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Facebook new patent for credit rating through social networks

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Have you heard about Facebook’s new patent to evaluate your credit rating based on your social network ? Everyone refers to it but despite all my efforts I’ve been unable to find a new such patent application filled in by Facebook in the US Patent and Trademark Office database. How curious … Yet, many articles refer to an official document. My guess is that few journalist have actually taken the time to read what they referred to. The patent they refer to is this one. And it has absolutely nothing to do with credit rating, loan or whatsoever. I found the right one and am analyzing it for you in this article.

This is the real patent … filled in 2012

The text quoted by journalists actually refers to an old 2012 patent filled under number 13/565,500 entitled “Authorization and authentication based on an individual’s social network”. To make it easier for you to access the document I made it available in pdf.

The primary purpose of this invention is to let an individual access content through a social network identification. The patent reads

The invention provides a method of authorizing transmission of content to an individual as a way to filter out unwanted communication such as SPAM or content that the individual might find to be offensive, and a method of authenticating individuals for access to content or service that makes the content or service available to more users while limiting access to potentially abusive users of the content or service.

Does it ring a bell ? You nailed it ! It’s Facebook-Connect.

What about credit rating through social networks ?

The part of credit rating is only a tiny section in the patent application and is only depicted on a diagram (figure 9). It reads like this

FIG. 9 is a flow diagram that illustrates the steps carried out in authenticating B for access to a loan. In Step 910, the lender receives a request for a loan from B. The request includes certain identifying information of B, such as B’s e-mail address. In Step 920, in accordance with the methods described in the application, “Method of Sharing Social Network Information with Existing User Databases,” (U.S. patent application Ser. No. 10/854,610, issued as U.S. Pat. No. 8,478,078), filed Jun. 14, 2004, this lender makes a request to a social network database for a graph representation of B’s social network and receives the graph representation of B’s social network. In Step 930, a black list that is maintained for B is requested and received from the social network database in the same manner as in Step 920. In Step 940, a gray list is derived from the black list and B’s social network In Step 950, a breadth first search (or alternatively, a depth first search) is conducted on B’s social network to generate a white list. All members of B’s social network who are connected to B along a path that does not traverse through any unauthorized nodes (i.e., individuals identified in the gray list) get included on this white list. Optionally, the lender may specify a maximum degree of separation value (e.g., N.sub.max). If it is specified, the white list will include only those members of B’s social network who are within N.sub.max degrees of separation from B. In Step 960, the credit ratings of individuals in the white list are retrieved and weighting factors are applied to the credit ratings based on the degree of separation between the individual and B. As an example, a weighting factor of 1/10.sup.N may be applied to the credit ratings, where N is the degree of separation between the individual and B. If the average credit rating is above a minimum score, B is authenticated and the processing of B’s loan application is permitted to proceed (Steps 970 and 980). If not, B is not authenticated, and B’s loan application is rejected (Steps 970 and 990).

I guess you didn’t want to read through all this complicated lawyer wording. So let me help you understand what’s behind all this. In particular let’s explore together how Facebook will eventually know even more about us and how banks will be fooled.

Analysis of Facebook patent for credit rating through social networks

Let’s now dig into this patent and try understand what it really means. There are actually three main steps in the process :

  1. An individual applies for a loan. This application contains the email address
  2. The bank uses the email address to get a visual representation of the applicant’s social network and social friends’ credit ratings
  3. A decision is taken by the bank to proceed or not, based on friends’ credit ratings within a given range (i.e. the degrees of separation : first, second, …)

Where is the trick ? What will Facebook really know about you ?

When I read the news about Facebook’s “new” patent I immediately wondered : “what will Facebook get out of that ?”. What you should realize is that behind each new service of Facebook there is always a dark side. Facebook will always try to maximize its return.

In the present case the secret is hidden in this sentence :

“a black list that is maintained for B is requested and received from the social network database”

Do you understand what it means ? It means that a bank may be requested to upload the credit ratings of its customers onto Facebook servers. The credit ratings list will be maintained by Facebook. The bank will never be able to own the results. It will always depend on Facebook to visualize the results. How frightening is that?

My take

Although its application remains hypothetical, this patent shows once again the aims of Facebook. Facebook want to own all possible data about you. It wants to become a central repository for data on the humanity.

In 1911 the USA recognized that the monopoly owned by the Standard Oil of Rockfeller had to be broken down into pieces. It was too dangerous for the democracy. A century later, the same situation is happening again. Today’s oil is called “data”. Two companies are dangerously accumulating data : Google and Facebook.

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Author: Pierre-Nicolas Schwab

Pierre-Nicolas est Docteur en Marketing et dirige l'agence d'études de marché IntoTheMinds. Ses domaines de prédilection sont le BigData l'e-commerce, le commerce de proximité, l'HoReCa et la logistique. Il est également chercheur en marketing à l'Université Libre de Bruxelles et sert de coach et formateur à plusieurs organisations et institutions publiques. Il peut être contacté par email, Linkedin ou par téléphone (+32 486 42 79 42)

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