Ocrolus Raises $ 80 Million at Over $ 500 Million Valuation to Automate Document Processing for Fintechs and Banks – TechCrunch
If you’ve ever had to take out a loan, you know how many documents are involved in the approval process.
It’s a lot.
The process is tedious and time consuming, and in many more cases than you might expect, always manual.
Ocrolus is a startup that hopes to change that with an automation platform that it claims analyzes financial documents with over 99% accuracy. To that end, the New York-based company announced today that it has raised $ 80 million in Series C financing for its mission to help lenders automate the underwriting process.
Fin VC led the funding, which values New York-based Ocrolus at “north of $ 500 million” and brings its total funding raised since its inception in 2014 to more than $ 100 million. Thomvest Ventures, Mubadala Ventures, Oak HC / FT, FinTech Collective, QED Investors, Bullpen Capital, ValueStream Ventures, Laconia, RiverPark Ventures, Stage II Capital and Cross River Bank also participated in the last round.
The company is a company that shows refreshing transparency about its finances. From the first quarter of 2018 to the second quarter of 2021, Ocrolus increased its revenue from $ 1 million to $ 20 million in annual recurring revenue (ARR), according to co-founder and CEO Sam Bobley. It has managed to attract a number of top fintech clients including Brex, Enova, LendingClub, PayPal, Plaid, and SoFi.
And it was done by spending roughly $ 10 million in total sales and marketing expenses, Bobley said.
It is now ready to tackle more traditional financial institutions as well. With its new capital, the start-up plans to “more aggressively” create products for the mortgage and banking sectors and expand its operations in the United States.
Ocrolus uses a combination of technologies including OCR (Optical Character Recognition), Machine Learning / AI and Big Data to analyze financial documents. But what sets it apart, says Bobley, is the Human-in-the-Loop (HITL) component also used in document processing to further ensure accuracy.
In a nutshell, the company aims to help lenders make “faster, data-driven decisions.” Its technology can categorize financial documents, capture key data fields, detect fraud and analyze cash flow, according to Bobley. It is clear that the COVID-19 pandemic has caused a digital acceleration in many industries, with financial services among the most affected. Before the pandemic, less than 1% of loans worldwide were made online, notes Bobley. But since the COVID-19 outbreak, demand for digital lending technology among traditional financial services companies has “accelerated dramatically.”
“Now, as COVID-19 has forced financial institutions to evolve, every lender and bank has no choice but to offer online options to customers,” he said.
Fin VC general partner and founder Logan Allin agrees, noting that fintechs and banks are still grappling with “mountains” of digital and paper documents to extract the financial data they need to process and analyze during the loan application process.
“Ocrolus has become one of the pillars of the fintech ecosystem and solves these challenges using OCR, AI / ML and big data / analytics,” he wrote via email. “We believe Ocrolus is just getting started in terms of the use cases and scope of its platform and we are excited about this unconstrained TAM. “
Ocrolus is not the only player in the space, but what helps drive its growth and set it apart, according to Allin, are its fraud detection and compliance overlay capabilities, as well as its analytical capabilities. and benchmarking for its customers.
Interestingly, Ocrolus started with the goal of automating the Medicaid long term care application process. He found that “well-paid professionals” spent their time combing through documents page by page, line by line.
“So we started to research the problem and what we learned was that the existing technology on the market just wasn’t precise enough to be useful,” Bobley told TechCrunch. Tech giants like Microsoft, Amazon, and Google offer OCR products, but many struggle to read text in PDFs and images, especially when documents are semi-structured or unstructured, Bobley said. It is also difficult for machines to understand all the various formats.
“We wanted to create a new way of doing things. And what we’ve done is we’ve built a machine learning-based platform that also integrates humans, ”he said. “The goal was that no matter what the submitted document looks like – whether it is a clean Chase document or a blurry cell phone image from a Kansas community bank – we will be able to process it with perfect precision. “
Whatever the solution can’t do automatically, it breaks down into smaller tasks and directs them to its own team of analysts and quality control specialists to perform data verification. The company then uses a series of algorithm checks to make sure its employees have done the job correctly.
“In short, we deliver perfectly accurate structured digital data for every file we process,” Bobley said.
About a year into the life of the business, the team realized that “loans were a much bigger and more attractive market opportunity” than Medicaid.
“We really entered the fintech lending space at the right place at the right time,” Bobley told TechCrunch.
By 2016, the company launched its official product and was generating revenue.
“One of the benefits of our software is that it has really helped fintech lenders scale,” said Bobley. “Our product, frankly, is 10 times better than doing it manually. Once we processed the loans instantly or in minutes, while everyone was taking hours or days, people started coming to us, including the big fintech lenders, without us even having a strength to sale.
And for Bobley, getting started is more than just speeding up the loan process. It is also a question of financial inclusion.
“Our platform helps lenders automate underwriting and intelligently leverage cash flow and income data for credit scoring,” said Bobley. “By enabling lenders to analyze various sources of financial data more quickly, Ocrolus is leveling the playing field for every borrower, providing expanded access to credit at a lower cost. “
The company also plans to use its new capital to keep hiring, with a focus on its machine learning and data science teams. He is also planning to open a new data quality control facility in Florida to accommodate financial institutions and government entities with onshore data requirements.