GCP Kenya Mobile Finance Deliverable

Kenya Mobile Money
Final Deliverable
Colloquium, April 30th, 2013

Table of Contents
 Executive Summary
 Project Objectives
 Project Objectives, Approach, Deliverables and Timeline
 Phase 1 Output
 Innovation Landscape
 Research Methods
 Pain Points, Issues Analysis and Prioritized Issues for Phase 2
 Phase 2 Output
 Phase 2 Approach
 Solutions Identification and Prioritization
 Phase 3
 Technology Solution A: Alternative risk scoring model based on capture of mobile data
 Technology Solution B: Mobile imaging and biometrics for mobile insurance
 Technology Solution C: Contactless technologies
 Technology Solution D: Agent liquidity management tools
 Technology Solution E: Social Networks as enablers in the mobile money ecosystem
 Recommendations and Next Steps
 Appendices
1

Identify technologies that can be applied to solve issues in Mobile Finance
in Kenya, and increase access to financial services while remaining
profitable for the providers
In scope
 Increase scalability of digital
payment systems
 Enhance financial services to
include savings, credit and
insurance
 Increase participation by players
in the ecosystem, including
merchants, service providers,
enterprises, banks and
regulators
Out of scope
 Addressing Non-Kenyan issues
 Non-technology solutions to
overcome Cultural, Sociological,
Political or Regulatory barriers
 Developing and/or implementing
new technologies to increase
financial access
2
Project Objective

3
We followed a three phased approach involving regular checkpoints with the
client at the end of each phase with detailed deliverables outlined for each
phase
Project Deliverables
• Value Chain Showing All the
Players, their Interactions
• Pain Points Across the Value
Chain
• Underlying Issues
• Prioritization Criteria for
Issues
• All the Issues, Including the
top issues to be considered
for the next Phase
Phase 1
Issues Identification and
Prioritization
Phase 2
Technology Identification
and Prioritization
Phase 3
Business Model
Analysis
~5 weeks ~5 weeks ~5 weeks
First Client Review Second Client Review Colloquium
• Technology Solutions to Solve
the Issues
• List of Vendors Supplying the
Technologies
• Value Proposition Describing
Solution Reach and Value
Created
Narrowing Technology Options
• All the Technology Options
Analyzed, Including the Top
Technology to Consider for the
Next Phase
• Five Business Models Showing
- Solution Benefits
- Implementation Details for Kenya
- Gap Analysis
- Implementation Option(s) for the Client
- Recommended Next Steps
• Executive summary of the project
deliverables including issues, technology
options, frameworks, prioritization
methods, etc. covered from Phase 1 to 3
Beginning April May 1st
Timeline
Deliverables Deliverables Deliverables

In Phase 1, we interviewed more than 15 experts across 12 players in the
mobile money ecosystem in Kenya …
4
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
Integration Products Analytics
Mr.Chidi Okpala
Ms. Catherine Kaunda
Mr. Paul Mugambi
Mr.Gichane Muraguri
Mr.Oscar Ikinu
Mr. Eric Nijigi
Mr. John
Stanley
Ms. Rose Goslinga
Ms. Laura Johnson
Mr. Dylon Higgins
Mr. Ben Lyon
Mr. John Waibochi
Mr. Hthuo
Mr. Sam Agatu
Phase 1: Interviews

… and interviewed 15 experts after the field trip in Kenya
5
Victoria Arch
Vivien Barbier
Joshua
Blumenstock
Karibu Nyagah
Jonathan Hakim
David Ferrand and Ravi Ramrattan
Massimo Young
Jack Kionga
Sammy Kigo
Sam Omukoko
Robert Ochola
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
Prof. Bill Maurer
Prof. Michael Klein
Prof. Colin Meyer
Phase 1: Interviews

In addition, we reviewed secondary research sources that spanned private and
public domains
6
• Book Review
• 2012 – Report (Mobile
phone usage at
Kenyan BOP)
• Mobile World Congress 2013-
Barcelona
• 3 expert interviews
• 02 publications
• Book Review
• The Financial Inclusion Webcast -
19th/20th Feb‟13
• 9 publications
• 10 publications
• 3 publications
• 07 case studies & Africa research reports
• 13 Publications
Phase 1: Secondary Research

7
Customer
Activation
Distribution
Payments Front
End
Payments
Back-End
For Phase 2, we reviewed over 250+ companies and identified 20 that provide
solutions for solving issues identified in Phase 1*
Phase 2: Research
Maana
Mobile
Save to Win by
D2D Fund
CSAs for tobacco
farmers in Malawi
Million-a-month
(MaMa) account
* This list also includes three companies that don’t directly address the issues identified in Phase 1, but
have innovative solutions that we have included in Phase 2

8
In Phase 1 and 2, we conducted prioritization of issues, technology solutions
and identified five technology solutions for business model development in
Phase 3…
Phase 1 Prioritized Issues Phase 2 Solution Options
1.1 Credit: Credit Scoring Models Risk Model Based On Airtime Mobile Data
1.1 Credit: Credit Scoring Models Risk Model Based On Mobile Financial Transactions
1.2 Credit: Recording financial activity Tracking Tool for Informal Financial Transactions
1.5 Credit: Consumer Data Ownership Capture of mobile financial activity
1.5 Credit: Consumer Data Ownership Mobile Accounting Tool Using SMS Data
2.2 Insurance: Efficient Onboarding and Fraud
Reduction
Mobile imaging for ID authentication +
Mobile Imaging for processing insurance documents
Reduction
Fingerprint-based mobile biometrics
3.1 Savings: Lack of Well-Designed Products for BOP Budget management tools
3.1 Savings: Lack of Well-Designed Products for BOP Savings applications for individual needs
3.1 Savings: Lack of Well-Designed Products for BOP Prize or lottery linked savings accounts
3.1 Savings: Lack of Well-Designed Products for BOP Pre-programmed Commitment Savings Accounts
3.1 Savings: Lack of Well-Designed Products for BOP Alternate Savings Products using Mobile Money
5.1 Transaction costs: Lack of interoperability Contactless Technology to address interoperability
8.1 Agent Network: Liquidity Management Location analytics based liquidity management
8.1 Agent Network: Liquidity Management Location Based Crowd-sourcing for liquidity management
Don‟t Relate Phase 1 Issues Self charging cell phones
Don‟t Relate Phase 1 Issues Deployment of light 3G infrastructure
Don‟t Relate Phase 1 Issues Usage of social networks as enablers
Don‟t Relate Phase 1 Issues Cell phone tower signals for rainfall monitoring
Phase 1 and Phase 2 Output
A
B
C
D
E

Alternative risk scoring model based on capture of mobile data: The solution focuses on
generating a reliable credit score for unbanked customers based on their cell phone usage patterns
as well as capturing their informal financial transactions. This will enable the market to design
customized and risk tolerant financial products for BoP consumers.
ID authentication tools to improve customer acquisition: The solution is focused on
using biometric technology and mobile imaging systems for customer activation, KYC, document
authentication and processing of insurance claims. This will enhance the speed/accuracy and
reduce costs associated with providing financial services to BoP consumers.
Contactless technologies: The solution is focused on using RF SIM/NFC technologies to create
a level playing field among competitors in the mobile finance ecosystem in Kenya. This, in turn,
will reduce costs and enhance the quality and variety or products available to BoP consumers.
Agent liquidity management tools: The solution will use location analytics/GIS technology to
smoothen agent liquidity flows and improve the quality of service available to BoP consumers.
… these five solutions include, risk scoring models, ID authentication tools,
contactless technologies, agent liquidity management tools, and social networks
to maximize the benefit to BoP consumers
9
Phase 2: Prioritized Solutions
A
B
C
D
E Social Networks as enablers in the mobile money ecosystem: Social reinforcement, peer
pressure, etc. aspects of social networks can be utilized across the mobile money ecosystem to
create new financial products, manage liquidity problems, encourage savings, prevent fraud, provide
better access to credit, etc.

10
Phase 3 : Approach
Phase 3 Deliverable
Phase 1 Analysis Additional Phase 3 AnalysisPhase 2 Analysis
• Conducted 27 interviews
including on-field interviews
during the trip to Kenya
• We reviewed and analyzed
more than 50 secondary
research papers
• Researched 250+ companies
identified as innovators
across innovation landscape
• Conducted 17 interviews
• Attended Industry
conferences and explored 50
technology innovators
• Continued secondary
research analysis with the
focus on case studies
• Conducted 7 interviews with
the technology innovators to
get additional details for
solutions
In Phase 3, we relied on interviews, secondary research and analysis from
Phase 1 and Phase 2 for developing Phase 3 deliverable

11
Recommendations
Solution Recommendations
Alternative
Credit
Scoring Models
• In the short term, we recommend funding Airtime Scoring Model using a Cignifi type of solution for
quick win by establishing partnership among MNOs, Cignifi, and MFIs
• In the long term, we recommend building up Mixed Scoring Model to expand credit product to BOP
and establishing partnerships/funding startups for achieving the goal
Mobile
Imaging
Technology
• Gates Foundation should engage key players to build the partnerships and financially support
deployment of agents/providers equipped with appropriate handheld devices
Contactless
Technologies
• NFC is not ready for near-term deployment in Kenya due to high infrastructure deployment costs,
but could be a potential future candidate
• The 3rd party enabled model is the best suited for Kenya followed by the collaborative model because
they address the interoperability issue
Agent
Liquidity
Management
Solutions
• We recommend Gates Foundation to take up the creation of a public-use Geographical
Information System (GIS) in Kenya to support initiatives in financial services, healthcare, education,
emergency management, and agriculture
• We do not believe that any investments or funding in liquidity management solutions (outside of the
GIS system proposed above) will be a viable value proposition for the Gates Foundation
Social
Networks
• Engage with companies to explore income generating opportunities offered by the data generated
through BOP social networks
• Do pilot projects with selected players in order to digitize informal financial groups to test social data
capture and data exploitation
A
B
C
D
E
We recommend that Gates Foundation partner with key players and fund pilot
projects related to Credit and Social Networks; Mobile Imaging should be
subsidized, while other solutions are not recommended

Table of Contents
 Phase 1 Output
 Phase 2 Output
 Phase 3
 Appendices
12

Identify technologies that can be applied to solve issues in Mobile Finance
in Kenya, and increase access to financial services while remaining
profitable for the providers
In scope
 Increase scalability of digital
payment systems
 Enhance financial services to
include savings, credit and
insurance
 Increase participation by players
in the ecosystem, including
merchants, service providers,
enterprises, banks and
regulators
Out of scope
 Addressing Non-Kenyan issues
 Non-technology solutions to
overcome Cultural, Sociological,
Political or Regulatory barriers
 Developing and/or implementing
new technologies to increase
financial access
13
Project Objective

14
Identify issues across the
mobile money value chain
and prioritizing the top six
issues
Identify technology
solutions to solve the
selected issues and
prioritize the top five
options
Create high level
business models
for the five options
We followed a three phased approach to address the project objectives
Phase 1
Issues Identification
and Prioritization
Phase 2
and Prioritization
Phase 3
Business Model
Development
Methodology and Approach

15
The approach involved regular checkpoints with the client at the end of each
phase with detailed deliverables outlined for each phase
Project Deliverables and Timeline
Chain
Issues
for the next Phase
Phase 1
Prioritization
Phase 2
and Prioritization
Phase 3
Business Model
Analysis
the Issues
Technologies
Created
Next Phase
- Solution Benefits
- Gap Analysis
Timeline

Table of Contents
 Phase 1 Output
 Phase 2 Output
 Phase 3
 Appendices
16

17
The approach involved regular checkpoints with the client at the end of each
phase with detailed deliverables outlined for each phase
Project Deliverables and Timeline
• Value Chain Showing All
the Players, their
Interactions
• Pain Points Across the
Value Chain
Issues
• All the Issues, Including
the top issues to be
considered for the next
Phase
Phase 1
Prioritization
Phase 2
and Prioritization
Phase 3
Business Model
Analysis
the Issues
Technologies
Created
Next Phase
- Solution Benefits
- Gap Analysis
Timeline

Gates Foundation has defined an Innovation Landscape with seven key
categories of innovation for Financial Services for the Poor…
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
18
• Reaching
the customer
through
agent
networks or
other
channels of
distribution
• Providing
opportunities
for digital
transactions
• Mobile
money
interfaces
used by
customers,
agents and
businesses
for
transactions
• Conversion
of mobile
phone
customers to
mobile money
customers
• Protocols,
systems and
infrastructure
for back-end
processing of
digital
transactions
• Inter -
operability
between
various
telecom
payment
networks and
banks
• Integration of
applications
across digital
transaction
platforms
• Delivery of
financial
products
through
mobile money
platforms
• Delivery of
value added
services
leveraging the
mobile money
platform
• Using data
to design
customized
products for
consumers
and to
improve
existing
services
• Using data
for risk
mitigation and
improving
cost efficiency
Innovation Landscape Description

… with an overarching goal within each category
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
19
Expand
channels for
distribution of
mobile money
and
incentivize
digital
transactions
Payment
front-end
interfaces/
systems
should be
versatile,
secure and
intuitive for
businesses
and
consumers
Communicate
value
proposition ,
establish trust
and develop a
fast and
efficient on-
boarding
system while
addressing
risks
Payment
back-end
systems
should be
robust,
reliable and
cost effective
through
increased
automation
Mobile
transaction
networks
should be
open-loop
systems
Mobile
transaction
platforms
should be
developed for
delivery of
products/
services that
go beyond
payments
(savings,
credit,
insurance
etc.)
Data analytics
should be
harnessed to
improve
breadth,
scope and
cost of
designing and
delivering
financial
services and
products over
the mobile
network
Innovation Landscape Goals
NOTE: More details on individual categories and focus areas within them is described in Appendix C

20
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
Government
(ID check)
Financial Services
Integrators
Financial Services
Innovators
Financial Services Integrators
Mobile money as a service delivery channel
Financial Services Innovators
Purely mobile money based business models
Bridge Builders
Develop applications to facilitate integration
MNOs MNOs
Money Transfer Service Providers
Agents/ Super
Agents
We used the Innovation Landscape to map the players in the Kenyan mobile
money ecosystem
Players in Kenyan Mobile Money Ecosystem
Agents/
Super Agents

We visited 12 players and met more than 15 people while in Kenya to
understand the mobile money eco-system…
21
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
Chidi Okpala
Catherine Kaunda
Paul Mugambi
Gichane Muraguri
Oscar Ikinu
Eric Nijigi
John Stanley
Rose Goslinga
Laura Johnson
Dylon Higgins
Ben Lyon
John Waibochi
Hthuo
Sam Agatu
Interviews

… and interviewed 15 experts since coming back from Kenya
22
Victoria Arch
Vivien Barbier
Joshua
Blumenstock
Karibu Nyagah
Jonathan Hakim
David Ferrand and Ravi Ramrattan
Massimo Young
Jack Kionga
Sammy Kigo
Sam Omukoko
Robert Ochola
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
Prof. Bill Maurer
Prof. Michael Klein
Prof. Colin Meyer
Interviews

In addition, we reviewed secondary research sources that spanned private and
public domains
23
• Book Review
• 2012 – Report (Mobile
phone usage at
Kenyan BOP)
• Mobile World Congress 2013-
Barcelona
• 3 expert interviews
• 02 publications
• Book Review
• The Financial Inclusion Webcast -
19th/20th Feb‟13
• 9 publications
• 10 publications
• 3 publications
• 07 case studies & Africa research reports
• 13 Publications
Secondary Research

1) Credit: adequate credit
based financial products are
not available to under-banked
and un-banked people
2) Insurance: Penetration of
insurance products in Kenya
is very poor - only 6.8% of
Kenyans currently use
insurance products
3) Savings: Despite the up-
take of mobile money in
Kenya, BOP population does
not have saving products
based on the mobile
technology (e.g. medical
savings)
4) Farmers / SMEs: Farmers
are generally price takers with
limited ability to predict or
influence the price (e.g. dairy
farmers in the milk market)
24
9) Mobile
Devices:
Mobile phone
penetration is
still low for
the BOP
We then identified pain points across the innovation landscape, the majority of
which relate to Products and Distribution
6) Transaction
Convenience:
Bank
branches/agents
are less
widespread than
mobile money
agents, hence
limiting
availability of
traditional
banking services
8) Agent
network:
Consumers have
trouble
depositing or
withdrawing
cash because
local agents run
out of either
cash or e-float
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
5) Transaction costs: High
mobile money transaction costs
(could be as high as 30%),
especially on transfer of small
amounts negatively impact BOP
population
7) B2B / C2B transactions: Businesses prefer
cash vs. mobile money but MM can provide
several advantages (e.g. accounting, fraud,
safety, operational ease etc.)
We then identified underlying issues for each of the pain points and mapped them back into the
innovation landscape…
Pain Points
NOTE: Pain Points are not numbered in any particular order

1) Credit : adequate credit based financial products are not available to under-
banked and un-banked people
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
Banks and
other financial
institutions do not
have alternative
and reliable risk
models to
evaluate credit
worthiness of
consumers that
are under-banked
and un-banked
Individual financial history is not
being built-up. Underlying data for
credit consideration not available for
customers with no bank accounts.
Further, because a lot of financial
activity happens informally (via M-
Chamas, SACCOS, MFIs, other
informal institutions), credit and
other transactional history of
individuals involved with these
informal institutions is not recorded
by any credit bureau
Regulations around credit
reporting do not require positive
events e.g. only negative events
(non-payment) are required to be
recorded
No easy way for customers to
consolidate their financial activity
through the mobile finance
integration into one document/file
and present it as a proof of positive
credit history
Available
data is not
shared
among
players -
MNO
consumer
airtime usage
and payment
activities are
not available
to banks or
financial
institutions
25
Main issues relate to lack of inter-MNO data sharing, analytics and underlying product data
1.3 1.2
1.4
1.5
1.1
Pain Points and Issues
NOTE: Issues are not numbered in any particular order

2) Insurance: Penetration of insurance products in Kenya is very poor - only
6.8% of Kenyans currently use insurance products
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
26
Main issues are lack of affordable, need-based products and inefficiencies in distribution channels
Limited
availability and
access of need-
based, affordable
and easy-to
understand micro-
insurance
products to the
BOP
2.1Current
inefficiencies in the
customer
acquisition and
distribution
channels increase
the level of fraud
and also translates
to higher costs,
which ultimately
pose a significant
problem to
adoption of
insurance products
2.2

3) Savings: Despite the up-take of mobile money in Kenya, BOP population does
not have saving products based on the mobile technology (e.g. medical savings)
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
27
People at the
BOP do not have
well-designed
savings
commitment
accounts for
important future
events (e.g.
Savings for
Education,
Medical, Bicycle
etc.) Current
means of savings
are not
adequately
protected/earm-
arked, in that they
could be easily
withdrawn or used
by friends/ family
for other purposes
Banks have
not performed
adequate
market research
and analysis,
and as a result
they do not
have a range of
savings
products that
properly reflect
the peoples
needs
3.23.1
Main issues are inadequate market research by banks leading to poorly designed financial products

4) Farmers / SMEs: Farmers are generally price takers with limited ability to
predict or influence the price (e.g. dairy farmers in the milk market)
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
28
Over 2M
Kenyans are
engaged in the
dairy value
chain. Dairy
farming is
extremely
fragmented
(~80% milk in
Kenya is
produced by
small scale
farmers).
Information
sharing
between local
channels is
currently limited.
Limited number
of co-operatives
exist Collective
selling could
increase selling
power
4.1
Main issues are lack of analytics, data sharing among farmers, and limited collective selling power

5) Transaction costs: High mobile money transaction costs (could be as high as
30%), especially on transfer of small amounts negatively impact BOP population
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
29
Lack of
interoperability
across MNOs
puts banks, and
other mobile
money
platforms at a
non-competitive
position
compared to
MPESA, which
allows MPESA
to continue its
monopoly and
dictate the
prices
Safaricom
(and hence
MPESA) is the
dominant player
and thus has
the largest
agent network.
Further, agents
are not widely
shared across
other providers,
limiting
competition to
MPESA. The
networks
effects increase
MPESA's
monopoly and
allow it to
dictate the
prices
5.2 5.1
Main issues relate to limited interoperability, network and data sharing among MNOs and banks

Different
sets of
regulations
apply to banks
compared to
MNOs with
regards to
mobile money
transfers. This
limits the banks
ability to
provide large
agent networks
and compete
with the
widespread
mobile money
agent network
provided by
MNOs
6) Transaction Convenience: Bank branches/agents are less widespread than
mobile money agents, hence limiting availability of traditional banking services
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
30
6.1
Main issue is different set of regulations for banks compared to MNOs

7) B2B / C2B transactions: Businesses prefer cash vs. mobile money but MM can
provide several advantages (e.g. accounting, fraud, safety, operational ease etc.)
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
31
7.1 Poor
integration of
MPESA with
business IT
platforms or
traditional
banking services
as businesses
using Pay Bill or
Bulk Payment
services lack the
skills and access
to (APIs). Thus,
MPESA
transactions
must be entered
manually,
introducing
delays, errors
and risk of fraud.
This should be a
fully automatic
process
Main issue is poor IT integration between businesses and mobile money services

8) Agent network: Consumers have trouble depositing or withdrawing cash
because local agents run out of either cash or e-float
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
32
Agent
liquidity
problem:
Inadequate
liquidity
management
often leads to
shortage of
cash or e-float,
which limits
service to the
client and
inconveniences
local agents
8.1
Main issue is due to lack of analytics to improve agent liquidity management

9) Mobile Devices: Mobile phone penetration is still low for the BOP
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
33
BOP can‟t
afford to buy
the phone (of
those Kenyans
living on less
than $2.5 US/
day, 60.5%
owned a mobile
phone. (RIA -
Research ICT
Africa, 2012)
Electricity
access is still
limited (by RIA
report of 2012:
among BOP
who did not
have mobile
phones 44.9%
said that there
is no electricity
at home to
charge the
mobile phone)
9.1 9.2
Main issues arise from direct cost of mobile phone or high cost of electricity to charge a phone

We synthesized the pain points and the issues to identify priority issues that
make the most impact using a prioritization framework
34
Secondary Priority
Do Not Focus Secondary
Low High
High
Low
Pain Points and Issues Prioritization Framework
Pain Point Impact
Criteria:
• Relevance and
Severity
• Scale
Issue Specificity
Criteria:
• Issue well-defined and specific
to allow solvability
Prioritization Framework

35
Low High
High
Low
Pain Points and Issues Prioritization Framework*
1.3 1.11.21.5
3.1
5.1
3.2
1.4
2.1
4.1
5.2
6.1
7.1
8.1
9.19.2
Credit: Credit scoring models
Banks and other financial institutions do not have alternative and
reliable risk models to evaluate credit worthiness of consumers
that are under-banked and un-banked
Credit: Recording financial activity
Individual financial history is not being built-up. Underlying data for
credit consideration not available for customers with no bank
accounts. Further, because a lot of financial activity happens
informally (via Chamas, SACCOS, MFIs, other informal
institutions), credit and other transactional history of individuals
involved with these informal institutions is not recorded by any
credit bureau
Credit: Consumer data ownership
No easy way for customers to consolidate their financial activity
through the mobile finance integration into one document/file and
present it as a proof of positive credit history
Insurance: Efficient onboarding and fraud reduction
Current inefficiencies in the customer acquisition and distribution
channels increase the level of fraud and also translates to higher
costs, which ultimately pose a significant problem to adoption of
insurance products
Savings: Well-designed products
People at the BOP do not have well-designed savings
commitment accounts for important future events. E.g. Savings for
Education, Medical, Bicycle etc. Current means of savings are not
adequately protected/earmarked, in that they could be easily
withdrawn or used by friends/family for other purposes
Transaction costs: Lack of inter-operability
Lack of inter-operability across MNOs puts banks, and other
mobile money platforms at a non-competitive position compared
to MPESA. This lack of interoperability allows MPESA to continue
its monopoly and dictate the prices
Agent network: Liquidity Management
Agent liquidity problem: Inadequate liquidity management often
leads to shortage of cash or e-float, which limits service to the
client and inconveniences local agents
1.1
2.2
1.2
1.5
3.1
5.1
Indicates No Clear Technology Solution and
dropped from further consideration
Issues Prioritized for the next phase
Label:
Issues related to Pain Points around Credit, Insurance, Savings, Liquidity
Management and Transaction Costs bubbled to the top**
PainPointImpact
Issue Specificity
Prioritized Pain Points and Issues
2.2
* Please see Appendix for score assigned to each pain point and issue
** 8.1 is secondary issue, but is included here for analysis in Phase 2. We will narrow down 1.1. 1.2 and 1.5 to 1-2 technology in Phase 2 so that
we have 5-6 issues to focus in Phase 2
8.1

Geo-mapping, Biometrics, Data-mining, Thin-film SIM, Social networks,
Gamification, Behavioral Economics, and Cloud-based services can be applied
to solve the issues identified
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
36
1.11.2
• Thin-film SIM.
“Open loop”
architecture.
Ex: Watchdata
solution -
‟SIMpartner‟,
Tangaza Pesa,
Paga (Nigeria),
Taisys‟
mPayment.
Credit: Credit
scoring
models
Credit: Recording
financial activity
• Biometrics id
verification and
data mining. Ex:
Virtualcity.
• Data mining
and models
based on
mobile usage
and other
behavioral
data. Ex:
Cignifi,
Entrepreneurial
Finance Lab
(EFL), Caytree
partners
• Interfaces for digitizing
informal groups. Ex:
mobile social
networks.
• Custom interfaces and
cloud based services.
Ex: Zebumob (Kenya)
• Geo-spatial mapping,
gamification,
behavioral economics
and data analytics Ex:
G-Life Microfinance
Limited (Ghana);
Gamification
(AppLab)
2.2 Insurance:
Efficient
onboarding
and fraud
reduction
Transaction
costs: Lack of
inter-
operability
5.1
1.5
3.1
Credit: Consumer
data ownership
Savings: Well-
designed
products
Technology Solutions
• Geo-location
analysis, robust
weather data
sources and
weather
modeling and
simulation. Ex:
satellite data.
Technology Solutions Preview
8.1 Agent Network:
Liquidity
Management
• Location
analysis, agent
mapping,
statistical
modeling.

Table of Contents
 Phase 1 Output
 Phase 2 Output
 Phase 3
 Appendices
37

38
We will present five business models based on technology solutions identified
in Phase 2 and will create executive summary of the deliverables
Revised Phase 3 Deliverables
• Value Chain Showing All
the Players, their
Interactions
• Pain Points Across the
Value Chain
Issues
• All the Issues, Including
the top issues to be
considered for the next
Phase
Phase 1
Prioritization
Phase 2
and Prioritization
Phase 3
Business Model
Analysis
the Issues
Technologies
Created
Next Phase
- Solution Benefits
- Gap Analysis
• Executive summary of the project deliverables
including issues, technology options,
frameworks, prioritization methods, etc.
covered from Phase 1 to 3
Timeline

We relied on secondary research and interviews to help identify and analyze
new technologies relating to Mobile Finance in Kenya
Phase 2 Approach
Secondary
Research
Interviews
Industry
Conferences
Issue Bound Solutions Open Ended Solutions
Researched solutions to solve issues
identified in Phase 1
Identified solutions by researching
companies provided by Gates Foundation
and those that propped up during interviews
and conference visits
• Explored 50 companies from solving issued identified during Phase 1 as well as innovative
solutions in the space
• Identified one solution to solve Phase 1 issue and two additional innovative solutions as part of
Phase 2*
Based on Phase 1 leads, secondary research and conference participation we:
• Contacted 30 companies
• Conducted 17 interviews in order to analyze current and potential technology solutions and
their feasibility in Kenya
• Cases analysis in Africa, China, South
Asia, Latin America
• Additional sources: Reports, Articles,
Blogs, News, Books
• Secondary research on leads
developed during the project
• Researched more than 250 companies
identified as innovators across innovation
landscape*
* Refer to Appendix A for insight developed on these companies. A number of these companies such as Jumio, Mitek systems, ABBYY,
Zebumob, Yodlee, etc. will be researched in detail in Phase 3 for developing business model 39

40
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
Out of the 250+ companies analyzed, we have identified 20 that provide
solutions relevant to issues identified in Phase 1*
Relevant Companies
Maana
Mobile
Save to Win by
D2D Fund
CSAs for tobacco
farmers in Malawi
Million-a-month
(MaMa) account
* This list also includes three companies that don’t directly address the issues identified in Phase 1, but
have innovative solutions that we have included in Phase 2

41
For each solution identified, we captured solution information and made
assessment for subsequent prioritization on benefit, feasibility and confidence
Solution # Name and Numbering of the
Solution
Solution
Description
Describe the solution
• as proposed by a vendor
• implemented in another country
or
• proposed by us
Underlying
Technologies
What underlying
data/technologies
• make up the solution
• are needed for the solution to
work
Examples Examples of implementation
• by the vendor
• or in another country/vertical
Assessment
Benefit of the
Solution
How much does the solution solve the
issue? Does the solution have other “side”
benefits?
Implementation
Feasibility
Do we believe, at a high level that this
technology solution can be implemented
in Kenya based on number of players who
need to participate in the solution,
regulatory environment, or other such
factors
Level of
Confidence in
the Solution
Is the underlying technology mature? Has
this solution be proven in other countries,
sectors etc. A less mature technology or
solution isn’t necessarily bad, as
investment can be to mitigate this
concern
MediumHigh Low
Solution Information Capture and Assessment

42
Solution # 1 Risk Model Based On Airtime Mobile Data
Solution
Description
Creation of behavioral credit risk scoring model
based on airtime activity
• Credit score based on consumer lifestyle and
behavior
• Provides immediate insight into a current or
prospective customer's probability of default,
even for customers with no traditional credit
history
Underlying
Technologies
Data capture from the Mobile transfer protocol
using:
• Airtime Data analytics
• Voice/ SMS Data analytics
• Behavioral Data analytics
• Mathematical Scoring Algorithm
• Can be integrated directly into existing decision
systems through API for online applications or
bulk data extracts for pre-screen and
customer monitoring
• Leverages near-ubiquity of mobile phones in
emerging markets
Examples Cignifi, behavior-based consumer data and
analytics company located in Cambridge, MA has
done a pilot in Brazil in 2011 with Oi Telecom
Alternative credit scoring model based on airtime activity can provide BOP
access to new financial products
Credit: Credit Scoring Models1.1
Assessment
Benefit of the
Solution
• Fast access to credit products for BOP who do
not have traditional financial backgrounds or
access to services
• No additional cost to the consumer, except for
what they already have – a phone (USSD,
feature or smart phone)
• Lower customer acquisition cost for banks,
insurance MNO‟s, MFIs, etc .
• Better information on customers allowing for
customized and tailored products with less
chance of default and higher chance of success
Implementation
Feasibility
• Third party application on the user's phone or
though MNOs sharing data
• The provider of the score can be either third
party authorized company or one of existing
credit bureau
• Could partner up first with Airtel and Orange to
get data before approaching Safaricom
• Data analysis is limited because it is not
capturing other mobile usages like remittances,
payments, transfers, etc
• Risks of airtime behavior manipulation
Level of
Confidence in the
Solution
• Very new, untested in Africa
• New Kenya's law, all SIM cards must be
registered under on ID, so all records will
match one person even if different SIMS or
phones are used
• All airtime data will be possible to capture on
the one ID, thus decreasing potential
manipulation
Medium
Medium
High

43
Solution # 2 Mobile Finance Data to Built a Credit Score
Solution
Description
Creation of real-time credit score using mobile
finance transaction
• Credit score based on top up, utilities bill and
saving transaction.
• Provides immediate insight into customer's
probability of default, even for customers with no
traditional credit history
Underlying
Technologies
Data capture from the Mobile transfer protocol
using SMS based money transaction
Examples Telepin is developing credit scoring algorithm that
will be natively implemented in its money transfer
platform and that will allow real time credit scoring
Real time credit scoring based on mobile finance transactions analysis can
provide BOP access to credit products
Credit: Credit Scoring Models1.1
Assessment
Benefit of the
Solution
• Allow fast large scale credit scoring for every
customer of the MNO using this money transfer
platform
• Allow fast response for credit demand
Implementation
Feasibility
• Require the MNO to change its mobile money
platform by a brand-new one
• Do not need any action by the customer
• Because real time scoring, borrower could wait
to have the perfect score to ask for loan.
Level of
Confidence in the
Solution
• Solution under development and not tested so
far
• Information about the company is limited
Low
Low
Medium

44
Solution # 3 Tracking Tool for Informal Financial Transactions
Solution
Description
A mobile app to track informal financial activity
among unbanked consumers (lenders and
borrowers)
• This mobile app product will enable management
and recordation of IOUs
• Mobile app will enable tracking of transactional
history of individuals involved with informal lending
institutions.(ROSCAs, SACCOs, moneylenders)
• Individual credit histories can be established using
temporal data once app finds traction among users
as a transaction tracking tool
Underlying
Technologies
• Lightweight mobile app solution that works on basic
phones. The applications platform is hosted on the
cloud
• Regular reminders, notice of availability of funds,
payments receipts can be managed via SMS and
through the app portal
Examples • Maana Mobile is free and secure mobile phone app
that lets consumers borrow money as well as
lenders keep track of what is owed to them
• Developed by a team in Boston, the app is currently
being tested in South Africa
• Borrowers and receivers get automatic reminders
when loans become due
• Borrower and lender records get updated
automatically when payments are made
• Cloud based app enables contacts and records to
be retrieved even if phone is lost or stolen
Tracking informal peer-to peer transactions can help capture credit
worthiness of unbanked consumers
Credit: Recording financial activity1.2
Assessment
Benefit of the
Solution
• Easy to use mobile app can enable easy tracking
of funds and prevent losses through system
leakage or manual errors
• Credit score based on individual repayment
behavior will result in increased discipline in loan
repayments
• Risk profile of unbanked consumers can be
assessed using payments history information;
formal financial institutions can use information to
extend credit to credit worthy consumers and weed
out risky customers
Implementation
Feasibility
• Success of the product greatly depends on
established network effect among borrowers and
lenders. A reliable credit score can be extracted
only if a majority of an individual‟s informal
transactions occur through the same mobile app.
• Product is currently in preliminary implementation
phase in South Africa and feasibility is yet to be
proven
Level of
Confidence in
the Solution
• Mobile app is currently being pilot-tested in the
field and results are unknown
• Basic technology may be easily implementable
through a mobile app solution; unclear whether
reliable credit scoring would be possible since
credit scoring model has not been developed yet
High
Maana
Mobile
Low
Low

45
Solution # 4 Capture Of Mobile Financial Activity
Solution
Description
Technology based on the mobile application, that
captures all SMS mobile platform‟s transactions.
Currently works on M-Pesa:
• All transactions are automatically saved in the
app and website
• Categorize transactions
• Create statements, detailed reports
The information collected on the server/cloud can
be utilized to build credit score models or analyze
consumer behavior for customized services
Underlying
Technologies
Technology based on:
• SMS automated analysis
• IP based communication
• Data storage
• Data encryption
Technology works for Android OS, but there is
opportunity to expand it to feature phones and
simple mobiles.
Examples Zebumob, company situated in Kenya.
Data capturing of mobile financial transactions can enable BOP establish formal
financial history
Credit: Consumer Data Ownership1.5
Assessment
Benefit of the
Solution
• Access to M-Pesa transactions, that is currently
very limited
• Availability of information that can be used as a
financial statements for unbanked
• Financial planning tool for consumers
• Data collection that can be used by financial
institutions for credit scoring and other
consumer behavior analysis
Implementation
Feasibility
• Build up partnership with credit bureau or other
firm that will build the credit scoring based on
data capturing and share it with the MFIs
• Can become official financial data provider, but
there is a need of special CBK approval
• Currently they don‟t have enough of
investments/partners to expand their business
and become a data provider
Level of
Confidence in the
Solution
• Technology successfully working for Android
OS, but there are some constraints to expand it
to more simple phones (download the apps to
the simple or feature phone)
• MNO can change the protocol of SMS that will
require change of the algorithm
• Currently working on M-Pesa transactions,
additional development required to expand to
other platforms
Medium
High
High

46
Solution # 5 Mobile Tracking Tool Using SMS Data
Solution
Description
• Simple, real-time accounting tool that works
through SMS to help BOP and business owners
do daily accounting and financial tracking so that
they can better manage their money
• Individuals can track daily revenue and
expenses to help them manage their money.
On-demand reports and analysis are also
provided via SMS
• Accurate risk analysis allows quality individuals
to access to the capital they need (home loans,
insurance, personal loans, etc.) for the first time
• Training & Field Support is part of the product
that helps teach basic accounting literacy to the
people
Underlying
Technologies
• A simple mobile accounting tool that works on
any mobile phone. No internet or download
required
• Credit assessment of potential borrowers based
on the unconventional model of a 26-variable
algorithm
• Customized metrics, customized reports and
data analytics
Examples InVenture – currently in India. However, it is already
in Kenya on a small scale by partnering up with
Musoni (MFI)
Mobile technology / accounting tool that provides credit scoring and data
tracking through a 2-way SMS communication
Credit: Consumer Data Ownership1.5
Assessment
Benefit of the
Solution
• Efficient tool for SMEs to capture their financial
activity
• Turning informal tracking to formal
• Improving financial literacy through trainings
• Data collection allows access to the credit
products and opportunity to create customized
products
Implementation
Feasibility
• Efficient and easy tool for SME and
entrepreneurs
• For BOP implementation depends on agent
network. Additional surveys satisfy KYC
requirements
• The score is shared within the MFIs that can
provide the loan; more extended partnership to
be developed
• Constraint: verifying the information is costly and
time-consuming; 5% of people are audited
manually by door-to-door visits
• Currently there is no automated MNO‟s data
capturing; opportunity to apply Zebumob
technology
Level of Confidence
in the Solution
• The technology implemented in India, but in
Kenya the company starts to work from January
2013 and no statistics yet to accumulate
• Potential for this technology would increase with
partnership from Zebumob
Medium
Medium
Medium

47
Solution # 6 Mobile Imaging : ID Authentication & Documents
Scanning
Solution
Description
The insurance agent‟s camera-equipped mobile
device is used as :
• An ID scanning terminal to meet KYC
requirements and reduce fraud
• A sophisticated document scanner integrated to
the insurance mobile app. Key processes are
automated by taking photos of documents :
getting a quote, filing a claim and sending
insurance-related documents electronically as a
high quality PDF
Underlying
Technologies
• Inte-grated within a mobile app, the technology
enables to authenticate customers‟ identities via
scan of an ID document to com-plete a
reg-is-tra-tion process in seconds
• The extractive imaging technology pulls the
relevant data from a scanned document and puts
it into the appropriate fields in the mobile form.
No data entry is required by the user
Examples
• JUMIO, Headquartered in Palo Alto, California.
Mobile imaging and ID verification technology
• Mitek Systems, Headquartered in San Diego,
California. Advanced Mobile Imaging & Dynamic
Data Capture solutions
• ABBYY is headquartered in Moscow, Russia.
Mobile Data Capture Solutions
Insurance: Efficient Onboarding and Fraud Reduction2.2
Assessment
Benefit of the
Solution
• Reduces fraud potential due to validating low
quality copies of documents
• Saves time and costs associated with KYC
requirements
• Improves operational efficiency of sales,
distribution and claims processes
• Lowers the cost of service
• Convenient and easy to use as it can be done from
anywhere at anytime
Implementation
Feasibility
• The technology requires the use of smart phones
or tablets at the level of the insurance agent and a
mobile app for the insurance product
• No barriers regarding the implementation of this
technology in Kenya are identified.
Level of
Confidence in
the Solution
• Several startups and platforms in the US and
Europe and well-established businesses such as
Travelocity and Western Union are using JUMIO‟s
mobile vision technology solution
• Mitek solutions are used by several mobile banking
and insurers applications in the US
• ABBYY has several clients in the banking and
insurance industry in Europe, Asia and the US
High
High
Mobile imaging is an innovative approach to ID authentication and documents
processing that reduces fraud and improves operational efficiency
High

48
Solution # 7 Fingerprint-based Mobile Biometrics
Solution
Description
At the level of the agent:
• Use of a biometric fingerprint reading device for
customer registration
• Use of fingerprint as identification for payment of
premiums
Underlying
Technologies
• Customer identity verification is enabled through
connection to the government database in real time
• Identities verification against previously captured
biometric data happens before financial
transactions
Examples • Tangaza Pesa, Kenya. Mobile money transfer,
backed by Mobile Pay Ltd
Mobile biometrics use in insurance improves security by addressing identity
fraud issues and increases product scalability
Insurance: Efficient Onboarding and Fraud Reduction2.2
Assessment
Benefit of the
Solution
• Ability to target a larger BOP segment through
registration of people without an identity card :
fingerprint as the identity needed
• Security against identity fraud
Implementation
Feasibility
• The technology requires the use of tablets at the
agent level
• Regulatory concerns regarding insurance
registration without an ID
• This technology has been implemented in Kenya
by TangazaPesa for registration and transactions
performance for their money transfer service
Level of
Confidence in
the Solution
• TangazaPesa transfer system based on fingerprint
registration and money deposit and withdrawal is a
successful business model in Kenya
• TangazaPesa is working on an insurance product
"Mwananchi Afya" underwritten by Britam
High
Medium
High

49
Budget tracking and expense monitoring tools will aid better financial
management among BOP consumers
Savings: Lack of well-designed products for BOP3.1
Solution # 8 Budget Management Tools
Solution
Description
Design mobile applications that :
• Acts as a mobile wallet app for under banked
customers
• Is free from any architectural dependencies on
carrier infrastructure
• Allows users to record and categorize all mobile
phone transactions
Underlying
Technologies
• The solution is based on mobile phone
application architectures that enables easy
download
• It requires linkage between the application
provider and the MNO
• The technology solutions are based on
integrated bill payments, invoicing and cash flow
management via mobile phone messages
related to transactions
Examples • Zebumob, launched in Kenya in 2012, is an
android application that records & categorizes
all M-Pesa transactions
• PreCash HQ in Texas, launched FlipMoney in
2012. Flip allows people without bank account to
instant remote cheque deposits and perform bill
payments via their smart phones
Assessment
Benefit of the
Solution
• The technology could benefit approximately 18mill
low income people. (72% of Kenyan population still
falls in low income bracket. Of this 60% own
mobile phones.- RIA 2012)
• Speeds up the check deposit process which
currently takes 3-6 days
• Enables people without a bank account to perform
banking transactions like cheque deposits and
stores money in a mobile wallet
Implementation
Feasibility
• Both Zebumob and Flip Cash are smartphone
products. There has been no clear indication of
developing a similar product for basic and feature
phones
Level of
Confidence in
the Solution
• Both companies have only launched their products
6 months back
• While Zebumob indicates 9600 downloads within 4
weeks. Adoption rate is still needs to be analyzed
Low
Medium
High

50
Creating a tool that gives people the ability to save towards committed goals
will enhance savings behavior among BOP
Solution # 9 Saving Applications for Individual Needs
Solution
Description
• Create a mobile application that is linked with
existing bank accounts of consumers
• The mobile phone user sets some financial
goals via the mobile app
• Using the phone the consumer can then start
moving small amounts of money into an interest
bearing bank account
Underlying
Technologies
• Mobiile application architectures that are
designed focusing on mobile based on usage
behavior of BoP consumers
• Use geospatial visualization to capture both
qualitative and quantitative data sets for further
behavioral analytics
• The solution uses mash up tools to track trends
and data geo-spatial data for analysis
Examples • ImpulseSave, a SanFrancisco based company
has launched a technology platform that is
linked to a real savings account. It allows users
to squirrel money for their goals and move it into
the account with a text message
Assessment
Benefit of the
Solution
• The technology could benefit a number of people,
users have to be well versed with mobile phone
usage and banking processes
• Allows people to save towards specific financial
goals, like savings towards education, etc.
• The data captured can be used to study the
changing behaviors and saving patterns of users
Implementation
Feasibility
• ImpulseSave has been rolled out in the US in
September 2012. The technology platform is
heavily dependant on advanced technology
platforms for smartphone users
• Requires 3rd party partnership with MNOs and
banks. This multi level partnerships may involve
regulatory concerns
Level of
Confidence in
the Solution
• The company has launched its products 6 months
back and user numbers have not been released in
the market due to confidentiality clause
Low
High
Low

51
Solution # 10 Prize or Lottery Linked Savings Accounts (PLS)
Solution
Description
“Prize or lottery linked savings accounts“ use
behavioral economics (thrill of winning) to design a
savings product for consumers who value the
guarantee of no principal loss and a large, but low
probability gain
• Low-income consumers with uncertain income
streams are incentivized to save
• Lottery scratch cards of varying denominations
(equivalent to amount of savings) are purchased
and saved on a cloud based mobile app
• Lottery prizes are given out every quarter as
returns. Consumers forfeit or accept reduced
compound interest on savings
Underlying
Technologies
• Lottery scratch cards could be purchased through
agents or directly via cloud based apps
• Mobile app solution to manage accounts and track
purchased cards; USSD app could be designed for
basic feature phones
• Lottery rewards can be announced via SMS
Examples • Designed by Doorway to Dreams (D2D) Fund,
Save to Win is available to members of
participating credit unions in Michigan, Nebraska, in
the US
• Consumers buy CD‟s worth $25 each and gain
entry into „Save to Win‟
• STW has grown to 58 credit unions, with over
25,000 unique accounts saving more than $40
million from 2009-2011
• MaMa accounts launched by First National Bank n
South Africa is a no-fee savings account that
rewards savers with monthly prizes
Prize linked savings (PLS) accounts use behavioral economics principles to
incentivize savings behavior by making the act of saving fun and rewarding
Assessment
Benefit of the
Solution
• Will force savings behavior among unbanked
Kenyans by leveraging consumer interest and
excitement around lotteries
• Statistical research on low income consumers in
the US indicates that interest in PLS accounts in
greatest among non-savers, those who have
volatile income streams and who play lotteries
extensively
Implementation
Feasibility
• A mobile based application will be required for
implementation in the Kenyan context.
• Consumers can manage their lottery CDs either
manually through bank agents, through internet
banking or through a mobile phone app.
• PLS accounts have found great success with
unbanked consumers in Mexico, South Africa,
Japan, Venezuela, Columbia
• Some jurisdictions have regulatory barriers around
gambling that could be an impediment to execution
• The economics of the program and scalability
options need to be studied closely
Level of
Confidence in
the Solution
• Several behavioral economists (Peter Tufano,
Daniel Schneider) and financial services
providers(FNB, D2D Fund) are experimenting with
PLS product design
• Mobile app development challenges such as,
MyMobileAppUpChallenge and the
FinCapDevChallenge are innovating next-
generation mobile tools using these ideas
• Save to Win has been successful in the US
High
Save to
Win by
D2D
Fund
Million-a-
month
(MaMa)
account
Medium
Medium

52
Solution # 11 Pre-programmed Commitment Savings Accounts
(Lock Box/Piggy Bank)
Solution
Description
“Pre-programmed commitment accounts“ (CSAs)
force consumers to put aside a pre-determined amount
at periodic intervals(monthly or daily) with penalties for
missing targets
• This mobile app product relies on the concept of
forced, regular savings with accumulated savings
returned to the consumer at the end of the
commitment period or after reaching a savings
target
• Restricted access to funds until a future date limits
use of funds for other purposes
• Penalties for missing targets keeps consumers alert
• Commitment accounts for specific purposes such
as maternity, education, small business purchases
can be designed
Underlying
Technologies
• Mobile app solution to set up commitment accounts,
make regular deposits and check balances; USSD
app could be designed for basic feature phones
• Daily savings reminders , notice of availability of
funds for withdrawal could be done via SMS
Examples • Opportunity International Bank Malawi, University of
Michigan and the World Bank developed a CSA
product for rural Malawi‟s farmers in 2009
• Farmers commit to save post future harvest
season through automatic deposit and set their own
withdrawal date
• Statistical research on Malawi farmers indicates
increase in overall savings by empowering the
consumer to set their own targets and increase in
future profits
Commitment savings accounts (lock box concept) force consumers to save a
minimum amount periodically
Assessment
Benefit of the
Solution
• Easy mobile app solution will force savings
behavior through direct deposit of funds as a
default setting, capitalizing on people‟s willingness
to forego future income and to maintain the status
quo
• Consumers can easily track their accounts through
mobile device for increased comfort with product
but cannot access funds
• Malawi experiment showed increase in farmers
profits from having commitment accounts
Implementation
Feasibility
• Commitment savings accounts are in the initial
stages of development
• A mobile based application will be required for
implementation in the Kenyan context. Although
CSA products are currently available, a mobile app
for commitment accounts is yet to be developed
• Profitability of commitment accounts is yet to be
seen (long term goal) and will depend on how
much more people save with commitment and how
long the deposits remain in the bank
• Requires some level of predictability in income
streams (either seasonal or regular) which will limit
adoption by consumer s who have more volatile
income patterns
Level of
Confidence in
the Solution
• Commitment product has been pilot-tested in the
field and more iterations are forthcoming
• Technology easily implementable through a mobile
app solution; however, it is unclear how periodic
savings discipline can be enforced among
unbanked consumers, given their volatile income
streams
CSA’s for
tobacco
farmers in
Malawi
Low
High
Low

53
Solution #12 Alternate Savings Products using Mobile Money
Solution
Description
• Livestock is a form of currency for BOP and can be
used for livestock savings bank (e.g. Goat Bank in
Bangladesh and Mindapore)
• Grain Bank, Non-Timber Forest Products as
Savings, Trees as Savings, etc. are more examples
of alternate savings products
Underlying
Technologies
• Mobile Money to organize the saving products
Examples • PROSHIKA, an NGO in Bangladesh has
encouraged long-term savings through trees in
innovative ways such as planting and maintaining
trees on the roadside by poor, agro-forestry by poor
on patches of land, short term savings in growing
vegetables on the roadside by poor and others
• Grameen Bank has been testing a number of such
products in Bangladesh
• Sal Piyali unnayan dal (SHG) in Midnapore has a
group of 15 participants from poor groups start a
goat bank in the year 2007, spread in at least 5
locations in 5 villages. It is a Goat Savings Bank
owned by the SHG and only 4 participants have
undertaken responsibility of keeping and
maintaining the goats since they have homestead
and better facilities to look after. All goats are group
property, which individuals look after. The off
springs of the goat are either retained or sold and
that constitutes the return on the goat capital, where
the proportionate share of the group members is 75
per cent and that of the participant who has
maintained the offspring is 25 per cent plus her/his
own share as a group member
Alternate savings products such as livestocks savings can be widely made
available using mobile money platform
Assessment
Benefit of the
Solution
• Though we were able to find examples of "Goat
Bank" or "Grain Bank" in Bangladesh, we couldn't
find similar examples in Kenya even though our
interview with Equity Bank suggest that BOP
customers do think of savings in terms of livestock.
If true, the solution would benefit BOP customers.
We recommend deeper market study to confirm
the BOP savings behavior before designing the
product
Implementation
Feasibility
• Mobile money platform can be used to digitize the
underlying products and save for or in the
underlying products using mobile money
• In the SHG example provided, people from
different locations can save towards buying and
the goat bank using the mobile money platform.
Subsequently, the return on the capital will also be
distributed using mobile money
• Implementation feasibility can change if we find
that Kenyan BOP consumers are attracted towards
such a solution
Level of
Confidence in
the Solution
• Various organizations such as Proshika, Grameen
Bank are still testing such solutions and we haven‟t
found scalable solution that will provide us
confidence
Low
Sal Piyali
unnayan
dal (SHG)
Medium
Low

54
Solution #13 Contactless Technologies to Address
Interoperability
Solution
Description
• Proximity technologies, such as, RF SIM and NFC
can be adopted for mobile payments and
transactions, that would allow decoupling of the
financial services providers on their reliance on
MNOs
• Successfully implementations: SK telecom (Korea),
Visa (Malaysia), Octopus (Hong Kong), NTT
DoCoMo (Japan)
Underlying
Technologies
• Currently, RF SIM and NFC are the two primary
proximity technologies that can be used to address
this issue
Examples
• Taisys‟ mPayment is a solution that is operator
independent and can be used by account holders
regardless of their mobile service provider
• Watchdata managed to provide a Stick-on SIM
card, called ""SIMpartner"" which is completely
independent from telecom operators and it is a
universal solution, which suits almost all mobile
handsets and SIM cards. It enables users to have
access to mobile banking functions such as fund
transfer, bill payment and balance inquiry
RF SIM and NFC technologies can be used to solve the interoperability issue
by providing banks with alternative channels that do not rely on MNOs
Assessment
Benefit of the
Solution
• Mobile devices can support NFC and RF SIM
• Proximity technologies can expand the range of
mobile services and create new revenue streams
• NFC can employ infrastructure deployed for other
contactless services
• Subscribers to „mobile wallet‟ applications may be
less likely to switch service provider
Implementation
Feasibility
• The advantage of RF SIM is that it is relatively
cheaper to implement from the end-user‟s
perspective. The disadvantage is that most existing
payment infrastructure (e.g. POS) is incompatible
with RF SIM, implying high capital costs
• NFC would require users to have an enabled
handset or an interim solution, which is more
expensive or complicated to fit to their existing
handsets
• The chip price is prohibitively expensive
• The business case for MNOs is unclear and
further, business models that benefit all
stakeholders have yet to be established
Level of
Confidence in
the Solution
• Typically found to be successful in countries where
the nationwide roll-out of infrastructure is
financially viable, the number of stakeholders is
low, the level of disposable income is high, and the
proportion of the population that has bank accounts
is high
• While the technology itself is quite mature, many of
these constraints don‟t directly apply to Kenya
Low
Low
Transaction costs: Lack of interoperability5.1
High

55
Solution # 14 Location Analytics Based Liquidity Management
Solution
Description
• A geographical analysis tool that measures and
optimizes agent network configurations for optimal
liquidity management
• Liquidity management through real time tracking of
liquidity and mobile money activities at agent
locations
Underlying
Technologies
• Population demographics and geographical
information is collected from public sources and
government databases
• Financial activity information will be provided by the
MNOs, banks, etc. to optimize their networks
• The data mining and linear optimization models
combine geographical data with demograhic
information and provides an optimal solution based
on a given set of parameters and objectives
Examples • The following companies employ geographical
analysis in their offerings, but their solutions do not
directly address the agent liquidity management
issue: Telfonica Dynamic Insights, 4Info, awhere
Location analytics and data mining solution to address agent network and
liquidity management issues
Agent Network: Liquidity Management8.1
Assessment
Benefit of the
Solution
• Mobile money operators can benefit from real time
liquidity management. Banks and MNOs benefit
from well optimized agent locations
• Public benefits from availability of cash and e-float
when they need it.
• Insurance companies, banks, and other service
providers can also utilize this underlying solution to
optimize the locations of their offerings, to
effectively utilize their marketing efforts, etc.
• Other benefits accrue significantly over time
Implementation
Feasibility
• We envision this as a third party offered solution
(not tied up with any MNOs or super agent
networks) that can eventually offer many value add
services (eg:aWhere, 4Info, Telefonica, etc.)
• The solution involves machine learning component
where the recommendations and anaalysis are
constantly optimized as new data comes in. Data
mining and network optimization are fairly well
understood
• The solution depends on public sources of
information for population demographics and
geographical information
• Significant upfront implementation costs
Level of
Confidence in
the Solution
• High since this is a proven technology used by
companies such as Telefonica Digital and 4Info.
However, this is a new application of an existing
technologies
High
High
High

56
Solution # 15 Location Based Crowdsourcing for Liquidity
Management
Solution
Description
• A user would post requests for cash, or other
products/services that would get crowdsourced to
nearby users that were registered with the service
• Users can control what requests they would receive
and the periodicity of those requests.
Underlying
Technologies
• Location capture and analysis
• Standard mobile technology for message
broadcasting
• Mobile cataloging
• Mobile app solution to allow users to sign-up,
register their broadcast receiving preferences
(USSD application for basic feature phones and the
application for smart phones will have features
similar to that Foursquare )
Examples • The following companies employ geographical
analysis in their offerings, but their solutions do not
directly touch upon the solution offered here: 4Info,
awhere, loopt, foursquare, etc.
Location based crowdsourcing to provide cash and other products/services
on demand
Liquidity Management8.1
Assessment
Benefit of the
Solution
• This solution can be thought of as a location aware
electronic market place that is supported by
crowdsourcing
• Agents in particular benefit from lower costs to
address their liquidity needs. For example, local
merchants in rural areas who deal with cash from
the sales of their merchandise can provide on-
demand liquidity relief to the agents
• General users can also benefit from lower fee
structure compared to a mobile money operator
• Over time, market intelligence can be built on the
analysis of transactions data
Implementation
Feasibility
• The underlying technologies used to build these
are fairly mature and constitute an easier aspect of
this implementation
• The basic location identification technology is
already wide used in a variety of smart phone
applications such as Loopt, Foursquare,Gowalla,
Google Places, SCVNGR, etc.
• High upfront customer acquisition costs. Safety
and other concerns related to acknowleding to
possess liquidity
Level of
Confidence in
the Solution
• The underlying technologies that will be used to
build this solution are mature. The main challenges
involved in this solution proposal are non-technical
(eg: getting people to sign-up, marketing efforts,
etc.)
Medium
Medium
High

57
Solution # 16 Self Charging Cellphones
Solution
Description
Implementing self recharging capability into cellphone.
A transparent solar panel inserted between the
protection screen and the touch screen of a cellphone
and directly linked to a miniaturized energy convertor
and the phone battery
Underlying
Technologies
Self charging capability uses:
• Transparent and thin solar panel
• Optimized energy convector
Examples • Wysips has developed a transparent solar panel
screen with a 90% transparency factor that have a
capacity of 3 miliwatts per CM2 allowing a 2
minutes conversation for 10 minutes of recharge
Self recharging mobile phone would allow BOP to own and use a mobile
phone for a lower price and thus ease their access to mobile money solutions
Financial Inclusion of People in Remote Areas: Self Recharging Mobile Phone
Assessment
Benefit of the
Solution
• Lower the cost of possession and usage of mobile
phone
• Allow usage of cell phone in area with no electricity
infrastructure
• Give access to Mobile finance services to people in
are with no electricity
Implementation
Feasibility
• Implementation of such capability is easy and
cheap (+1$ by device) but has to be done by cell
phone manufacturer at the fabrication of the phone
• Recharging capability is given for 7 years
• Working with confidence on a 6 miliwatts per cm2
solar panel
Level of
Confidence in
the Solution
• Successful experimental demonstration
• Technology reliable between -20 C and +70 C.
• No large scale implementation so far
Medium
Medium
Medium

58
Solution # 17 Deployment of Light 3G Infrastructure
Solution
Description
Broaden the financial inclusion of BOP in remote area
with no mobile coverage by implementing light
consuming 2G/3G infrastructure. Meanwhile extending
rainfall measurement coverage in the most remote
thus most dry areas.
Underlying
Technologies
Light 2G/3G infrastructure use:
• Passive cooling technology
• Low energy consumption electric components
• High-end solar panel
• Bandwidth reduction algorithms
Examples • Altobridge has successfully deployed such
infrastructure in dozens of country including Nigeria,
Iraq and Ghana
Light 3G infrastructure allow BOP in remote area to access 3g network thus
allowing their financial inclusion throughout mobile money solutions
Financial Inclusion of People in Remote Areas : Light 3G Infrastructure
Assessment
Benefit of the
Solution
• Connect people in remote area to the mobile
network
• Enhance the financial inclusion of people in remote
area by giving them access to mobile finance
service where they live
• Give MNOs access to more customers for a
positive NPV due to the low cost of this light type
infrastructure
• Allow fast and low cost infrastructure deployment
and relocation
Implementation
Feasibility
• Easy and fast implementation as long as the are a
has a reasonable Solar exposition (70w)
• Sweet spot for positive NPV between 500 to 5000
customers in a given area (2KM range for one pod)
• Need to be linked by hardline of wireless to a
satellite station
Level of
Confidence in
the Solution
• Successfully implemented in dozens country
• Technology reliable between -20 C and +55 C
• Implementation in large scale remain to be tested
Medium
Medium
High

59
Social networks can open new ways to reach customers, and generate data
on populations previously not available to financial institutions
Social networks: Usage of social networks as enablers
Solution # 18 Usage of social networks as enablers
Solution
Description
As the use of social networks expands, the
relationships and activities within them can be
leveraged to increase access to financial services.
• Data generated in social networks can be used to
complement existing credit scoring models (or even
create new alternative ones).
• Marketing: data from social networks can help
develop better customized financial products.
• Gamification of savings using social groups
• Liquidity management using crowd sourcing
• Group based lottery linked savings accounts
• Digitization of Chamas, SACCOs, etc.
• Word-of-mouth: customers can advertise financial
products to others in their network
Underlying
Technologies
Feature phone based social networks, smartphone
based ones, and in-between technologies (e.g., FB via
USSD thru Orange), location based analytics,
Examples Neo: checks if applicant‟s job is real by looking at the
number and nature of LinkedIn connections, also
estimates how quickly laid-off employees will land a
new job by rating their contacts at other employers.
Kreditech: analyzes the applicant‟s connections. An
applicant whose friends appear to have well-paid jobs
and live in nice neighborhoods is more likely to secure
a loan.
Movenbank: uses Facebook data to adjust account
holders‟ credit-card interest rates. It monitors
messages on Facebook and cuts interest rates for
those who talk up the bank to friends.
Assessment
Benefit of the
Solution
Can reach customers by means of their social ties,
overcome lack of trust in traditional financial
institutions, better understand customer needs and
preferences.
Can evaluate the creditworthiness of applicants that
don‟t generate traditional financial data
Implementation
Feasibility
Kenya is already relatively advanced in terms of the
use of mobile-based social networks (e.g., Ushahidi,
iCow).
There is high market interest in the fast-growing
mobile industry in Africa (e.g. Microsoft‟s 4afrika
initiative).
Level of
Confidence in
the Solution
There is little doubt that the use of social networks on
mobiles will increase and become commonplace.
However, most solutions in the market that take
advantage of this are in their early stage.
Low
Lenddo: loan-seekers ask Facebook friends to vouch for them. To
determine if those who say “yes” are real friends rather than mere Facebook
contacts, Lenddo‟s software checks messages for shared slang or wording
that suggests affinity. The credit scores of those who have vouched for a
borrower are damaged if he or she fails to repay. social-enforcement
mechanism.
PrivatBank: all of its customers have a mobile phone. Bank uses mobile
number as ID. Customers can receive a commission for referring others.
Only need to pass the interested person‟s name and mobile number to the
bank. The banks‟ agent follows up on the lead. 40000 customer-agents
have sold at least 1 product in 3 months. PrivatBank sells a good portion of
its products this way.
High
Medium

60
Solution #19 Cell-phone tower microwave signals for rainfall
monitoring
Solution
Description
• Microwave signals that cell towers use to
communicate with each other can be used to detect
the amount of rainfall passing between the towers
and can be applied to weather index insurance
• The measurements from each base station are fed
into a spatial modeling algorithm to predict rainfall
across the entire map.
Underlying
Technologies
• Microwave signals between cell phone towers
combined with a spatial modeling algorithm are the
two key elements of this technology solution
Examples • A team of scientists from the Netherlands led by
Aart Overeem from the Royal Netherlands
Meteorological Institute measured rainfall using
information provided by T-Mobile.
• Overeem‟s team studied signals sent between
towers in a four month period between June and
September 2011, with the signal strength measured
every 15 minutes across the approximately 8,000
towers in the Netherlands.
Microwave signals between cell phone towers can be applied to weather index
insurance products
Assessment
Benefit of the
Solution
• This technique can provide a new source of data
for weather index insurance that may help
eliminate basis risk.
• The accuracy of this technology, especially in
areas where the density of cell-phone towers is low
is yet to be determined.
Implementation
Feasibility
• The accuracy is non-linear and dependent on the
density of cell-phone towers.
• The models would need to be redone and
customized for each country.
• The geo-spatial models used to estimate the
rainfall density between the cell phone towers need
to be more sophisticated.
• In developing countries getting access to the data
and making sure it was captured and stored
correctly, may be challenging.
Level of
Confidence in
the Solution
• The technology is in a very preliminary phase and
has just been tested in one location. It has not
been tested or verified in locations where cell
phone tower densities are low.
• The link in getting the data to have a statistically
significant impact on the weather index, over and
above satellite images and other more
sophisticated techniques that can also forecast the
weather is still to be determined.
• Country specific issues related to modeling, data
gathering, data storage, etc. still need to be
addressed.
Low
Analytics: Weather index insurance
Low
Medium

We synthesized the solutions using a prioritization framework to identify the
top solutions
61
Levers might
need to be pulled
for
implementation
Priority
Low Priority
Implement if a
related solutions
can be combined
for greater benefit
Low High
High
Low
Solution Prioritization Framework
This is the secondary criteria where we evaluate at a high
level if the technology solution can be implemented in Kenya
based on
• Number of players who need to participate in the solution
and clear value proposition for them
• What types of players (e.g. Safaricom and Airtel
involvement might be different)
• Regulatory environment amenable to the solution?
• Available infrastructure (e.g. Smartphone availability)
Prioritization Framework
Benefit of the
Solution
This is the primary
criteria (reflected in
quadrant selection
for prioritization)
where we looked at
• How much does
the solution solve
the issue?
• Does the solution
have other “side”
benefits?
* Each circle represents a solution
Level of
Confidence*
This is shown
here for
information and
was not used for
prioritizing. We
included
• Is the
underlying
technology
mature?
• Has this
solution be
proven in other
countries,
sectors etc.
Low
High
Label:
• How easy is customer acquisition? Do we
need a critical mass for the success of the
solution?
• Is the model for implementation clear at
least at a high level
Feasibility of Implementation in Kenya

Using this framework, mobile imaging, recording financial activity and location-
based analytics emerged as the most beneficial and implementable technology
solutions
62
Low
High
High
Low
Solution Prioritization
BenefitoftheSolution
Prioritization of Solutions
1: Risk Model Based On Airtime Mobile Data
2: Risk Model Based On Mobile Financial
Transactions
3: Tracking Tool for Informal Financial
Transactions
4: Capture of mobile financial activity
5: Mobile Accounting Tool Using SMS Data
6: Mobile imaging for ID authentication +
Mobile Imaging for processing insurance
documents
7: Fingerprint-based mobile biometrics
8: Budget management tools
9: Savings applications for individual needs
10: Prize or lottery linked savings accounts
11: Pre-programmed Commitment Savings
Accounts
12: Alternate Savings Products using MM
13: Contactless Technology to address
interoperability
14: Location analytics based liquidity
management
15: Location Based Crowd-sourcing for liquidity
management
16: Self charging cell phones
17: Deployment of light 3G infrastructure
18: Usage of social networks as enablers
19: Cell phone tower signals for rainfall monitoring
Label: Level of Confidence
LowHigh
5
7
611
16
10 4
1
13
17
12
14
15
19
3 8
9
18
2

To cover a wider range of technology innovations, we combined related
technology solutions into five categories
63
Low
High
High
Low
Solution Prioritization
BenefitoftheSolution
Label: Level of Confidence
LowHigh
5
7
6
16
1013
17
12
14
15
19
3 81811
* These solutions don’t relate to issues identified in Phase 1
1: Risk Model Based On Airtime Mobile Data
2: Risk Model Based On Mobile Financial
Transactions
3: Tracking Tool for Informal Financial
Transactions
4: Capture of mobile financial activity
5: Mobile Accounting Tool Using SMS Data
6: Mobile imaging for ID authentication +
Mobile Imaging for processing insurance
documents
7: Fingerprint-based mobile biometrics
8: Budget management tools
9: Savings applications for individual needs
10: Prize or lottery linked savings accounts
11: Pre-programmed Commitment Savings
Accounts
12: Alternate Savings Products using Mobile
Money
13: Contactless Technology to address
interoperability
14: Location analytics based liquidity management
15: Location Based Crowd-sourcing for liquidity
management
16: Self charging cell phones*
17: Deployment of light 3G infrastructure*
18: Usage of social networks as enablers
19: Cell phone tower signals for rainfall monitoring*
A
B
C
D
4
1
9
2
E

64
To recap, in Phase 1 and 2, we conducted prioritization of issues, technology
solutions and identified five technology solutions for business model
development in Phase 3…
Phase 1 Prioritized Issues Phase 2 Solution Options
1.1 Credit: Credit Scoring Models Risk Model Based On Airtime Mobile Data
1.1 Credit: Credit Scoring Models Risk Model Based On Mobile Financial Transactions
1.2 Credit: Recording financial activity Tracking Tool for Informal Financial Transactions
1.5 Credit: Consumer Data Ownership Capture of mobile financial activity
1.5 Credit: Consumer Data Ownership Mobile Accounting Tool Using SMS Data
Reduction
Mobile imaging for ID authentication +
Mobile Imaging for processing insurance documents
Reduction
Fingerprint-based mobile biometrics
3.1 Savings: Lack of Well-Designed Products for BOP Budget management tools
3.1 Savings: Lack of Well-Designed Products for BOP Savings applications for individual needs
3.1 Savings: Lack of Well-Designed Products for BOP Prize or lottery linked savings accounts
3.1 Savings: Lack of Well-Designed Products for BOP Pre-programmed Commitment Savings Accounts
3.1 Savings: Lack of Well-Designed Products for BOP Alternate Savings Products using Mobile Money
5.1 Transaction costs: Lack of interoperability Contactless Technology to address interoperability
8.1 Agent Network: Liquidity Management Location analytics based liquidity management
8.1 Agent Network: Liquidity Management Location Based Crowd-sourcing for liquidity management
Don‟t Relate Phase 1 Issues Self charging cell phones
Don‟t Relate Phase 1 Issues Deployment of light 3G infrastructure
Don‟t Relate Phase 1 Issues Usage of social networks as enablers
Don‟t Relate Phase 1 Issues Cell phone tower signals for rainfall monitoring
Phase 1 and Phase 2 Output
A
B
C
D
E

Alternative risk scoring model based on capture of mobile data: The solution focuses on
generating a reliable credit score for unbanked customers based on their cell phone usage patterns
as well as capturing their informal financial transactions. This will enable the market to design
customized and risk tolerant financial products for BoP consumers.
ID authentication tools to improve customer acquisition: The solution is focused on
using biometric technology and mobile imaging systems for customer activation, KYC, document
authentication and processing of insurance claims. This will enhance the speed/accuracy and
reduce costs associated with providing financial services to BoP consumers.
Contactless technologies: The solution is focused on using RF SIM/NFC technologies to create
a level playing field among competitors in the mobile finance ecosystem in Kenya. This, in turn,
will reduce costs and enhance the quality and variety or products available to BoP consumers.
Agent liquidity management tools: The solution will use location analytics/GIS technology to
smoothen agent liquidity flows and improve the quality of service available to BoP consumers.
These five categories include, risk scoring models, ID authentication tools,
contactless technologies, agent liquidity management tools, and social networks
to maximize the benefit to BoP consumers
65
A
B
C
D
E Social Networks as enablers in the mobile money ecosystem: Social reinforcement, peer
pressure, etc. aspects of social networks can be utilized across the mobile money ecosystem to
create new financial products, manage liquidity problems, encourage savings, prevent fraud, provide
better access to credit, etc.

These top technology solutions can be applied to solve many of the key issues
that rose to the top in Phase 1, related to inadequate credit & insurance
products, agent network issues and high transaction costs for BoP consumers.
Customer
Activation
Distribution
Payments
Front End
Payments
Back-End
66
1.11.2 Credit: Credit
scoring
models
Credit: Recording
financial activity
• ID
authentication
technologies
• Mobile Imaging
• Fingerprint-
based Mobile
Biometrics
• Building Financial
History through Data
Capturing
2.2 Insurance:
Efficient
onboarding
and fraud
reduction
Transaction
costs: Lack
of inter-
operability
5.1
1.5
3.1
Credit: Consumer
data ownership
Savings: Well-
designed
products
Solutions For Phase 3
• Location Based
Analytics
Solutions Summary
8.1 Agent
liquidity
management
• Credit Scoring
Models for the
Unbanked
Alternative risk scoring models
Agent liquidity management tools
• Location Based
Crowdsourcing
Contactless
Technologies
• Thin film SIM
• NFC
B C A
D
A
B C
D
E E
• Alternate credit
scoring
• Social lending
Usage of social networks
• Digitization of savings
groups
• Gamification
• Targeted
marketing
Usage of social networks
• Social network
based
verifications
• Collective
payments
E
E

Table of Contents
 Phase 1 Output
 Phase 2 Output
 Phase 3
 Appendices
67

68
We will present five business models based on technology solutions identified
in Phase 2 and will create executive summary of the deliverables
Revised Phase 3 Deliverables
Chain
Issues
for the next Phase
Phase 1
Prioritization
Phase 2
and Prioritization
Phase 3
Business Model
Analysis
the Issues
Technologies
Created
Next Phase
- Solution Benefits
- Gap Analysis
Timeline

Table of Contents
 Phase 1
 Phase 2
 Phase 3
 Appendices
69

Table of Contents
 Phase 1 Output
 Phase 2 Output
 Phase 3
 Appendices
70

End to end credit solution consists of capturing data from data sources, credit
modeling and credit reporting
Credit - End to End Value Chain for the Solution
Though the credit reporting value chain is shown sequential here, these elements are really interdependent. E.g. The
type of Credit Modeling might drive what kind of data is captured, which in turn drives what type of data sources are
important to focus on
71
Identify all the potential
sources of data,
focusing only on data
sources that have a tie
in with the “mobile
ecosystem”
For each data
source, identify
players/technolo
gies involved in
data capture
Credit Data
Source
Credit Data
Capture
Phase3
Business Model
Development
Identify different
types of
modeling
available for the
data
Credit Modeling
Reporting of
Credit
information for
lending, savings,
insurance, etc.
purposes
Credit
Reporting
Co-operation with Credit Bureaus
are vital for reporting credit,
however, we have not elaborated
as reporting of Credit is not directly
tied to mobile platforms per se

There are multiple sources of credit data that can be used by a number of
scoring models
Credit - Players Relevant for End-to-End Solution
Consumer’s
Phone
• Financial (e.g. - payment,
savings), and behavioral
transactions (e.g. contact list)
Mobile Money
Platforms
• All the mobile money transactions
go through the mobile money
platforms
Telecom
Providers
• Airtime usage, airtime expenses,
data usage, etc.
Service/
Products
Providers
• Utilities, Retailers, Service
providers, Wholesale supplies etc.
data
Financial
Institutions
• Bank, Insurance companies, MFI,
etc. that provide financial
services
Government
• Social security, welfare, payment
as well as census/demographic
data
72
Credit Data
Capture
Credit
Modeling
Credit
Reporting
Credit Data
Source
• Model based on cash flow
analysis or on extrapolation of
existing score of similar profile
to the unbanked population
• Mixed classical, airtime,
social and behavioral models
(survey, on-field and mobile
data collection)
• Model based on data of of
social network activity both
quantitative and qualitative
• Model based on analysis of
airtime activity both
financial(spending) and
behavioral (qualitative
characteristics of the call –
time, frequency contact list)
Classical
Scoring Model
Social and
Behavioral
Scoring Model
Airtime Based
Scoring Model
Mixed Scoring
Model

Consumer’s phone, Telecom Providers and Mobile Money Platforms are critical
source of data for credit scoring models
Credit - Players Relevant for End-to-End Solution
Consumer’s
Phone
Mobile Money
Platforms
Telecom
Providers
Service/
Products
Providers
Financial
Institutions
Government
73
Credit Data
Capture
Credit
Modeling
Credit
Reporting
Credit Data
Source
Quality of Data
• Need to rely on consumer to install
application on the phones
• Basic phones might not support this
• Be definition, consumers‟ phone is an
excellent source of all the transactions
originating from the phone
Accessibility of the Data
• With 78%* penetration, mobile phones
and hence Telecom providers have the
data that is very relevant
• Safaricom, with 65% market share
is the largest telecom providers and
by definition has the most amount
consumer data
• 31% of Kenya‟s GDP is spent through
mobile phones, making these platforms
extremely important part of any credit
analysis
• M-pesa, because of its dominance,
is required for any scalable credit
data capture solution
• A number of savings, loans, insurance,
solar, health, etc. products are
available on the M-Pesa platform,
providing a good starting point for the
capturing the data
• Most of these services have
partnered with MNOs and Mobile
Platform providers in providing the
services

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GCP Kenya Mobile Finance Deliverable

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