The Challenge

For property insurance underwriters, intake and processing of insurance submissions, including the data within a stack of corresponding documents, is not a nice to do – it’s a must do business critical operation. 

However, the FurtherAI customer in this case faced the same conundrum almost every Property Insurance underwriter faces – that being a must-complete process but with that process being exclusively manual. 

Ability to Intake Submissions & SOV’s is a Must 

On one hand, like all property underwriting organizations, the FurtherAI customer could not even begin to underwrite a property risk without extracting the risk data from ACORD applications and SOV’s. 

No Opportunity Automate Intake, Historically 

However, on the other hand, automating or efficiently extracting and processing SOV data is extremely difficult to achieve due to the high variance in formatting, structure and data type and inclusion the 30k+ P&C agents licensed in the US submit with. 

However, this has all changed with the recent advent of LLM’s and AI Teammate solution providers like FurtherAI who build on top of the newly in-place LLM and AI ecosystem.  

The Solution: FurtherAI’s AI Assistant

FurtherAI’s state-of-the-art AI Assistant automates and optimizes the submission intake process. With FurtherAI, this MGA is now able to efficiently intake Property Insurance Submissions on an automated, AI Assisted basis. 

Automation Speed: 30x

From a speed perspective, the average “time to clear” a submission went from ~ 32 minutes to about 1.

Streamlined Submission Processing: 

• Incoming submissions were forwarded to the AI Assistant, which extracted and structured data from documents like SOVs, ACORD forms, and Loss Runs.
• The AI converted SOVs into a standardized format, ensuring consistency and ease of use for underwriters.
• Data from Loss Runs was extracted, summarized, and integrated with other submission data.

Automated Risk Insights and Data Enrichment:

• The AI created detailed risk summaries and flagged missing data from submissions, ensuring no critical information was overlooked.
• It enriched submissions with data from external web sources, overlaying valuable context for the underwriting team.

Triage and Prioritization:

• The AI triaged submissions, ensuring that the most relevant and high-priority cases reached underwriters quickly, reducing manual backlogs.
• Eligibility checks were automatically performed to ensure submissions met underwriting criteria before advancing in the process.

Underwriting Efficiency: 200%+ Gain + 99% accuracy 

Within 3 months, the AI Assistant processed over $20 billion in Total Insured Value (TIV) and saved more than 2,000 hours of manual effort by automating data extraction and enrichment tasks previously handled by underwriting assistants. This led to a 200% improvement in underwriting efficiency within the first 3 months.

This was achieved through faster processing times, enhanced data accuracy, and the elimination of repetitive manual tasks. Brokers experienced materially faster quote turnaround times, significantly improving their service experience.

The system achieved almost 100% accuracy, reducing errors associated with manual data entry and enabling underwriters to focus on complex decision-making.

The Bottom Line

The AI Assistant not only ensured faster and more accurate submission processing but also empowered underwriters to make data-driven decisions quickly and confidently.

This success in automating their commercial property line of business has inspired the MGA to expand the AI’s capabilities to other insurance programs, cementing their position as a market leader in specialty insurance innovation.