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Big (Alternative) Data in Credit Decisioning

Slate Wednesday published a blog post profiling a number of companies involved in P2P lending and online short-term and payday lending which rely on social networking data from borrowers to help gauge credit risk. The post also examined the technologies available to the companies via vendors such as Kreditech.

Lenddo, LendUp, and Wonga all use such alternative data and scoring technologies to help screen applicants. Some of the elements involved in these types of alternative screening processes can include location data (GPS, micro-geographical), social graph (likes, friends, locations, posts), behavioral analytics (movement and duration on the webpage), people’s e-commerce shopping behavior and device dats (apps installed, operating systems). The core thesis of the piece:

All these efforts are premised on the sensible idea that current models of assessing creditworthiness focus on too few indicators, shutting off many potential borrowers who pay their bills on time but don't have good (or any) credit histories. “Big data” can separate the lazy slackers from those who truly deserve better loan terms. The goal, then, is to get as many data as possible, perhaps even nudging potential applicants to pre-emptively disclose as much information about themselves as possible.
The focus of the blogger’s argument is privacy. Even though new technologies may facilitate greater access to credit, what is the price in terms of personal privacy for those needing expanded access?

Click here to read more from Slate.

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