The Role of Social Media in Extending Financial Opportunities – FinTech Examples

June 19, 2017     By : Elena Mesropyan

Facebook alone gathers 98 personal data points for targeted ads

The number of social media users worldwide has reached 2.34 billion and is expected to grow to ~2.95 billion by 2020. Social media users generate 500 million tweets every day, and share 1.3 million pieces of content on Facebook every minute of every day, not mentioning other widely adopted channels. Two things allowed social media to become a powerful machine: the level of engagement reached through scale, and permissions.

While the level of engagement on social media channels has been quiet explored, permissions are a bit more curious. The average user will spend over five years of his/her life on social media, getting the latest news, engaging with friends and acquaintances, etc., paying a rarely-spoken-about price in exchange – precious personal data. How much does Facebook, for example, know about its users? According to its new targeted ad education portal and updated ad preference settings, Facebook puts together 98 personal data points that it knows about its users. For those really curious about all 98 data points Facebook is using to target ads, here’s the full list pulled by equally curious people:

  1. Location
  2. Age
  3. Generation
  4. Gender
  5. Language
  6. Education level
  7. Field of study
  8. School
  9. Ethnic affinity
  10. Income and net worth
  11. Home ownership and type
  12. Home value
  13. Property size
  14. Square footage of home
  15. Year home was built
  16. Household composition
  17. Users who have an anniversary within 30 days
  18. Users who are away from family or hometown
  19. Users who are friends with someone who has an anniversary, is newly married or engaged, recently moved, or has an upcoming birthday
  20. Users in long-distance relationships
  21. Users in new relationships
  22. Users who have new jobs
  23. Users who are newly engaged
  24. Users who are newly married
  25. Users who have recently moved
  26. Users who have birthdays soon
  27. Parents
  28. Expectant parents
  29. Mothers, divided by “type” (soccer, trendy, etc.)
  30. Users who are likely to engage in politics
  31. Conservatives and liberals
  32. Relationship status
  33. Employer
  34. Industry
  35. Job title
  36. Office type
  37. Interests
  38. Users who own motorcycles
  39. Users who plan to buy a car (and what kind/brand of car, and how soon)
  40. Users who bought auto parts or accessories recently
  41. Users who are likely to need auto parts or services
  42. Style and brand of car you drive
  43. Year car was bought
  44. Age of car
  45. How much money user is likely to spend on next car
  46. Where user is likely to buy next car
  47. How many employees your company has
  48. Users who own small businesses
  49. Users who work in management or are executives
  50. Users who have donated to charity (divided by type)
  51. Operating system
  52. Users who play canvas games
  53. Users who own a gaming console
  54. Users who have created a Facebook event
  55. Users who have used Facebook Payments
  56. Users who have spent more than average on Facebook Payments
  57. Users who administer a Facebook page
  58. Users who have recently uploaded photos to Facebook
  59. Internet browser
  60. Email service
  61. Early/late adopters of technology
  62. Expats (divided by what country they are from originally)
  63. Users who belong to a credit union, national bank or regional bank
  64. Users who investor (divided by investment type)
  65. Number of credit lines
  66. Users who are active credit card users
  67. Credit card type
  68. Users who have a debit card
  69. Users who carry a balance on their credit card
  70. Users who listen to the radio
  71. Preference in TV shows
  72. Users who use a mobile device (divided by what brand they use)
  73. Internet connection type
  74. Users who recently acquired a smartphone or tablet
  75. Users who access the Internet through a smartphone or tablet
  76. Users who use coupons
  77. Types of clothing user’s household buys
  78. Time of year user’s household shops most
  79. Users who are “heavy” buyers of beer, wine or spirits
  80. Users who buy groceries (and what kinds)
  81. Users who buy beauty products
  82. Users who buy allergy medications, cough/cold medications, pain relief products, and over-the-counter meds
  83. Users who spend money on household products
  84. Users who spend money on products for kids or pets, and what kinds of pets
  85. Users whose household makes more purchases than is average
  86. Users who tend to shop online (or off)
  87. Types of restaurants user eats at
  88. Kinds of stores user shops at
  89. Users who are “receptive” to offers from companies offering online auto insurance, higher education or mortgages, and prepaid debit cards/satellite TV
  90. Length of time user has lived in house
  91. Users who are likely to move soon
  92. Users who are interested in the Olympics, fall football, cricket or Ramadan
  93. Users who travel frequently, for work or pleasure
  94. Users who commute to work
  95. Types of vacations a user tends to go on
  96. Users who recently returned from a trip
  97. Users who recently used a travel app
  98. Users who participate in a timeshare

There, no need to wonder anymore – Facebook alone knows enough information about its users to make one cringe. Some of that information is ‘scarier’ than other. That’s not the important part yet, though.

In the last five years or so, this exchange of personal data for access to vast networks (with continuous enrichment of those networks with diverse personal data), wasn’t really under the spotlight. As we saw financial technology startups in the lending business gain ground, alternative data became a thing of interest. In the quest to reinvent the way to assess consumer-related risk (as well as extend credit to unscored and questionable), startups were found more imaginative than traditional institutions.

While the wealth of data offered by alternative sources is certainly an increasingly important contributor to one’s personal profile, social media, in particular, can only play a complementary role in shedding a brighter light on the risks involved with extending financing to individuals and businesses. The controversies involved in the use of such data do not yet allow its consideration for a substantial part of one’s credit profile.

Social media will play a complementary role for extension of financing, not a substitutional role for existing models

Back in 2013, Eric Bradlow, now a Faculty Director at the Wharton Customer Analytics Initiative, emphasized social data being most useful when it is applied to people with little or no credit history. “It’s an additional, valuable data source that could be quite predictive of someone’s behavior,” he said. “It’s going to be especially valuable when there is sparse data on an individual.” Looking at new variables is standard practice anyway when building predictive models in credit. “They’re constantly looking for variables that add predictive power to their score,” Bradlow added.

The cases when social media can ‘sharpen the image’ and extend financing opportunities include:

  1. Add a predictive power to the score: Assessment of pointing indicators that can add a predictive power to algorithms;
  2. Patch the lack of history: Holes in the data available to credit scoring where there is little other information about a person (young people who have yet to build up a credit history; international students/employees, immigrants (refugees or other);
  3. Lack or immaturity of assessment systems: Countries where robust credit history-based systems do not exist or are immature.

FICO study found that with the right alternative data, it can accurately score large numbers of previously unscorable credit applicants. In fact, more than 50% of these applicants can be scored.

FinTech examples: How lending & scoring companies approach social media in risk assessment

  1. Lenddo: Lenddo creates credit scores using social media.
  2. Facebook: In 2015, Facebook secured a patent that allows creditors to assess your creditworthiness based on the credit rating of the people in your social network.
  3. Neo: Neo can offer lower-interest rate loans after considering the quality of connections on a LinkedIn profile.
  4. ZestFinance founder (former Google CIO): “People who type only in lowercase or uppercase letters are more likely to be deadbeats (all other things equal).” ZAML considers 70,000 signals and feeds them into 10 separate underwriting models.
  5. Kreditech: Looks at information from social networks, which is voluntarily shared by applicants. What type of friends do you have? Do you live in one place and always party in another? It looks at 8,000 indicators.
  6. Kabbage: Social media data is being used to help approve loans for small businesses.
  7. DemystData: Integrates social along with telecommunications information, and corporate data to create the risk profile associated with an individual or small business.
  8. Happy Mango (credit score based on cash flow management): Allows users to submit testimonials about their character – positive feedback provides 10% of a score, which is on a scale of 0 to 100.
  9. LendUp: Looks at social media activity to ensure that factual data provided on the online application matches what can be inferred from Facebook and Twitter.
  10. TrustingSocial: Credit scoring 2.0. Its engine extracts tens of thousands of data points from social networks (Facebook, Twitter, LinkedIn, Weibo). It tracks hundreds of signals to detect any anomalies in behavior, network and interaction patterns.
  11. China’s credit rating for everything: Traditional, social, and online inputs to the total score. The online score includes interactions with other internet users, ‘reliability’ of information posted or reposted online, and shopping habits.
  12. FriendlyScore: FriendlyScore uses over 820 variables contained within existing online social profiles to generate a holistic view of a person’s creditworthiness.
  13. Credit Sudhaar (credit advisory, Arun Ramamurthy, co-founder): “Alternative sources of data, including social media, are an important part of creditworthiness assessment. It is only through the use of psychometric tests, social media data and other unconventional sources of information can companies and banks identify the intention of a potential borrower.”
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Elena Mesropyan
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Elena Mesropyan

Global Head of Content at Let's Talk Payments
Elena is a research professional with a background in social sciences and extensive experience in consumer behavior studies and marketing analytics. She is passionate about technologies enabling financial inclusion for underprivileged and vulnerable groups of the population around the world.
Elena Mesropyan
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