Document processing and back-office automation startup Ocrolus is launching a cash flow-informed credit (CfiC) model that uses cash flow analytics from bank statements to determine the likelihood of default.
“CfiC modeling provides lenders with a detailed understanding of the unique financial dynamics of their customers and allows them to tightly link cash flow health with future repayment behavior,” Ocrolus Head of Analytics David Snitkof said in a press release emailed to PYMNTS on Thursday (Sept. 9).
“They can identify individuals and businesses that they may not have approved using legacy credit bureau scores, opening up the potential for more business volume; conversely, the score will also reveal entities to whom they shouldn’t be lending, significantly reducing defaults,” he added.
The Ocrolus CfiC solution gives lenders in all industries the ability to create credit-scoring and risk tolerance models that can be used in lieu of traditional credit scores to determine the creditworthiness of the loan applicant.
Founded in 2014 and headquartered in New York City, Ocrolus specializes in analyzing financial documents with an accuracy topping 99 percent, per the release. Its CfiC scoring program taps “key cash flow analytics” by leveraging the company’s “industry-leading document classification and capture engine.” The technology used by the startup morphs disordered documents into systematic data sets that can be used to automatically spot fraud and get rid of team-based manual processes like analytics and financial calculations.
Ocrolus developed the CfiC model using a scoring schematic that uses analytics from over 100 bank statements. Insights include cash flow, balances, debt capacity, and more. Lenders can use this data to tailor the product for their own risk models.
As part of the development journey, Ocrolus worked with several lenders and retro-analyzed the bank statements and data used to loan money to thousands of small and medium-sized businesses (SMBs). The result was that by adding the Ocrolus cash-flow features to lenders’ data, there was more than a 20 percent uplift in predictive power, according to the release.
“Two indicators of the success of our model are lower loss rates than other online lenders and higher renewal rates among our customers,” said ForwardLine Financial CEO Sri Kaza, an early adopter of the Ocrolus CfiC. “Both measurements can be attributed to our mission-based approach and our effective use of technology.”