As the online lending sector seeks to rebound from a wrenching year, it should look to Amazon for inspiration, the CEO of industry bellwether Lending Club said.
In many ways where online lending is in 2017 parallels the early days of online retail, Scott Sanborn said at the LendIt conference in New York on Monday. In that situation, new entrants disrupted an established industry, before going through a period of pain and change themselves during the dot-com bubble.
The original online retailers that survived and thrived after that period reinvented and expanded what they did, Sanborn said, citing Amazon as the prime example.
Much like how Amazon went from selling books to selling cloud services, Sanborn predicted online lenders that succeed well into the future will change how they do business.
Online lenders, he said, will have to think about “how they unleash their platform potential and amplify their core ambitions” to improve the loan experience. “This means, how do we enable one-click underwriting and deliver loan approval instantaneously?”
One way Amazon has prospered, Sanborn pointed out, was by allowing other retailers to use its platform.
“Amazon opened up and allowed other people to deliver products,” through its website, he said. “Today, platform sellers make up half of their unit volume.”
Likewise, Sanborn said, online lenders should look to partner with others.
“So we would control the customer experience, while others might provide some of the services,” he said. “We would manage the overall reputation, while also opening up the technology to other institutions.”
Sanborn acknowledged that 2016 was “a tough year for online lending.” Lending Club was one of the more notable online lenders
Still, he expressed optimism for online lending as a whole, pointing to the growth for not only his company, but other online lenders such as OnDeck and SoFi as well.
Another factor that will change lending for online lenders and traditional ones alike is data and how to mine and analyze the sheer amount of it institutions possess. American Express, for example, employs nearly 1,500 data scientists, said Ash Gupta, its president of global credit information management.
Big data, Gupta said at the conference, can improve not only credit models, but fraud monitoring as well.
“We can analyze transactions in seconds to improve [fraud] results,” he said.
Further, Gupta said, mining big data is helpful at “looking at buying patterns to make better offers to our customers.”
American Express employs machine learning and artificial intelligence capabilities to aid in these efforts, but still needs human analysis to make it more effective.
Machines “can give you credit model A, B, and C, but can’t tell you how we arrived there,” Gupta said. “We need to be able to explain to the customer exactly why we are taking the action that we are.”
For that reason, he said, machines and humans
“It’s important to have this technology, but it won’t completely eliminate humans from the equation,” he said.
