios, and trending. Missing fields (cause of loss, claimant injury type, location) are entered by hand. Duplicate claim numbers are de-duplicated manually, and partial policy periods are stitched together. Reserve releases late in the period require special handling to avoid false improvements.

Step two is exposure alignment. Exposure bases are often inconsistent or missing. For Commercial Auto, the analyst might request units by class, mileage by route, MVR distributions, or USDOT inspection histories. For GL & Construction, they reconcile payroll, hours worked, subcontractor percentages, class codes, and project rosters or wrap-ups. For Property & Homeowners, they tie loss periods to SOV changes, new construction, updated protections, and major occupancy shifts. If ACORD forms (125/126/127/140) are present, the analyst tries to crosswalk them to the loss runs; if not, they reverse-engineer exposure from broker narratives.

Step three is analysis. Pivot tables segment claims by cause, location, body part or peril, and paid/incurred size; outliers are flagged; frequency/severity ratios are compared to industry benchmarks; and the analyst crafts a narrative—often under tight deadlines. But red flags can go unnoticed: non-standard deductible applications, silent sublimits, wrap-up leakage across policies, or repeat claimants appearing under slightly different names. Manual review also struggles to triage claim latency (time from incident to report), Attorney Representation ratios, and reserve development by claim cohort.

The result is a process that can take days per submission, especially when the file includes additional references like FNOL forms, police reports, ISO claim reports, OSHA logs, or repair estimates. In peak periods, backlogs form, quote turnaround times slip, and underwriting decisions lean on incomplete views. The risk isn’t just speed; it’s quality and consistency.

What Gets Missed When Volume and Complexity Spike

When a submission spans five to ten years and multiple carriers, small inconsistencies add up. A handful of high-severity losses may hide an attritional frequency issue; conversely, a cluster of med-only or low-dollar claims might mask a deteriorating safety culture destined to produce a severity spike next year. Without a machine-grade reconciliation across the full document set, Risk Analysts can miss:

• Duplicate claims and claim splits across carriers or TPAs.
• Reserve development patterns that invert after reallocation of LAE vs. indemnity.
• Attorney representation and venue-specific severity drivers.
• CAT vs. non-CAT property losses conflated without COPE context.
• Inconsistent deductible applications and silent sublimits impacting net loss.
• Lag time anomalies that correlate with higher ultimate severity.
• Misaligned exposure bases across periods, inflating or deflating frequency.
• Repeat locations, VINs, job sites, or building components tied to repeat losses.

These are not edge cases; they are everyday realities in Commercial Auto, General Liability & Construction, and Property & Homeowners. The bigger and more complex the account, the more likely human-only processes will miss an important pattern.

Loss Run Report Automation for Underwriters and Risk Analysts: What It Takes

To automate loss run analysis credibly, the solution must handle both volume and complexity. It needs to ingest entire submission packets—including loss run reports, prior carrier claims summaries, broker submissions, ACORD applications, FNOL forms, ISO claim reports, OSHA logs, and SOV/COPE exhibits—normalize all data to your fields and definitions, and then compute the exact metrics your underwriting playbook expects. That’s not generic OCR; it’s a personalized, policy-aware, line-of-business-aware engine that produces consistent, defensible results—fast.

Generic tools falter because the rules you use to interpret a GL wrap-up or a property schedule aren’t fully written down. As Nomad Data explains in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, document intelligence in insurance is about inference—applying unwritten rules across heterogeneous documents, not simply “finding fields.” Loss run automation must be trained on your playbooks so the output matches your underwriting lens.

AI Review of Complex Broker Submission Loss Runs: How Doc Chat Does It

Doc Chat automates end‑to‑end analysis of complex submissions for Risk Analysts and underwriting teams. Here’s how it works in practice:

1) Ingest everything at once. Drop in multi-carrier loss run reports (PDF, Excel, CSV), prior carrier claims summaries, broker submissions, ACORD 125/126/127/140, FNOL forms, ISO claim reports, OSHA logs, police reports, and SOV/COPE schedules. Doc Chat ingests entire claim and submission files—thousands of pages at a time—without added headcount. As detailed in our piece The End of Medical File Review Bottlenecks, the platform processes massive document sets in seconds and never loses focus on page 1,500.

2) Normalize to your standard. Doc Chat maps every carrier’s column naming and coding to your canonical schema. “Paid,” “Paid Indemnity,” “Expense,” “ALAE,” “Defense,” “Outstanding,” and “Incurred” are harmonized; policy periods are stitched; and exposure bases are aligned to your definitions (e.g., units vs. miles for auto; payroll/hours for GL & Construction; TIV for Property). The Nomad Process ensures the system is trained on your underwriting playbook and document conventions so outputs look like your best analyst produced them.

3) Compute the metrics that matter. Frequency and severity are computed by line, year, cause, location, and more. Attorney rate, claim latency, reserve development, subrogation recoveries, deductible application, and CAT vs. non‑CAT splits are analyzed automatically. The engine flags duplicates and claim splits, highlights step‑change shifts in loss patterns, and benchmarks segments with your internal thresholds.

4) Answer questions in real time—across the entire submission. Ask, “Which five locations drove 80% of GL losses?” or “List all Commercial Auto claims with attorney representation over $100K incurred and show police report references.” Doc Chat returns the answers instantly with page‑level citations so you can verify in a click. As shared in Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI, adjusters and analysts can jump straight to the source page, preserving auditability and trust.

5) Generate standardized, exportable outputs. Produce a one‑pager summary or a fully structured export (CSV/Excel/JSON) dropping directly into your pricing model or risk memo. Create consistent narratives for underwriters and referral committees: frequency/severity trends, top perils, reserve development, deductible impact, and recommended investigative or safety questions for the broker or insured.

Line-of-Business Workflows Tailored for Risk Analysts

Commercial Auto: From Fleet Complexity to Clear Drivers of Loss

Commercial Auto loss runs often conflate bodily injury and property damage, bury defense costs, and mix in subrogation adjustments late in the cycle. Doc Chat separates the noise:

• Computes frequency per 100 vehicles or per million miles, normalizing exposure across policy years.
• Segments by driver, route, venue, and attorney involvement to isolate severity drivers.
• Highlights latency clusters (e.g., claims reported >15 days after incident) that correlate with higher ultimate severity.
• Cross-references FNOL forms and police crash reports to reconcile claim numbering and timing.
• Flags repeating VINs or vehicle classes tied to outlier losses, supporting targeted risk controls.

With AI review of complex broker submission loss runs, a Risk Analyst can immediately answer: Which five bodily injury claims over $250K incurred had shared counsel? Did reserve development accelerate in the last two quarters? Are large losses concentrated in a subset of routes or contracts? That’s loss run report automation for underwriters and risk teams, done right.

General Liability & Construction: Wrap-Ups, OSHA Signals, and True Severity

GL & Construction submissions challenge even veteran analysts: OCIP/CCIP wrap-ups create coverage ambiguity; products/completed ops claims emerge years after installation; and contractor/subcontractor splits complicate causation. Doc Chat serves as a specialized reviewer:

• Harmonizes claims across multiple wrap-up policies and policy periods.
• Connects OSHA 300/300A logs and incident reports to loss types, revealing underreported exposure.
• Differentiates med-only from indemnity and LAE so trends aren’t diluted.
• Detects repeat premises or job-site incidents with similar causes of loss (e.g., slip-and-fall clusters tied to the same floor finish or lighting condition).
• Surfaces silent sublimits and deductible structures that materially change net severity.

Risk Analysts get a defensible view of whether high frequency is a reporting/culture artifact or a precursor to true severity. They can also press into safety management efficacy: Are near-miss patterns appearing in OSHA logs reflected in loss runs, or are they diverging?

Property & Homeowners: Aligning SOV/COPE with Loss Reality

Property & Homeowners loss runs demand tight integration with SOV and COPE attributes—construction type, occupancy, sprinklers, hydrant distance, and exposures. Doc Chat connects the dots:

• Splits CAT vs. non-CAT losses and reconciles to TIV changes and deductible structures (AOP vs. named storm/wind/hail).
• Flags repeat causes like water damage on the same riser or recurring electrical faults tied to vintage systems.
• Normalizes partial policy periods when assets were added/removed mid-term.
• Identifies salvage/subrogation discrepancies and late reserve releases that distort year-over-year comparisons.
• Produces a location-level heatmap of severity, letting the Risk Analyst recommend targeted mitigation or exclusions.

By aligning loss data with actual property characteristics, the analyst avoids the trap of blaming “CAT volatility” when the culprit is chronic maintenance or protection gaps.

The Documents Doc Chat Reads So You Don’t Have To

Traditional IDP tools stumble on the variability of insurance paperwork. Doc Chat is built for it. In typical submissions, Risk Analysts will see Doc Chat synthesize:

• Loss run reports (multi-carrier, multi-format)
• Prior carrier claims summaries and bordereaux
• Broker submissions and narratives
• ACORD 125/126/127/140
• FNOL forms and adjuster notes
• ISO claim reports/ClaimSearch references
• OSHA 300/300A logs and incident reports
• Police crash reports (Commercial Auto)
• Statement of Values (SOV) and COPE data (Property)
• Repair estimates, invoices, and subrogation correspondence

Doc Chat’s strength is not just extraction; it’s inference across these materials, codifying your unwritten rules so every reviewer produces a consistent result. For a deeper dive on why this matters, see Beyond Extraction.

The Business Impact for Risk Analysts and Underwriting Teams

Automating loss run analysis is more than a time saver; it reshapes performance metrics across the underwriting funnel. Based on Nomad Data’s work with carriers and TPAs (see Reimagining Claims Processing Through AI Transformation and our GAIG webinar recap), teams consistently report:

• Review time for complex loss runs reduced from days to minutes, enabling more quotes per analyst without additional staff.
• Improved accuracy and consistency—even at high volume—via page‑level citations and standardized outputs mapped to your playbook.
• Earlier identification of adverse selection drivers (e.g., attorney-heavy venues, venue drift, chronic water intrusion) that influence appetite and pricing.
• Higher underwriting hit ratios due to faster, more insightful broker feedback and targeted RFI questions.
• Reduced leakage from missed deductibles, sublimits, or reserve patterns that skew severity assumptions.
• Happier analysts focused on judgment and strategy rather than repetitive document prep and data cleanup.

Speed without explainability is risky. Doc Chat’s page-level citations and exportable summaries create an audit trail that satisfies internal governance, reinsurers, and regulators. As highlighted in the GAIG story, transparent sourcing builds trust across claims, underwriting, and compliance.

From Manual to Automated: A Side-by-Side View

Manual workflow: Download 10+ PDFs and spreadsheets; clean/normalize fields; stitch partial policy periods; reconcile duplicates; build pivot tables; compute frequency/severity by line and cause; write narrative; spot-check anomalies; iterate when a missing file arrives; repeat across three lines of business. Result: 1–3 days per complex submission with high variance in quality.

With Doc Chat: Drag-and-drop packet; the system ingests, normalizes, computes, and summarizes; ask questions like “Top five loss drivers over $100K incurred” or “Show all GL med-only claims that later converted to indemnity with venue = X”; export a one-pager summary and a structured file for your pricing model. Result: minutes per submission with consistent, defensible outputs.

Security, Compliance, and Auditability Built In

Insurance submissions contain sensitive claimant, financial, and policy data. Nomad Data operates with enterprise-grade security and governance. As detailed in our work across carriers, Doc Chat maintains document-level traceability, page citations for every answer, and configurable retention policies to align with your compliance needs. Answers are verifiable, not black-box. For organizations concerned about data stewardship and explainability, Doc Chat supports a defensible transition to AI-assisted underwriting analysis.

Why Nomad Data Is the Best Partner for Loss Run Automation

Most “document AI” vendors offer one-size-fits-all extraction. Nomad Data delivers a personalized solution built around your documents, rules, and workflows. The differentiators matter to Risk Analysts:

• Volume without compromise: Ingest entire claim and submission files—thousands of pages—so your analysis isn’t limited by manual bandwidth. In real-world settings, tasks that took days shrink to minutes.
• Complexity mastered: Doc Chat uncovers exclusions, endorsements, triggers, and deductible applications that hide in dense, inconsistent documents, enabling more accurate severity assumptions and fewer disputes.
• The Nomad Process: We train Doc Chat on your playbooks and standards to produce outputs your team trusts.
• Real-time Q&A: Ask anything across massive document sets; get instant answers with citations.
• Thorough and complete: No blind spots—Doc Chat surfaces every reference to coverage, liability, or damages that affects your risk view.
• Your partner in AI: You’re not buying software; you’re gaining a white‑glove partner that co‑creates with you, adapts to your feedback, and delivers ongoing value.

Implementation is fast. Typical deployments take 1–2 weeks from kickoff to production use, thanks to modern APIs and a pragmatic approach. Start with drag‑and‑drop, then integrate once your team is comfortable. As described in AI’s Untapped Goldmine: Automating Data Entry, the ROI from automating repetitive document work is often immediate and outsized.

Answers Risk Analysts Need—Out of the Box

Doc Chat ships with pre-built prompts and summaries tuned for underwriting and risk analysis. For loss run report automation for underwriters and Risk Analysts, standard deliverables include:

• Frequency/severity by LOB, year, cause, venue, and location, normalized to exposure base.
• Reserve development and paid-to-incurred ratios by cohort with variance analysis.
• Attorney representation rates and claim latency mapping with severity correlation.
• CAT vs. non‑CAT splits with deductible application and net severity impact.
• Duplicate detection, claim splits, and subrogation/salvage reconciliation.
• “Top five drivers of loss” and “Top five questions to ask the broker/insured.”

Prefer your own template? We’ll configure presets so every summary mirrors your house style—consistent headings, metrics, and narratives across Commercial Auto, GL & Construction, and Property & Homeowners.

Practical Scenarios That Win Back Your Time

Scenario 1: Mid-market construction account with a five-year GL wrap-up history across two carriers, OSHA logs in spreadsheets, and incident narratives attached as scanned PDFs. Historically, reconciling med-only vs. indemnity and isolating job site clusters takes two days. With Doc Chat, normalization and clustering take minutes; the Risk Analyst sees a pattern of ladder-related falls at three sites and identifies a missing safety audit appendix—all before the underwriting huddle.

Scenario 2: Regional fleet program with inconsistent mileage reporting and mixed body types sees sporadic BI severity spikes. Doc Chat reveals that 70% of $100K+ incurred claims involve a single corridor with elevated attorney representation and long claim latency. The underwriting recommendation: pricing differentiation by route and a broker-led plan to reduce reporting lag with refreshed FNOL protocols.

Scenario 3: Multi-state property schedule with shifting TIV and protection upgrades shows flat severity at first glance. Doc Chat separates CAT and non‑CAT, normalizes deductibles across years, and flags repeated water damage claims on a subset of older risers, recommending targeted maintenance and revised self-insured retention levels.

From Insight to Action: Raising Underwriting Confidence

Great loss run analysis doesn’t end at charts; it produces better decisions. With Doc Chat, Risk Analysts arm underwriters with precise, line-of-business‑specific insights and actionable follow-ups: Which units, routes, or locations require a pricing load or exclusion? Where does a loss control visit change expected loss cost? Which schedule items justify a sublimit? By compressing the time from document intake to defensible recommendations, you move faster without sacrificing diligence.

Implementation: Fast, Safe, and Built Around Your Team

Getting started is simple. Begin with drag‑and‑drop analysis of a few active submissions. Use Doc Chat’s real‑time Q&A to validate outputs against claims you already know cold—an approach that consistently builds trust, as seen in the GAIG experience. Once aligned on templates and rules, connect Doc Chat to your intake and pricing systems via API. Most teams are productive within 1–2 weeks, with white‑glove support from Nomad Data throughout. As your volume grows, Doc Chat scales instantly—no overtime, no backlog.

Bottom Line: Turn Loss Runs into Competitive Advantage

For Risk Analysts supporting Commercial Auto, General Liability & Construction, and Property & Homeowners, the struggle has never been a lack of data—it’s been time and consistency. With Doc Chat, AI review of complex broker submission loss runs becomes your default workflow, and loss run report automation for underwriters becomes a competitive edge. You’ll quote faster, triage smarter, and present underwriting with confident, documented recommendations.

Ready to transform loss run analysis from a bottleneck into a strength? Explore Doc Chat for Insurance and see how a purpose-built partner can deliver measurable impact—fast.