DataCubes raises $15.2 million in Series B
Commercial data and analytics company, DataCubes , announced it has raised $15.2 million led by Palm Drive Capital.
DataCubes uses machine learning and artificial intelligence to analyze a broad array of information sources, including semi-structured insurance submissions and over four billion objects in its data lake. Using these capabilities, DataCubes’ d3 Underwriting™ software platform empowers commercial insurers to optimize their underwriting workflow processes for SMB and middle market risks in all standard commercial and specialty lines of business.
This latest round of funding will support DataCubes’ investment in research and development as well as help expand the team, adding 50 new employees in 2020, to further support its growing clientele of commercial insurers, which includes The Hanover, Selective Insurance, RLI, Columbia Insurance Group, Penn National Insurance, Tangram, WCF Insurance and Synergy Coverage Solutions among dozens more that have not yet been publicly announced.
“The old way of underwriting risks is a drain on both a carrier’s resources and the customer experience it is able to provide,” said Kuldeep Malik, CEO and co-founder, DataCubes. “This round of funding will give us a boost to support the demand we are seeing from the underwriting community to fix inefficiencies and drive better expense and loss ratio results.”
The Series B funding round, which was led by Palm Drive Capital, included participation from Altos Ventures, NFP Ventures, Stage 2 Capital, MPK Equity Partners and existing investors including Seyen Capital and MK Capital. In total, DataCubes has raised nearly $18 million in less than three years.
Nick Hsu, partner at Palm Drive Capital and member of DataCubes’ board of directors added, “DataCubes’ rapid growth is a testament to the fact that there is a better way to underwrite commercial P&C risks. Carriers that continue to do business as usual, will lose out to the modern carrier who will operate faster, more accurately and at a reduced cost.”