Property & Casualty Insurance
Accelerating Operational Efficiency Through Automated Loss-Run Data Extraction
95% accuracy
Extraction accuracy across loss-run reports
Reduced manual effort
Faster retrieval & reporting with automated extraction
Secure & scalable
Private cloud deployment within client security perimeter
Client: A leading national property & casualty insurance company, needed a faster, more accurate way to extract data from Loss Run reports. Fulcrum Digital helped automate the extraction, validation, and processing of report data, reducing reliance on manual work and improving the speed and accuracy of insurance data operations.
The Challenge
Manual Loss-Run Data Extraction Was Slowing Operations and Increasing Risk
The client relied on manual processes to extract data from Loss Run reports. As report volumes and data complexity grew, the process became harder to scale, more vulnerable to errors, and less efficient for teams that depended on timely, accurate information.
High data volumes strained manual reporting
The client had to manage large volumes of loss-run data across insurance operations, making manual extraction and reporting slow, inconsistent, and difficult to scale.
Reports arrived in inconsistent formats
Loss Run reports came in varied structures across carrier formats, making it harder to extract claim details, reserves, and payment information through a standard process.
Manual processes created delay and error risk
Manual extraction increased the risk of delays, data leakage, extraction errors, and inconsistent reporting across business teams.
Compliance and security requirements shaped the solution
The process had to support regulatory requirements across US and UK jurisdictions while keeping sensitive insurance data within a secure operating environment.
the Solution
Automated Data Extraction for Faster, More Reliable Loss-Run Processing
Fulcrum Digital delivered an automated approach to retrieve, validate, and process data from Loss Run reports more efficiently. The solution was designed to reduce manual effort, improve extraction accuracy, and support higher-volume processing across impacted geographies.
DocIntel-powered extraction
FD RYZE® DocIntel parsed structured and semi-structured Loss Run reports across varied carrier formats, extracting claim details, reserves, and payment data.
Four-phase implementation model
The solution followed a phased approach across POC, extraction validation, conversational interface, and business insight assistance.
Nexus RAG conversational querying
Nexus RAG enabled users to ask natural-language questions and receive cited responses from extracted and structured loss-run data.
Private cloud deployment
The solution was deployed within the client’s private cloud infrastructure, keeping data inside the client’s security perimeter.
F1 Connector integration
The F1 Connector supported integration with industry-specific file-sharing platforms, reducing manual download workflows.
Structured data output
Extracted data was normalized into a queryable format for downstream analytics, actuarial modeling, and regulatory reporting.
results
A More Accurate and Efficient Loss-Run Data Operation
| Operational Area | Improvement |
|---|---|
| Extraction accuracy | Achieved 95% accuracy across Loss Run report extraction. |
| Manual effort | Reduced manual extraction and retrieval work through an automated pipeline. |
| Data usability | Converted inconsistent report formats into normalized, queryable information. |
| Downstream readiness | Prepared extracted data for analytics, actuarial modeling, and regulatory reporting. |
Why Fulcrum
What Made This Work
Insurance reporting context
Fulcrum understood that Loss Run extraction was not just a document processing task. The data had to support reporting, compliance, actuarial use, and business decision-making.
Security-first implementation
The engagement required automation without moving sensitive insurance data outside the client’s controlled environment.
Structured-data discipline
Fulcrum focused on making extracted information usable after extraction, not just readable from the source document.
Practical path to adoption
The phased approach helped move the solution from extraction validation to conversational access and business insight support without forcing a single big-bang change.
FAQ
Frequently Asked Questions
Answers to the most common questions about the engagement, implementation approach, and business impact.
Why is manual Loss Run data extraction a problem?
Manual extraction is time-consuming, difficult to scale, and more vulnerable to human error, especially when reports contain high data volumes and extensive data points.
What benefits can automated Loss Run data extraction deliver?
Automated extraction can improve operational efficiency, reduce manual dependency, support faster decision-making, and improve the accuracy of extracted data.
How can insurance companies automate Loss Run report data extraction?
Insurance companies can automate Loss Run report data extraction by using a structured process to retrieve, validate, and process report data without relying on manual handling.