With a broader U.S. economic recovery under way, loans to small businesses that fall under the $1 million mark have still been slow to recover.
A Y Combinator startup called Drip Capital is looking to step in with a new product for working capital that helps small businesses cover orders. Small business entrepreneurs working in this slice of the market typically rely on credit cards, which have substantially higher borrowing costs, or on community banks, which Drip argues are slower at evaluating cases.
Drip is entering the lending market at a time when many other startups are analyzing large swaths of data to evaluate credit risk and approve loans on the fly to consumers and students (like Max Levchin’s Affirm) or borrowers in developing countries (like Kiva founder Matt Flannery’s Branch Capital).
Drip Capital’s two founders Pushkar Mukewar and Neil Kothari, have experience in both the tech and financial worlds after managing debt portfolios at Capital One, Goldman Sachs and BlackRock. They met at Wharton.
Drip evaluates small businesses based on their live working orders from customers. They’re focused on the 5 million business-to-business focused companies in the United States, and are giving loans that they expect to be repaid in 1 to 4 months.
“Banks have an almost one-size-fits-all product,” said co-founder Neil Kothari. “But there’s a pretty substantial difference in how banks cater to larger clients versus smaller clients.”
Part of that dynamic stems from the risk of investing in established businesses versus smaller ones. But part of it also comes from the time that a lender needs to invest in evaluating borrowers. It’s just inherently more costly relative to the return to evaluate a small business’ potential. Many of these newer lending startups are hoping to grind those differences down by marrying machine learning and big data with human skill.
Drip will evaluate an application by looking at order data and evaluating future revenue. They’ll look at a company’s contract purchase and work orders. Then they’ll provide a loan against that future revenue in as little as 48 hours. They say their typical borrowing costs varies from 1.25 percent a month to about 2 to 2.5 percent per month. One of their earlier customers was a potato chip maker that got placement in Whole Foods, but needed capital from Drip to cover the order.
While Drip is mostly evaluating loans by hand now, they say they can implement optical character recognition and algorithms that will evolve over time to measure risk.
“Verification, to some extent, will always have to be partly manual because it’s difficult to sort out proper samples of orders,” said co-founder Pushkar Mukewar.
The company has raised a “friends and family” round so far, and will have to raise debt capital in the future to support their balance sheet.