Supercharging Loss Run Analysis for Complex Submissions with Doc Chat — Commercial Auto, GL & Construction, Property

Risk analysts and underwriters know the drill: broker submissions show up packed with loss run reports, prior carrier claims summaries, ISO claim reports, and a stack of attachments. You need to quickly separate frequency from severity, reconcile incurred versus paid values, find questionable spikes or outliers, and shape a defensible view of risk. But the clock is always ticking, formats vary wildly, and the manual scrubbing work overwhelms even the most seasoned team. That is the challenge this article tackles head-on.

Nomad Data's Doc Chat is purpose-built to conquer this bottleneck. Doc Chat ingests entire submission packets, reads thousands of pages across PDFs and spreadsheets, standardizes fields, and answers your questions with citations to the exact page and line. Whether you are in Commercial Auto, General Liability and Construction, or Property and Homeowners, Doc Chat delivers real-time, defensible summaries that reveal claim frequency, severity, trends, and anomalies in minutes. It is loss run report automation for underwriters and the daily accelerator for every risk analyst facing complex broker submissions.

For a deeper look at Doc Chat for insurance, visit the product overview: Doc Chat by Nomad Data. For context on why document AI must go beyond simple extraction to inference, see Nomad's perspective here: Beyond Extraction: Why Document Scraping Isnt Just Web Scraping for PDFs.

The loss run reality for risk analysts across Commercial Auto, GL and Construction, and Property

While loss runs feel universal, the nuances change by line of business. Risk analysts are expected to connect dots across multiple carriers, claim systems, and time periods, then produce a single source of truth that guides appetite, pricing, terms, and conditions. Here is how complexity shows up by line.

Commercial Auto

Commercial Auto loss run analysis lives and dies on frequency, large loss spikes, litigation severity, and the operational hygiene of the fleet. Risk analysts must distinguish crash-heavy operations from isolated severity. Anomalies hide in inconsistent cause codes, missing driver identifiers, or claims that reopened. Reserves can mask ultimate severity; subrogation and salvage complicate paid and recovery totals. You may see mixed programs across years, with some policy periods written on high deductibles or SIRs and others on first-dollar structures, all in one broker submission. For larger fleets, adjusters attach police reports, MVR summaries, or supplemental incident narratives. When the same collision appears in two carrier systems with slightly different dates of loss or claim numbers, the risk analyst must deduplicate to avoid overstating loss costs.

On top of that, commercial auto submissions often include endorsements, USDOT compliance notes, and garaging changes across time. Identifying the inflection point where frequency accelerates, or where a litigation trend begins, can change the entire rating conversation. Yet those insights are not neatly labeled; they are buried in the fine print of loss run reports and prior carrier claims summaries.

General Liability and Construction

GL and Construction loss runs introduce wrap-up complexity, additional insured claims, products and completed operations, and jobsite specific exposures. A general contractor may have OCIP or CCIP history that must be parsed separately from corporate GL. Bodily injury severity can explode in a single litigated incident, skewing the most recent policy year. Premises-only exposures look very different from subcontractor-heavy operations. OSHA logs or incident reports might be mentioned in the broker submission but not reflected in the loss runs, creating blind spots.

Across multiple brokers and prior carriers, you will encounter varying cause and location codes, inconsistent reserve philosophies, and incomplete suits and subrogation fields. Distinguishing employee injury miscodes that belong in workers compensation from true third-party liability incidents is common. The risk analyst has to reconcile paid, incurred, ALAE, and outstanding reserves, then roll up clean picture-by-year trends that stand up to underwriting, actuarial, and reinsurance scrutiny.

Property and Homeowners

Property and Homeowners loss runs introduce weather patterns, cat versus non-cat splits, and peril-specific deductibles like wind and hail. You may be handed schedules of values and property characteristics alongside loss runs that mix water damage frequency, fire severity, theft, and attritional glass claims. One severe wind event can drive dozens or hundreds of related claims; proper aggregation and tagging matters for trend analysis. Recoveries, salvage, and subrogation against contractors may alter net paid. For admitted and non-admitted programs, catastrophe coding could be inconsistent across carriers or TPAs, making year-over-year comparisons tricky.

In all three lines, risk analysts must map coverage triggers and policy form quirks they can only infer from loss runs and carrier summaries. The right explanation often depends on granular context that is scattered across a submission, not a single well-structured document. This is exactly where Doc Chat’s ability to read at scale and draw inferences shines.

How loss run review is handled manually today

Even in sophisticated underwriting shops, loss run review is a manual gauntlet. Risk analysts and underwriters read PDFs page by page, copy figures into spreadsheets, and cross-check values across multiple files. Format changes, broken tables, and scanned images slow everything down. Analysts juggle multiple versions sent by different brokers and carriers, all while trying to normalize paid vs incurred, open vs closed, and separate ALAE from indemnity.

Typical manual steps include:

Downloading, renaming, and cataloging loss run reports, prior carrier claims summaries, broker submission cover letters, ISO claim reports, and any supplemental notes from adjusters.
Hand-keying or copy-pasting claim number, date of loss, cause or event codes, paid, incurred, reserve, recovery, subrogation, and status into a spreadsheet; fixing OCR mistakes for scanned PDFs.
Deduplicating across policy numbers and carrier systems; matching near-duplicate events with different claim numbers or dates; resolving reopened claims and reserve movements.
Normalizing inconsistent field names and currencies; reconciling ALAE versus indemnity; separating CAT-coded events and grouping related occurrences.
Rolling up frequency and severity by line of business, policy year, location, division, driver or vehicle where available, or jobsite for construction submissions.
Spot-checking trends, unusual reserve spikes, late-reported claims, lag patterns, litigation indicators, and subrogation opportunities.
Calculating loss ratios against stated exposures; clarifying exposure basis mismatches across years and lines where payroll, sales, vehicle count, or TIV changed materially.

These steps are slow, repetitive, and error-prone. They steal time otherwise spent on risk insight and negotiation strategy. Worse, humans inevitably miss patterns when confronted with thousands of pages and inconsistent formats. The result: elongated cycle times, inconsistent conclusions by analyst, and preventable leakage in pricing, terms, and conditions.

The hidden cost of doing nothing

When manual loss run analysis remains the norm, insurers and brokers absorb significant cost and risk. Backlogs delay quotes and bind decisions. Loss-adjustment expenses rise because highly skilled people spend hours on data entry. Human fatigue leads to missed exclusions, misread claim statuses, or overlooked duplicates, creating leakage and eroding underwriting profit. Seasonal spikes or large program submissions force overtime or last-minute outsourcing. Knowledge becomes fragmented in spreadsheets on local drives, and onboarding new analysts is slow because institutional heuristics are unwritten.

Nomad Data has documented these industry dynamics across claims and underwriting. For a cross-industry perspective on why intelligent document processing is an ROI engine, explore Nomad’s analysis here: AIs Untapped Goldmine: Automating Data Entry. And to see how real carriers have crushed document review bottlenecks, see GAIG’s story: Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI.

What good looks like for a risk analyst

Across Commercial Auto, GL and Construction, and Property and Homeowners, high-performing loss run review shares the same characteristics. The wish list from risk analysts typically includes:

Instant ingestion and standardization of all loss run reports, prior carrier claims summaries, ISO claim reports, and broker submissions, regardless of format.
Accurate extraction of key fields: claim number, date of loss, cause code, location or vehicle, paid, incurred, reserve, ALAE vs indemnity, subrogation, recoveries, suit indicator, open vs closed, and reopened flags.
Automatic deduplication of claims that appear across carriers, TPAs, and policy numbers; merging near-duplicates with fuzzy matching.
Clear rollups: frequency, severity, and pure loss trends by year, peril or cause, location, operating unit, and line of business, with cat vs non-cat separation for Property and Homeowners.
Outlier and anomaly detection: reserve spikes, late-reporting patterns, litigation acceleration, unusual ALAE ratios, and claim reopening waves.
Exposure alignment and loss ratio calculations that respect changing bases like payroll, sales, vehicle count, or TIV.
Real-time Q and A: ask for five-year loss triangles, top 10 drivers by severity, or the largest construction-site claims with suit indicators and get answers with citations.
Export-ready output that plugs into pricing worksheets, capital models, and reinsurance submissions.

Until now, teams have had to build this workflow manually with fragile macros and project-based scrubbing sprints. Doc Chat changes the equation.

Introducing Doc Chat for loss run report automation

Doc Chat by Nomad Data is a suite of insurance-trained AI agents that ingest entire claim and submission files, extract and normalize key facts, and answer questions instantly across massive document sets. It is designed for the realities of loss run analysis: inconsistent formats, scanned PDFs, varying nomenclature, and the need to infer what is not explicitly written. The result is fast, consistent, defensible insights for risk analysts and underwriters across Commercial Auto, GL and Construction, and Property and Homeowners.

Highlights include:

Volume: Doc Chat ingests thousands of pages per submission — entire claim files and multi-carrier loss runs — in minutes without adding headcount.

Complexity: It reads exclusions and endorsements when provided, normalizes field names across carriers, separates indemnity and ALAE, and aligns cat versus non-cat in Property contexts.

Nomad Process: Doc Chat is trained on your underwriting playbooks and loss run conventions, so outputs match your standards and scorecards.

Real-time Q and A: Ask for frequency and severity trends, largest claims by cause, or all open GL claims with suit indicators. Get instant answers with page-level citations.

Thorough and complete: Doc Chat surfaces every reference to coverage, liability, damages, and loss allocation so nothing important slips through. It eliminates blind spots and reduces leakage.

You can learn more about the platform here: Doc Chat for Insurance.

How Doc Chat automates loss run analysis end to end

Doc Chat does the work a risk analyst wishes a junior team could do perfectly every time — at machine speed and without fatigue. Here is how it runs the workflow:

1. Ingest and classify the submission

Drag and drop the entire broker submission. Doc Chat automatically recognizes loss run reports, prior carrier claims summaries, and related materials like FNOL snapshots, adjuster letters, and ISO claim reports. It classifies the documents by line of business and policy period, and it identifies cross-references like account name variants, acquisitions, or DBA naming that can complicate trend analysis.

2. OCR, parse, and normalize

Whether machine-readable or scanned, Doc Chat applies accurate OCR and table reconstruction. It extracts standardized fields across carriers and TPAs: claim number, date of loss, location, cause code, paid, incurred, reserve, ALAE versus indemnity where present, open or closed status, recovered amounts, subrogation indicators, and suit flags. For Property and Homeowners, it flags wind, hail, water, fire, theft, and other perils and distinguishes cat-coded events where indicated.

3. Deduplicate and reconcile

Loss runs often contain the same claim more than once under different carriers, TPAs, or policy numbers. Doc Chat merges near-duplicates with fuzzy matching on claim descriptors and event details, resolves reopened status, and tracks reserve movements to avoid overstating severity. It also separates wrap-ups from corporate GL in construction contexts when metadata indicates OCIP or CCIP.

4. Summarize frequency, severity, and trends

In minutes, you get standardized rollups: frequency and severity by line, by policy year, by cause or peril, by location or jobsite, and by vehicle or operating unit where fields exist. Doc Chat can present cat versus non-cat splits for Property and Homeowners and highlight single-event clusters. It can calculate loss ratios against exposures provided in the submission, while calling out exposure gaps or inconsistencies that limit comparability.

5. Detect outliers and anomalies

Doc Chat flags late-reporting patterns, reserve spikes near renewal, unusual ALAE to indemnity ratios, litigation trends, and unexpected concentrations by location or vehicle class. It highlights claims that may be duplicates across carriers or those missing critical data elements that require broker follow-up.

6. Answer questions with citations

Type questions like: List top 10 GL claims by incurred in the last five years with suit indicators and subrogation status. Or: Show all open Commercial Auto claims over 100,000 incurred and indicate reserve movements over the last 90 days. Doc Chat returns structured answers with a link back to the exact page in the loss run where each figure appears. This make-or-break explainability accelerates underwriting committee conversations and reinsurer reviews.

7. Export and integrate

Doc Chat outputs to Excel, CSV, or direct system integrations, powering pricing worksheets, capital models, and reinsurance submissions. APIs can connect to modern claims and policy platforms. Because fields are normalized, downstream analytics do not require one-off field mappings for each new carrier format.

Illustrative scenarios across lines

Commercial Auto fleet with mixed program structures

A 600-vehicle regional fleet arrives as a complex submission including five years of loss run reports from three prior carriers and a TPA. Some years were written with a 250,000 SIR, others with first-dollar coverage. Doc Chat ingests everything, normalizes fields, and separates the SIR years from first-dollar years to avoid apples-to-oranges comparisons. It deduplicates two collisions appearing across a carrier and TPA report, reconciles reserve movements, and flags a litigation-driven severity jump in year four. Real-time Q and A shows that the largest ten claims are concentrated in a single terminal where turnover is high, guiding targeted risk control questions and terms.

General contractor with wrap-ups and corporate GL

A GC presents loss runs that mix OCIP, CCIP, and corporate GL. Doc Chat recognizes wrap-up indicators and splits the streams. It finds that the apparent frequency spike is actually concentrated within one wrap-up project. It also flags several incidents miscoded as third-party injuries that belong in Workers Compensation. The risk analyst uses Q and A to request all third-party bodily injury claims with reserve increases greater than 50 percent in the last 120 days and receives an answer with citations, ready for the underwriting file note.

Property portfolio exposed to wind and water

A property schedule with coastal locations arrives with mixed-quality loss runs from two MGAs and one admitted carrier. Doc Chat normalizes peril data, clusters cat-coded claims from a single hurricane, and isolates non-cat water damage frequency at aging buildings. It highlights an unusual pattern of repeated plumbing failures at one location and a spike in ALAE on a subset of small claims, prompting a deeper look at vendor usage and potential subrogation.

Real-time Q and A for risk analysts and underwriters

Doc Chat is not just a summarizer. It is a research assistant that understands underwriting questions. Examples that risk analysts use every day:

Commercial Auto: List all open auto BI claims with incurred over 100,000, show reserve movements by quarter, and identify whether the driver had a previous incident in the last 24 months.

GL and Construction: Show the top five job sites by total incurred, identify any claims with suit indicator true and subrogation pending, and provide the distribution of slip and fall incidents by location type.

Property and Homeowners: Break out cat versus non-cat incurred for the last five years, list any locations with more than three water damage events, and calculate the non-cat attritional loss ratio excluding the top two events.

These questions can be issued right after upload. Answers arrive with citations so stakeholders can validate the numbers instantly. This is, in practical terms, loss run report automation for underwriters that keeps human judgment in the loop while eliminating the grind.

From broker submissions to decisions in hours: AI review of complex broker submission loss runs

On complex accounts, the difference between winning and losing often comes down to time to insight. Doc Chat turns around an AI review of complex broker submission loss runs the same day they arrive. Instead of waiting days for a manual scrub, underwriters get a normalized summary and can dive into targeted Q and A immediately. Reserve committee questions can be answered with sourced evidence. Reinsurance placements benefit from consistent storylines backed by page-level citations. Your best analysts focus on judgment, deal-shaping, and broker dialogue rather than typing.

Data quality checks and anomaly detection

Doc Chat embeds control checks so risk analysts spot issues earlier:

Duplicate detection: Merges near-duplicates across carriers and TPAs; flags potential double counts by claim number variants and event matching.

Reserve movement anomalies: Highlights late-year reserve increases, negative incurred moves, and reopened claims with unexpected patterns.

ALAE vs indemnity balance: Surfaces unusual ALAE ratios for peer comparison; flags inflated defense costs for small indemnity amounts.

Cat clustering: Groups property claims linked to the same weather event; checks for inconsistent cat coding across sources.

Exposure mismatches: Calls out inconsistent exposure bases year to year that would distort loss ratios.

For an in-depth view of how Nomad applies similar anomaly detection in claims organizations, see Reimagining Claims Processing Through AI Transformation.

Integrations, outputs, and explainability

Doc Chat produces standardized outputs that plug into pricing models and underwriting workbenches. Export to Excel or CSV for rapid collaboration with actuaries or capital teams. Capture rollups by year, peril, location, or vehicle class for presentation. For modern cores, API connectors expedite sending structured fields into downstream systems. Critically, every number has a breadcrumb: answers link back to the exact source page so risk analysts, managers, auditors, or reinsurers can verify details instantly.

Business impact: time, cost, accuracy, and morale

Organizations using Doc Chat report dramatic improvements on complex submissions:

Time savings: Reviews that took days collapse to minutes. One analyst can handle many more submissions without overtime, enabling same-day triage for enterprise accounts.

Cost reduction: With fewer manual touchpoints, loss-adjustment and intake costs fall. Expensive external scrubs become targeted exceptions rather than the norm.

Accuracy and consistency: Machines never tire. Doc Chat reads page 1,500 with the same rigor as page 1. Normalized fields and standardized rollups remove person-to-person variance.

Morale and retention: Analysts spend more time on meaningful investigation and negotiation strategy, less on copy-paste. This reduces burnout and preserves institutional knowledge.

Nomad Data regularly observes 30 to 200 percent ROI in document automation programs by eliminating rote work. See supporting discussion here: AIs Untapped Goldmine: Automating Data Entry.

Security, compliance, and auditability

Loss runs and claim summaries contain sensitive information. Doc Chat operates with enterprise-grade security and supports rigorous audit and regulatory review. Outputs carry document-level traceability, making it simple to demonstrate how a conclusion was reached. Nomad Data maintains modern security certifications and deploys architecture aligned with insurer IT and compliance requirements. Your data governance teams retain full control, and risk analysts gain an irrefutable audit trail that stands up to internal QA, reinsurer reviews, and regulators.

Why Nomad Data is the best partner for loss run automation

Nomad Data blends domain expertise with insurance-grade AI engineering. Doc Chat is not generic software; it is a tailored solution built around your documents, your playbooks, and your underwriting standards. The Nomad Process trains Doc Chat on your target lines — Commercial Auto, GL and Construction, Property and Homeowners — and on the way your risk analysts evaluate loss runs.

What sets Nomad apart:

White glove approach: Our specialists interview your best risk analysts to capture unwritten heuristics and edge cases, then encode them so every desk benefits from the same best-in-class process.

Fast implementation: Typical implementations take one to two weeks, not months. Teams often start with drag-and-drop usage on day one and integrate later.

Scales with you: During surge periods — seasonality or large program marketing — Doc Chat scales seamlessly, eliminating backlogs without temp staffing or overtime.

Co-creation mindset: You are not buying a static tool. You get a partner that evolves the solution with your business and codifies new insights over time.

Implementation roadmap: 1 to 2 weeks to value

Doc Chat deployments are rapid and lightweight. A typical path:

Week 1: Discovery and baseline. We review representative loss runs and broker submissions across Commercial Auto, GL and Construction, and Property and Homeowners. We align on field mappings, preferred rollups, and your red-flag list for anomalies. We stand up a secure environment and deliver a calibrated preset for summaries and Q and A.

Week 2: Pilot and calibration. Your risk analysts upload live submission packets and test real questions. We tune normalization rules, deduplication sensitivity, and line-of-business nuance. Exports are aligned with your pricing templates. Most teams expand usage immediately after the pilot.

Beyond week 2, optional API integration pushes structured loss run fields into underwriting workbenches, pricing tools, or data lakes. Throughout, your analysts retain the ability to ask ad hoc questions and cite results back to original pages.

Frequently asked questions from risk analysts

Will Doc Chat work on scanned or messy PDFs? Yes. Doc Chat applies high-accuracy OCR and table reconstruction, then normalizes fields regardless of per-carrier idiosyncrasies.

Can it separate ALAE from indemnity and track reserve movements? Where fields are present, Doc Chat extracts and separates them, and it summarizes reserve movements by period. It flags missing fields so you can request improved runs.

How does it handle duplicates across carriers and TPAs? Doc Chat uses fuzzy matching on claim descriptors and event data to merge near-duplicates and prevent double counting. Suspicious cases are flagged for human review with clear rationale.

Can it map cat versus non-cat for Property and Homeowners? Yes. It leverages cat indicators when present, clusters related events, and highlights inconsistencies for confirmation.

Does it replace human judgment? No. Doc Chat functions like a tireless, highly trained analyst that prepares structured insights and answers questions with citations. Humans remain in control of decisions, negotiation strategy, and final underwriting calls.

How quickly can we start? Most teams start same day in a drag-and-drop mode and reach a tailored, production-ready workflow in one to two weeks.

A new operating model for loss run analysis

Loss run scrutiny is too critical to be constrained by manual drudgery. Doc Chat delivers the speed, consistency, and explainability that risk analysts need to turn complex broker submissions into clear underwriting decisions. It compresses days of reading and spreadsheet wrangling into minutes of interactive analysis and supports every number with page-level evidence.

To learn how your team can deploy loss run report automation for underwriters, request a walkthrough of Doc Chat here: Doc Chat by Nomad Data. If you want to understand why successful document AI must capture unwritten human logic, revisit Nomad’s take: Beyond Extraction. And to see how high-volume claims teams already scaled similar workflows, explore GAIG’s experience: GAIG Accelerates Complex Claims with AI.

For risk analysts operating in Commercial Auto, GL and Construction, and Property and Homeowners, the path is clear. Automate what is repetitive. Standardize what should be consistent. Keep judgment where it belongs — with you — and let Doc Chat handle the rest.