Financial document automation platform Ocrolus pulled in $80 million in Series C funding today. The round was led by Fin VC and included participation from Thomvest Ventures, Mubadala Capital, Oak HC/FT, FinTech Collective, QED Investors, Bullpen Capital, ValueStream Ventures, Laconia, RiverPark Ventures, Invicta Growth, Stage II Capital, and Cross River Bank.
The New York-based company now boasts $127 million in funding and is valued at over $500 million. Ocrolus plans to use the funds to expand U.S. operations and “more aggressively” build products for banking and mortgage lending.
“Our platform helps lenders automate underwriting and intelligently leverage cash flow and income data for credit scoring,” said Ocrolus Co-founder and CEO Sam Bobley. “By enabling lenders to more quickly analyze diverse sources of financial data, Ocrolus levels the playing field for every borrower, providing expanded access to credit at a lower cost.”
Ocrolus was founded in 2014 to create a document processing automation solution that helps lenders classify, capture, detect, and analyze financial documents to make better lending decisions. To accomplish this, the company leverages AI, machine learning, and human-in-the-loop (HITL) optimization. The HITL component serves as Ocrolus’ key ingredient to differentiation because it ensures an enhanced level of accuracy when analyzing data derived from documents.
The company, which won a Best of Show award at FinovateFall last week for its document analysis technology, has benefitted from the recent acceleration of digitization brought on by COVID. In today’s lending environment, FIs need to offer online options to compete. We spoke with Ocrolus’ VP of Solutions Nicole Newlin last year on the effects of this digitalization.
Ocrolus’ client list is as impressive as it is extensive, including firms such as Brex, Enova, Lending Club, PayPal, Plaid, and SoFi. Accommodating for a recent uptick in demand, the company added more than 75 employees this year and plans to boost its hiring efforts next year, focusing specifically on machine learning and data science professionals.