In a new blog post for the International Monetary Fund, four researchers presented their findings from a working paper that examines the current relationship between finance and tech as well as its potential future. Gazing into their crystal ball, the researchers see the possibility of using the data from your browsing, search, and purchase history to create a more accurate mechanism for determining the credit rating of an individual or business. They believe that this approach could result in greater lending to borrowers who would potentially be denied by traditional financial institutions. At its heart, the paper is trying to wrestle with the dawning notion that the institutional banking system is facing a serious threat from tech companies like Google, Facebook, and Apple. The researchers identify two key areas in which this is true: Tech companies have greater access to soft-information, and messaging platforms can take the place of the physical locations that banks rely on for meeting with customers.
The concept of using your web history to inform credit ratings is framed around the notion that lenders rely on hard-data that might obscure the worthiness of a borrower or paint an unnecessarily dire picture during hard times. Citing soft-data points like “the type of browser and hardware used to access the internet, the history of online searches and purchases” that could be incorporated into evaluating a borrower, the researchers believe that when a lender has a more intimate relationship with the potential client’s history, they might be more willing to cut them some slack. […] But how would all this data be incorporated into credit ratings? Machine learning, of course. It’s black boxes all the way down. The researchers acknowledge that there will be privacy and policy concerns related to incorporating this kind of soft-data into credit analysis. And they do little to explain how this might work in practice.