9. How can the emerging
middle class get credit?
All rights reserved by Ferdi Galeon
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. 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. 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. 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.
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. 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. Big Data: Homophily Works!
Birds of a feather flock together
Customers with a desirable risk profile cluster together
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
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
