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Jeff_Stewart_MIT_2015

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Jeff_Stewart_MIT_2015

  1. 1. How You Can Use Mathematics to Financially Empower the Emerging Market Middle Class Jeff Stewart MIT – Cambridge May 1st 2015
  2. 2. Financial Services Works Great Here
  3. 3. Financial Services Works Great Here Verification Verification Databases Robust Credit Scores Scoring 874
  4. 4. But the Need Is Here….
  5. 5. But the Need Is Here…. And Here….
  6. 6. But the Need Is Here…. And Here…. And Here….
  7. 7. And Here…
  8. 8. Verification No Verification Databases Thin or No Credit Files Scoring 874 ? ?
  9. 9. How can the emerging middle class get credit? All rights reserved by Ferdi Galeon
  10. 10. Large  and  Growing  Addressable  Market 1.8bn MIDDLE CLASS 4.9BN BY 2030 OECD Working Paper 285 – Note: McKinney, estimates $30 Trillion of consumption capacity in Emerging Markets by 2025 LESS THAN 5% HAVE ACCESS TO CREDIT FROM TRADITIONAL FINANCIAL INSTITUTIONS THE EMERGING MARKET MIDDLE CLASS
  11. 11. Deep  Mobile  &  Social  Penetra:on 900Million Source: IDC Worldwide Quarterly Mobile Phone Tracker, Morgan Stanley Research, Annual rate based on 1999-2013 GREATER THAN ARE MOBILE AND SOCIAL 98% SMARTPHONES SHIPPED TO THE EMERGING MARKETS IN 2014 YEAR OVER YEAR GROWTH 32.4% DATA PLAN COST DROPPING 27% ANNUALLY THE EMERGING MARKET MIDDLE CLASS
  12. 12. Finance  will  be  Restructured  by  “SMAC”   Social  –  Mobile  -­‐  Analy;cs  -­‐  Cloud     “Tech companies might do to banks what Uber is doing to taxis” Bank of England Governor, Mark Carney, Davos 2015
  13. 13. Finance  will  be  Restructured  by  “SMAC”   Social  –  Mobile  -­‐  Analy;cs  -­‐  Cloud     1.  Digitalization of Payments 2.  Move to real-time 3.  Proliferation of Choice 4.  Commoditization 5.  Supernationalization
  14. 14.     1st PLATFORM 2Nd PLATFORM 3rd PLATFORM Data is now here! SMAC: The data is in the network Data was here Each new computing cycle typically generates around 10x the installed base of the previous cycle 1,000,000 100,000 10,000 1,000 100 10 1 0 1960 1970 1980 1990 2000 2010 2020 Mainframe 1MM+ Units Minicomputer 10MM+ Units PC 100MM+ Units Desktop Internet 1B+ Units / Users Mobile Internet 10B+ Units? Devices or users in millions; logarithmic scale Devices/Users(MMinLogScale) Source: Morgan Stanley Mobile Internet Report
  15. 15. CREDITWORTHINESS LONELINESS (Boomsma and Others 2005) OBESITY (Christakis and Fowler 2007) SMOKING (Christakis and Fowler 2008) HAPPINESS (British Medical Journal 2008) (Lenddo 2014) YOU SMAC opens up New Behavioral Science
  16. 16. Big Data: Homophily Works! Birds of a feather flock together Customers with a desirable risk profile cluster together
  17. 17. Big Data: NLP Works! Linguistics can be used to manage risk and target desirable customers Words associate closely together, and can be commonly associated with good or bad loans. % Association with GOOD loans % Association with BAD loans
  18. 18. 18 Mission: Economically empower the Emerging Market Middle Class Founded: January 2011 Foundation: Algorithms based on 4 years of online lending Technology: Opt-in Mobile/Social Cloud Based API Web or Standard Developer Kit Services: Social Verification LenddoScore INVESTORS/ADVISORS INCLUDEAWARDS INCLUDE •  Omidyar Network •  Accel Partners •  Blumberg Capital •  Metamorphic Ventures •  iNovia Capital •  Kickstart Lenddo Background
  19. 19. zv   Lenddo Products: Verification & LenddoScore Machine Learning Bad Good Latent Dirichlet Allocation Multiple Cox Regression Arachno Score NLP Markov Network 2.6billiondatarecords 12,000variables 300features 15bestfeatures Verification 874 Scoring
  20. 20. ContentsLenddo is Changing lives I got an InstaLoan! I got a Postpaid Loan! I got a Motorcycle Loan!
  21. 21. Q & A Or ror more information about Lenddo’s product and services, please visit us at Partners.Lenddo.com Or Contact Us: Sales@Lenddo.com

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