Platforms may leverage implicit social and usage data to challenge traditional industries like banking.
Earlier this week, I had an interesting tweet exchange with Giles Andrews, the founder of Zopa, one of the leading peer lending platforms. Clearly, Zopa and peer lending platforms of its ilk are going strong. The strong growth notwithstanding, they are a drop in the ocean of traditional lending models, as is often the case with emerging platforms. Will such platforms ever pose a compelling alternative to the banking industry? The answer to this question may lie in their ability to build a data-driven feedback loop.
A platform’s ability to use data often has a direct impact on the value proposition and benefits delivered to users, and consequently on the network effect. A platform that can deliver greater efficiencies through better utilizing data about its users may, in turn, attract more users (as well as more participation from existing users), which further increases the data that the platform captures about them and further strengthens its ability to deliver value to users. If the above statement sounds convoluted, that is because most descriptions of feedback loops are.
Peer lending platforms like LendingClub, OnDeck and FundingCircle are out to disrupt traditional lenders and financial institutions. Unlike banks which borrow and lend, while acting as gatekeepers that benefit from the spread, peer lending platforms connect buyers and lenders directly, bypassing the inefficient gatekeeper, much like Amazon’s Kindle Publishing bypasses the traditional editorial control of publishing houses. To their disadvantage, though, peer lending platforms do not have the trusted brands that regulated financial institutions do. But a data-driven feedback loop in peer lending may hold the key to ultimate disruption of the banking industry.
Usage data – A unique advantage
Peer lending platforms look at all the traditional sources of data while determining a borrower’s ability to repay a loan. They even look at data sources that a bank may never look at, like the Yelp score of a restaurant that is borrowing or the length of time a borrower has used the same email address, as signals for potentially fraudulent requests. But above this, they have the added advantage of looking at correlation patterns gleaned from actual usage data on the platform to determine the ability of a borrower to repay loans. As an example, most peer lending platforms have a slider allowing the borrower to decide what loan they would like to take. In an excellent whitepaper by Foundation Capital on the state of peer lending, Charles Moldow shares that the longer a borrower spends moving the slider up and down (and hence, potentially, debating her ability to return the loan), the more likely is she to return the loan. Such correlations help platforms improve their ability to curate participants over time.
At large numbers, such correlations have high predictive power.
Traditional FIs do not have the luxury of such data. As the platform gathers more data, two positive effects emerge:
The manual effort required to underwrite loans falls as algorithms take over
As we saw with Airbnb, feedback loops of this kind simultaneously improve the quality and the quantity of the core interaction that the platform enables. With greater curation, the quality of the core interaction improves. And with greater reliability (as in the case of Airbnb) and higher value generated (as in the case of peer lending platforms), the platform moves from an early adopter group and gains mainstream adoption. We’ve already seen it work out for platforms like YouTube, Airbnb, Elance-oDesk etc. I expect emerging platform that focus on improving curation and leveraging data in such feedback loops to become increasingly important going forward and pose a significant challenge to their traditional industry counterparts.
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