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Your social network profile used in credit scoring models

Who you like on Facebook might end up in your loan application being denied. This article was posted in MotherJones as Your Deadbeat Facebook Friends Could Cost You a Loan.

You can get a loan to buy this house if you have the right Facebook friends

My opinion: Some lending institutions just started to test new predictive models that include your social (public) data - what you post, your friends - what the whole world can see. The risk if people going private, then the data is no longer available, creating instability in predictive scoring models.

The article:

It's already well known that Facebook and other social media networks harvest user data and sell it to companies that use that info to peddle their products to consumers. But some lenders have begun to find a new use for this information, scrutinizing Facebook, Twitter, and LinkedIn data to determine the credit-worthiness of loan applicants. It's an unprecedented practice that consumer advocates say can be unfair or discriminatory—and one that is poised to only become more prevalent in the years ahead.  

Among the US-based online lenders that factor in social media to their lending decisions is San Francisco-based LendUp, which checks out the Facebook and Twitter profiles of potential borrowers to see how many friends they have and how often they interact; the company views an active social media life as an indicator of stability. The lender Neo, a Silicon Valley start-up, looks at the quality and quantity of an applicant's LinkedIn contacts for clues to how quickly laid-off borrowers will be rehired. Moven, which is based in New York, also uses information from Twitter, Facebook, and other social networking sites in their loan underwriting process.

Several international lenders have been using similar tactics for a while. Lenddo, for example, which makes loans to folks in developing countries, denies credit to applicants who are Facebook friends with someone who was late repaying a Lenddo loan. Big banks have not yet jumped on board with this controversial credit-vetting method, but consumer advocates and financial industry experts say it's probably only a matter of time.

Read full article at http://www.motherjones.com/politics/2013/09/lenders-vet-borrowers-s...

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Comment by Vincent Granville on September 20, 2013 at 2:50pm

A reader mentioned that data governance and regulations would make such algorithms illegal. My opinion is that it depends what kind of metrics you use. Here are a few that seem relatively benign: 

  • Presence/absence of Facebook account (if Facebook account is found, then use algorithm to detect if it is fake, hijacked or real) 
  • Multiple accounts attached to person
  • Number of friends
  • Category attached to profile (using a taxonomy to assign categories) 
  • Account recency 
  • Account popularity (number of genuine likes) 
  • Number of posts 

Of course the content (postings) might not represent the reality, thus metrics based on content should be carefully calibrated. But if a solid, consistent pattern emerges, and has great predictive power, why not leverage it? For instance, if age provided on application does not match age on Facebook, and this turns out to be a good predictor. 

It's not any different from insurance companies calling your neighbors to learn about your lifestyle, before making a decision about your application.

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