Groundspeed was selected as a Top 10 Insurtech Startup for 2022 by Insurance CIO Outlook. This is the repost of our article in Insurance CIO Outlook’s Insurtech Startup edition 2022:

 According to Accenture, 80 percent of the data received by insurance underwriters is sent via email with sensitive information like applications, exposure schedules, and loss runs attached within the body. Extracting meaningful information from these unstructured data sources and documents is arduous, typically taking days, if not weeks. This manual approach to consolidating sensitive information often leads to delays that can potentially weaken an insurance policy’s risk assessment. This is precisely where Groundspeed comes into the picture. 

Groundspeed is an insurtech company that assists commercial insurance carriers in automating the collection of unstructured data across varied sources and formats to unlock actionable insights. Its emphasis on data value helps clients rapidly structure and enrich high-quality data in close to real-time. Groundspeed also provides an AI-driven data pipeline platform backed by human curation, which automatically captures all inbound submission data, extracts insights, and structures the information into a useful format, streamlining the submission, rating, and policy underwriting process. Through these capabilities, Groundspeed optimizes productivity for underwriters and carriers. 

“Our unique data extraction approach removes the need for manual data entry, providing underwriters with fast, actionable information that can help enhance carriers’ risk selection, underwriting efficiency, and business growth.”

– Eric Kobe, CEO of Groundspeed 

Groundspeed has developed a step-by-step data extraction process, which begins when their advanced data pipeline connects all inbound documents sent to underwriters via dedicated APIs. Then, automation decodes, cleans, and structures the collected data to provide a reliable and transparent result. Meanwhile, the platform’s ML models enrich the data by adding Groundspeed’s own analytics and third-party data sources, providing a detailed and structured dataset. Most importantly, Groundspeed’s failsafe quality assurance uses a proprietary, tech-enabled human-in-the-loop process, ensuring no mistakes are made during data annotation. As a result, it returns accurate data to carriers in near real-time and helps them glean critical insights to boost strategic decision-making. 

Groundspeed’s models also help underwriters assess an open claim’s loss run and develop a loss run reserve score, which is generated by predicting the expected change in the total incurred value on a 5-point scale. 

“Because these datasets are both complete and accurate, they enable underwriters and data science teams to see all the elements of risks, helping carriers find, prioritize and quote the best submissions.”

– Naim Falandino, CTO at Groundspeed

Falandino’s statement is reflected in a recent instance where an insurance carrier sought the help of Groundspeed to streamline their unstructured documents and rapidly gain insights. The client was initially outsourcing their inbound document submissions, manually typing and only collecting 10 percent of data fields. This legacy process typically took two to three weeks. However, by leveraging Groundspeed’s data pipeline platform that automatically ingests all submissions, delivery time for structured data improved to just an hour on average. But even more importantly, nearly 85 percent of those submissions were returned almost immediately, in “near-real-time.” This, in turn, helps carriers improve the time from application submission to quote by an enviable 25 Percent. 

At the heart of such successes is Groundspeed’s seasoned team of industry experts who understand the most significant pain points for insurance carriers and enable the development of tailored solutions capable of addressing their requirements. Looking ahead, Groundspeed aims to continue enhancing the operational efficiency of its carrier clients by untangling inbound claims processing through robust enrichment analyses and predictive models. 

Click here to view the original publication.

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