Social Media and Text Analytics

There is no doubt that the use of social media is ubiquitous in our society. Yet the implications of social media are enormous, with possibilities in financial services growing more each year. Industries will soon realize how text analysis can merge our social media driven society, and offer an unconventional and intuitive approach to the social lending industry. Let’s look at the current possibilities.

With conventional lending institutions, there has to be a set system in place that tracks and record consumer transactions and their credit history. However, countries and individuals lack the access to these lending institutions as well as confidence in borrowing and lending in an established lending organization. This leaves a huge untapped market for people that would like to borrow and/or lend but are not connected to a standard credit lending/borrowing institution to do so.

Several trends are emerging within text analytics that can make use of consumer data in the credit market and attempt to monetize on its findings. For example, in emerging economies, social media liquidity markets are being made possible by connecting individuals that want to lend with ones that what to borrow via credentials of reputation. This method can employ the use of text analytics by analyzing the abundant data of consumers and assessing the most reliable and stable borrower from someone who is most likely to default in paying back a debt obligation.

Tools such as SAS text miner can take the unstructured data of a person an aggregate the following information:

  • Their personal information, i.e., Name, Address, Assets, SSN
  • Type of loan or credit they are requesting, i.e., Mortgage, Car loan
  • Why they want this loan, i.e., purpose and intent
  • How will they use the borrowed money, i.e., correctly spend the money
  • Possible areas of expanding financial options

After this customer related information is gathered the text analytics tool can then interpret what would the prospective customers’ traditional credit score be. Once the benefits of the text analytics become known, it’s application in social media can generate an entire credit market from an entire people to people basis, in the sense of word of mouth lending, and the social connections made. This form of lending and borrowing is considered social lending.

Banks have caught on to the power of social lending and are attempting to use consumer data to supplement lending strategies leading to greater revenue streams. How you ask? By taking on a customer data mining approach of gathering information from social media profiles and interpreting it to lead to actionable predictions of consumer financial behavior. Banks can possibly find out events in a client or even prospective clients life, such as a baby on the way, or a couple that recently got married, and can interpret that information to market specific products and foster unique selling strategies. There are 3 things that banks can look for:

  1. Change in a clients financial situation, i.e., laid off, a substantial gain/loss in income
  2. Linkage to you social financial connections. “You are a reflection of your friends.” Banks can use relative events of your closest friends, let’s say on Facebook or Twitter to have a good idea of what is happening financially in your life. For example, if a friend tweets something on a housewarming party banks can infer that you are likely to be open on purchasing a home or already have one, and may be receptive to refinancing for example.
  3. Lastly – regarding up and coming life events, banks could know and use information on what is the next big move that is approaching in life and what it means to the banks business solutions, perhaps you own a home and you have expressed your family is outgrowing your current residence and may be considering taking out a mortgage.

Armed with this information, banks can make inferences on the events of prospective and current clients and tailor their sales strategies to the client’s specific needs.