Unlocking Faster Risk Assessment: AI for Loss Run Analysis in Commercial Insurance Underwriting

In the competitive landscape of commercial insurance, the ability to rapidly and accurately assess risk has become a defining differentiator for carriers and brokers alike. With underwriting teams inundated by mounting submission volumes, evolving compliance requirements, and tighter turnaround expectations, outdated manual processes are no longer sustainable. At the heart of every risk decision lies the loss run—a document detailing an applicant’s historical claims experience, loss amounts, causes of loss, dates, and claim frequency. Yet, despite their critical role, extracting actionable insights from loss runs remains an arduous, error-prone task, slowing the underwriting cycle and often impacting the accuracy of risk evaluation.

Emerging AI solutions like Nomad Data’s Doc Chat offer a transformative path forward. By leveraging advanced document intelligence, carriers can automate the analysis of loss runs—regardless of format, length, or complexity—significantly accelerating risk assessment and decision-making in commercial lines. In this article, we’ll explore how loss run analysis automation is revolutionizing commercial underwriting, driving faster risk selection, greater consistency, and enabling underwriters to focus on strategic work. We’ll highlight how Nomad Data’s white glove implementation and rapid go-live process allows organizations to unlock value in as little as 1-2 weeks.

Why Manual Loss Run Analysis Hampers Commercial Underwriting

Commercial insurance underwriting is heavily dependent on historical loss experience. For property and casualty risks, loss runs reveal the frequency, severity, and patterns of past claims—vital statistics that drive risk acceptance, pricing, limit setting, and coverage terms. However, the manual process of reviewing and extracting data from loss runs is fraught with inefficiencies, especially as document volumes mount in mid-market or larger submissions. Key challenges include:

Format variability: Each carrier has its own template for loss runs. These may be PDFs, scanned images, Excel files, or even handwritten documents, complicating data extraction and normalization.
Unstructured content: Critical data such as cause of loss, paid amounts, reserved amounts, claim outcomes, and timelines may be scattered across pages, buried in notes or table footers, and not always presented as clear, extractable fields.
Volume overload: Large books of business or complex commercial accounts can involve dozens or hundreds of loss runs per submission—each needing to be reviewed in full to ensure no material losses are overlooked.
Manual error and delays: Legacy approaches require underwriters or assistants to painstakingly comb documents, enter data into spreadsheets or rating engines, and cross-check for omissions or inconsistencies—a process prone to errors, omissions, and missed insights.

All of this results in slower turnaround times, higher operational costs, variable risk selection, and staff burnout from repetitive, tedious work. Meanwhile, missed or misinterpreted losses can lead to adverse selection, poor pricing, and increased claims volatility for the carrier.

How AI Transforms Loss Run Analysis for Commercial Underwriting

Modern AI-driven loss run extraction tools like Nomad Data’s Doc Chat fundamentally reimagine how underwriters and operations teams engage with loss run documents. By combining powerful large language models with advanced data pipelines, these solutions deliver:

1. Automated Extraction and Normalization

AI agents read and interpret loss runs from any carrier, regardless of their layout or file type. Whether a scanned PDF, an Excel table, or a complex multi-page document, Doc Chat instantly locates and extracts the following:

Claim dates
Claim numbers
Causes of loss (e.g., fire, theft, water, slip-and-fall)
Paid and reserved amounts
Claim status (open, closed, subrogated, etc.)
Total incurred loss
Loss frequency and trends

This structured information is automatically normalized into a consistent output—ideal for direct import into underwriting workbenches, rating models, or broker management systems regardless of the original source format. Gone are the days of manually keying data line by line.

2. Rapid Summarization and Insight Generation

Beyond basic data extraction, Nomad’s Doc Chat provides automated loss summaries tailored to your desired format. AI-driven analysis can quickly highlight:

Loss ratios by year or line of business
Frequent causes of loss
Trends such as deteriorating claims frequency
Large individual claims above set thresholds
Open claims, outstanding reserves, and severity outliers

Underwriters can specify which metrics matter most to their appetite and receive instant insights—freeing them from manual spreadsheet work and enabling them to focus on nuanced risk evaluation and broker negotiation.

3. Cross-Check, Enrich, and Verify Data from Multiple Loss Runs

Many submissions arrive with multiple years of loss runs from different carriers, each using a unique style and terminology. AI systems like Doc Chat reconcile these, identifying:

Duplicate or recurring claims across years
Discrepancies or data gaps
Potentially missing or incomplete reports

Additionally, Doc Chat can connect to external data sources for enrichment—validating claim locations, matching policy periods with external benchmarks, and flagging unusual loss activity relative to industry benchmarks. This robust, multi-layer analysis is impossible to match at scale with human effort alone.

4. Consistency, Auditability, and Compliance

In highly regulated insurance markets, every risk decision requires traceability and defensibility. Doc Chat logs every extraction, highlighting exactly which source page or cell produced each data point. This creates a full audit trail and supports compliance with both internal and external mandates—reducing regulatory risk and simplifying audits. Custom output formats enforce standardized loss run reviews across teams, eliminating the variance found in purely human-driven analysis.

Impact: Slashing Cycle Times and Costs with Loss Run Analysis Automation

The implementation of AI for commercial underwriting offers both immediate and long-term business value. Real-world carriers and MGAs deploying Nomad’s Doc Chat have observed:

Cycle time reductions of up to 85%: Tasks that once took hours or days—especially for large or complex accounts—are now completed in minutes.
Labor cost savings of 30% or more: Teams can reallocate staff from rote data entry to higher-value risk assessment and broker engagement.
Improved risk selection quality: Automated extraction reduces missed or misinterpreted losses, leading to more appropriate risk pricing and acceptance.
Greater scalability: Carriers can handle surges in submission volumes during renewal seasons without increasing headcount or risking bottlenecks.
Enhanced employee satisfaction: Staff freed from tedious work report higher job engagement and lower turnover, allowing the business to retain underwriting expertise.

For portfolio underwriting or renewal reviews involving thousands of policies, automated loss run analysis makes previously impossible levels of throughput and review frequency achievable—powerful advantages in a fast-moving market.

Why Manual Loss Run Review Remains Prevalent—and Why It’s Changing

If the benefits are so clear, why do so many carriers and brokers still use manual methods for loss run review? The answer lies in the historical limitations of automation technology:

Brittle extraction tools: Older solutions relied on fixed templates or keyword searches, breaking down when document layouts changed or language varied.
High setup and maintenance costs: Custom configuration and IT involvement hindered scalability, making automation economically viable only for the largest players or highest-volume use cases.
Data quality concerns: Inconsistent results created mistrust among underwriters who relied on 100% accurate information to make risk decisions.
Poor integration: Legacy systems were often hard to connect with modern rating engines or policy admin platforms, causing fractured workflows and manual rekeying.

Advances in AI for loss runs extraction tools—like Nomad Data’s Doc Chat—change the equation entirely. Modern systems require no pre-defined templates or training on your document types, understand context and semantic meaning, and work out-of-the-box with minimal configuration, regardless of incoming formats or carrier nuances.

How Nomad Data’s Doc Chat Accelerates Loss Run Analysis

AI-Powered Document Ingestion and Processing

Doc Chat ingests thousands of pages per minute, reading and interpreting every detail of each loss run received. Using advanced natural language understanding and purpose-built extraction models, the system finds not just preset fields, but semantically relevant information—no matter where it appears or how it’s phrased. Complex or ambiguous language is processed with domain-specific context, dramatically reducing the risk of missing subtle but important facts about prior claims.

Custom Workflow Integration and Output Formatting

One of Nomad’s hallmarks is white glove customization. We work directly with your underwriting, data, and technology teams to:

Define the precise data points needed from loss runs across property, casualty, liability, or specialty lines
Tailor output formats to fit your workflow—whether that’s structured spreadsheets, direct feeds to rating platforms, or detailed PDF summaries for human review
Automate document triage, data validation, and exception routing to maximize efficiency

This approach ensures that Doc Chat doesn’t just extract data—it supports your full submission workflow, delivering exactly what your underwriters need to move quickly and confidently.

Real-Time Interrogation for Underwriters

Underwriters often need to dig deeper—asking follow-up questions about specific losses, narrowing in on certain years or types of claims, or clarifying ambiguous entries. With Doc Chat, users can interactively query the extracted data and source documents, receiving instant answers with links to the relevant document page or section for easy verification.

Lightning-Fast Implementation and Ongoing Support

Unlike old-school document automation projects that took months to deploy and maintain, Nomad Data delivers live solutions in as little as 1-2 weeks. Our white glove service means our team handles the heavy lifting—from document schema mapping and output formatting to system integration and user training. You gain instant access to a domain-tuned, production-ready platform that scales effortlessly as your submission volume grows.

The Broader Impact: Unlocking New Business Opportunities

By automating and accelerating loss run analysis, underwriters can respond more quickly and accurately to brokers and insureds—winning business away from slower, less data-driven competitors. Carriers can analyze risk on books of business previously considered too complex or time-consuming to review. Portfolio-level analysis becomes practical, supporting improved reinsurance negotiations, block transfer pricing, and strategic risk appetite management.

Moreover, the integration of AI for commercial underwriting supports advanced analytics and data enrichment—enabling predictive claims modeling, more accurate pricing, and better loss control recommendations for insureds. Underwriters become true risk advisors rather than manual data gatherers, freeing up bandwidth for higher-value work and innovation.

Why Nomad Data is the Best Choice for Loss Run Analysis Automation

Nomad Data stands apart due to our unique fusion of cutting-edge AI technology and deep industry expertise. Our team’s experience spans insurance, document processing, and enterprise integrations, allowing us to deliver:

Proven accuracy and reliability, even with challenging formats
Seamless customization and integration to client workflows
End-to-end support—from needs assessment to ongoing optimization
Rapid implementation (1-2 week go-live) for quick time-to-value
Robust compliance, security, and auditability

With Nomad, you’re not buying an off-the-shelf product, but a tailored risk intelligence solution built to your specifications and supported by a responsive, expert team. Our clients report rapid ROI, often in less than one quarter, with significant time and labor reductions from day one.

Conclusion: Revolutionizing Risk Assessment in Commercial Insurance

The move toward loss run analysis automation with AI agents like Doc Chat marks a pivotal shift in commercial insurance underwriting. By eliminating manual bottlenecks, reducing the risk of errors, and surfacing actionable insights in minutes, Nomad Data empowers carriers and brokers to compete—and win—in a data-driven marketplace. Early adopters of these technologies are redefining internal processes, elevating service to insureds and brokers, and realizing dramatic improvements in both efficiency and underwriting result quality.

If your organization wants to harness the power of AI for loss runs extraction, commercial underwriting, and risk assessment, contact Nomad Data today. Our team will guide you through every step of the journey, ensuring a seamless, rapid, and high-ROI deployment tailored to your needs.