Deep Vector Secures $1.5 Million Funding to Transform Insurance Data Processing

Deep Vector Secures Funding to Revolutionize Insurance Data Extraction

Deep Vector, an innovative artificial intelligence platform tailored for the intricacies of insurance data extraction, has successfully closed its seed funding round, raising $1.5 million. This round was co-led by Aperture Venture Capital and InsurTech NY, marking a significant milestone for the burgeoning start-up, formerly known as Loss Scan.

The need for efficient data processing in the insurance industry has never been clearer, as the sector is inundated with various forms of analog documents that are critical for underwriting. These include claims loss runs, ACORD forms, motor vehicle records, and other business documents. Deep Vector leverages a proprietary algorithm combined with machine learning capabilities to convert these numerous document types—over 5,600 unique formats specific to each insurance carrier—into actionable data.

David Gritz, Managing Director at InsurTech NY, pointed out the challenges faced by insurers in optimizing their claims and loss run analysis processes. He stated, "Insurers have little incentive to make their loss run and claims analysis process easier because it is simply a cost center." Deep Vector aims to bridge the long-standing gap between insurance brokers and underwriters, especially in the realm of complex commercial accounts, saving thousands of hours in data analysis and processing.

According to the company, its platform is already in use by over 30 of the top 100 insurance brokerages, highlighting the rapid adoption of its technology. Deep Vector is not just a novel entrant; it is set to become the essential translation layer for insurance data extraction, providing much-needed efficiency and insight to industry players.

Garnet Heraman, General Partner at Aperture VC, expressed enthusiasm for Deep Vector's growth potential, articulating, "The insurance industry is the largest data repository for business. Deep Vector unlocks that data for better risk modeling, resource management, and ultimately better customer experiences."

The founders, Scott Knowles and Wesley Janse van Rensburg, bring extensive experience to the table. Knowles previously worked as a commercial insurance broker, which gives him a first-hand perspective of the industry's challenges. The team previously sold their insurance analytics start-up, Modgic, to a larger entity, Zywave, before embarking on this new venture.

Understandably, a significant strides for Deep Vector had to be made in overcoming the limitations imposed on data access in insurance underwriting processes. "Deep Vector's product Loss Scan breaks down the barriers that have locked away valuable claims data for decades," Knowles explained. The platform automates the extraction of information from complex loss runs, allowing insurance professionals to tap into previously hidden insights that were often locked away in tedious PDFs and spreadsheets.

The implications are profound—not only does automation save time, but it fundamentally changes how underwriters evaluate risk and how brokers engage their clients. By streamlining data extraction, Deep Vector allows professionals to divert their focus from paperwork to meaningful analysis and strategic advice.

Excitement builds for the upcoming InsurTech Spring Conference, where Deep Vector will showcase its state-of-the-art technology on April 2-3 in New York City. This event is anticipated to bolster the company’s visibility and further integrate its services within the insurance community.

In summary, Deep Vector’s methodology stands to disrupt traditional practices within the insurance space by making critical data much more accessible and manageable. As the insurance industry continues to evolve, so too does the potential for artificial intelligence to serve as a transformative instrument in its ongoing digital transformation efforts.