Your SlideShare is downloading. ×
GCP Kenya Mobile Finance Deliverable
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

GCP Kenya Mobile Finance Deliverable

2,637

Published on

Wharton GCP project consisting of five students each from Wharton and HEC to identify technologies that can be applied to solve issues in Mobile Finance in Kenya, and increase access to financial …

Wharton GCP project consisting of five students each from Wharton and HEC to 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.

Published in: Economy & Finance, Business
0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
2,637
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
2
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • `
  • Transcript

    • 1. Kenya Mobile Money Final Deliverable Colloquium, April 30th, 2013
    • 2. 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
    • 3. 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
    • 4. 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 • Prioritization Criteria for 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
    • 5. 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
    • 6. … 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 Integration Products Analytics Prof. Bill Maurer Prof. Michael Klein Prof. Colin Meyer Phase 1: Interviews
    • 7. 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
    • 8. 7 Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics 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
    • 9. 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 2.2 Insurance: Efficient Onboarding and Fraud 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
    • 10. 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.
    • 11. 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
    • 12. 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
    • 13. 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 12
    • 14. 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
    • 15. 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 Technology Identification and Prioritization Phase 3 Business Model Development Methodology and Approach
    • 16. 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 • 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 • Prioritization Criteria for 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
    • 17. 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 16
    • 18. 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 • 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 • Prioritization Criteria for 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
    • 19. 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 Integration Products Analytics 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
    • 20. … with an overarching goal within each category Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics 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
    • 21. 20 Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics 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
    • 22. 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 Integration Products Analytics 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
    • 23. … 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 Integration Products Analytics Prof. Bill Maurer Prof. Michael Klein Prof. Colin Meyer Interviews
    • 24. 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
    • 25. 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 Integration Products Analytics 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
    • 26. 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 Integration Products Analytics 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
    • 27. 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 Integration Products Analytics 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 Pain Points and Issues NOTE: Issues are not numbered in any particular order
    • 28. 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 Integration Products Analytics 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 Pain Points and Issues NOTE: Issues are not numbered in any particular order
    • 29. 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 Integration Products Analytics 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 Pain Points and Issues NOTE: Issues are not numbered in any particular order
    • 30. 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 Integration Products Analytics 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 Pain Points and Issues NOTE: Issues are not numbered in any particular order
    • 31. 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 Integration Products Analytics 30 6.1 Main issue is different set of regulations for banks compared to MNOs Pain Points and Issues NOTE: Issues are not numbered in any particular order
    • 32. 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 Integration Products Analytics 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 Pain Points and Issues NOTE: Issues are not numbered in any particular order
    • 33. 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 Integration Products Analytics 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 Pain Points and Issues NOTE: Issues are not numbered in any particular order
    • 34. 9) Mobile Devices: Mobile phone penetration is still low for the BOP Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics 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 Pain Points and Issues NOTE: Issues are not numbered in any particular order
    • 35. 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
    • 36. 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
    • 37. 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 Integration Products Analytics 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.
    • 38. 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 37
    • 39. 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 • 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 • Prioritization Criteria for 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
    • 40. 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
    • 41. 40 Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics 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
    • 42. 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
    • 43. 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
    • 44. 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
    • 45. 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
    • 46. 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
    • 47. 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
    • 48. 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
    • 49. 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
    • 50. 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
    • 51. 50 Creating a tool that gives people the ability to save towards committed goals will enhance savings behavior among BOP Savings: Lack of well-designed products for BOP3.1 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
    • 52. 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 Savings: Lack of well-designed products for BOP3.1 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
    • 53. 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 Savings: Lack of well-designed products for BOP3.1 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
    • 54. 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 Savings: Lack of well-designed products for BOP3.1 Sal Piyali unnayan dal (SHG) Medium Low
    • 55. 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
    • 56. 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
    • 57. 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
    • 58. 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
    • 59. 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
    • 60. 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
    • 61. 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
    • 62. 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
    • 63. 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 Feasibility of Implementation in Kenya 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
    • 64. To cover a wider range of technology innovations, we combined related technology solutions into five categories 63 Low High High Low Solution Prioritization BenefitoftheSolution Feasibility of Implementation in Kenya Prioritization of Solutions 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
    • 65. 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 2.2 Insurance: Efficient Onboarding and Fraud Reduction Mobile imaging for ID authentication + Mobile Imaging for processing insurance documents 2.2 Insurance: Efficient Onboarding and Fraud 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
    • 66. 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 Prioritization of 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.
    • 67. 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 Integration Products Analytics 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
    • 68. 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 67
    • 69. 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 • 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 • Prioritization Criteria for 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
    • 70. Table of Contents  Executive Summary  Project Objectives  Phase 1  Phase 2  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 69
    • 71. 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 70
    • 72. 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
    • 73. 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
    • 74. 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
    • 75. Models based on Airtime and Mixed Data are most relevant for BOP customers in Kenya Credit - Players Relevant for End-to-End Solution 74 Credit Data Capture Credit Modeling Credit Reporting Credit Data Source Using cash-flow analysis and group co-guarantees. Equity Bank – extrapolation of the existing score model Classical Scoring Model Social and Behavioral Scoring Model Airtime Based Scoring Model Mixed Scoring Model Mixed classical, social and behavioral model On-field credit worthiness analysis Model based on data of of social network activity both quantitative and qualitative – Facebook, Twitter, Linkedin (DemystData, LendDo, Neo, Kreditech ) Credit score build on airtime credit product utilization (60% of the users) Model based on analysis of airtime activity (qualitative and quantitative) Pros Examples • Widely used and understood • Incumbent banks already have this Cons • Lack of data required for classical modeling for BOP customers prevents wider application • Manual collection of information is costly, relies on on-field agents • Some data can‟t be formally confirmed • Relatively new model, financial data is not included • Social network activity is limited for BOP • Hard to get access to data • Financial activity is not included • Possible Manipulations • Easy tool to assess credit worthiness of BOP • Can use network contacts as guarantors • Collect wide range of information • Relevant to the BOP specific
    • 76. Not surprisingly, main challenges involve access to credit data, majority of which is being held by the MNOs and Mobile Money Platforms Credit - Challenges or Gaps to Address for Each Player Consumer’s Phone • Difficulty scaling any solution that requires consumers to install application provide data out Mobile Money Platforms • M-pesa is even more dominant than its parent, making partnership with M- Pesa critical Telecom Providers • Safaricom dominance (65% market share) implies any industry wide solution would need to involve them Service/ Products Providers • Expect minimal resistance from these players Financial Institutions Government 75 Credit Data Capture Credit Modeling Credit Reporting Credit Data Source • Expect minimal resistance from these players • Expect minimal resistance from these players • Access and availability of reliable data is the main challenge Classical Scoring Model Social and Behavioral Scoring Model Airtime Based Scoring Model Mixed Scoring Model
    • 77. We have identified value proposition showing all players will benefit from credit solution, including MNOs and Mobile Money Platforms providers Credit - Value Proposition for the Players Consumer • Increased access to credit at a lower rate • Simple tools to manage finances Mobile Money Platforms • Increase use of SMS (in case of InVenture) as SMS is the primary way of performing accounting • Better segmentation of customer base for offering wider set of products • Ability to offer additional services to third party vendors on top of the credit scores Telecom Providers Service/ Products Providers Financial Institutions Government 76 Credit Data Capture Credit Modeling Credit Reporting Credit Data Source • Understanding “needs” of the customers at a deeper level than basic demographic segmentation • A standard credit score that can be used to standardize financial products • Licensing revenue from the technology • Recurring revenue from revenue share or selling credit data to third party Classical Scoring Model Social and Behavioral Scoring Model Airtime Based Scoring Model Mixed Scoring Model
    • 78. Solutions utilizing Airtime Based Scoring Model rely on airtime usage and financial data from MNOs 77 Credit Data Capture Credit Modeling Credit Reporting Credit Data Source Description Data is collected from MNO based on additional agreements and fed into credit scoring applications such as Cignifi Pros • Quick assessment of creditworthiness • Good source data is available as mobile penetration is 78% in Kenya Cons • Requires partnerships with MNO for getting data • Risk model need to be adopted by loan providers for providing loans Option 1: MNO+ Airtime Based Scoring Model Credit - Solution Options
    • 79. Mixed Scoring Model is based on data collected directly from phone or from Mobile Money platforms and FIs 78 Credit Data Capture Credit Modeling Credit Reporting Credit Data Source Description Data is collected from Consumer‟s phone using technologies such as Zebumob and Maana Mobile and fed into credit scoring applications such as InVenture Pros • Enable informal data tracking and avoid additional agreement with mobile platforms operators • Almost of all the relevant data generated using the phone can be captured and made available to the credit model • Wider adoption of Smartphones will make this solution attractive Cons • The solution might not scale readily • Even with the availability of “credit model” and associated scores, MFIs need to adopt the score Pros • Reliable and direct capture of the data Cons • Agreement with M-Pesa might be challenging • Risk model need to be adopted by loan provider Description Data is collected though partnership with Mobile Money Platforms and FIs, and fed into credit scoring applications such as InVenture Option 2: Consumer’s Phone Data Collection + Mixed Analytics Option 3 : Backend Data Collection + Mixed Analytics Credit - Solution Options
    • 80. In the short term, we recommend Airtime Scoring Model using a Cignifi type of solution for quick win… Credit – Short Term Recommendations 79 Option 1: MNO+ Airtime Scoring Model • Establish partnership among MNO (data provider), Cignifi (score provider) and MFI (loan provider) by – - Funding start ups such as Cignifi, MNOs and MFIs to perform pilot project - Use the clout of Gates Foundation to bring MNOs together to ally their fear about losing control on their customer data by partnering with players such as Cignifi Utilizing Airtime Scoring Model will provide the greatest short term benefit by expanding micro credit to the entire Mobile operators customers • Current model is limited to the users of Okoa Jahazi (airtime credit product), which is 60% of the MNO users. CBA conduct simple scoring analysis • Enhanced M-Shwari can use Cignifi to expand it to the 100% of the Safaricom users, providing more reliable model for CBA Enhanced Applying this model to the existing product of M-Shwari will expand M-Shwari it to 100% of Safaricom users Quick Win Example
    • 81. In the long term, we recommend building up Mixed Scoring Model to expand credit product to BOP Credit – Long Term Recommendations 80 We recommend two parallel assessment of funding both Option 2 and Option 3 with the goal of rolling out a Mixed Scoring Model solution Option 2: Consumer’s Phone Data Collection + Mixed Scoring Model Option 3 : Backend Data Collection + Mixed Scoring Model • Provide financial assistance to Zebumob for the assessment of rolling out its application to feature phones • Fund a market assessment project (in partnership with Association of MFIs) for MFI adoption of this solution • Help establish partnerships and fund pilot involving MNOs, FIs, and companies such as InVenture for end to end credit solution • Fund a market assessment project (in partnership with Association of MFIs) for MFI adoption of this solution
    • 82. 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 81
    • 83. Promote end-to-end mobile solution for the health insurance vertical with new sources of value through use of mobile imaging, biometrics technologies and mobile money platform Mobile Imaging - Business Model Overview A business model proposition with new ways of engaging BOP customers based on : • Convergence of mobile innovative technologies: mobile imaging, biometrics and mobile money platforms to ensure convenient delivery of individual health insurance • A partnership between stakeholders aimed at increasing BOP reach and decreasing costs • The guidance and support of the Gates Foundation to establish the partnership 82
    • 84. Mobile Imaging -Technology Description Government database Insurance database ID check for KYC compliance Association of ID and fingerprints Insurance database • Electronic document processing • Data capture Mobile app taking picture Document’s photo ID’s photo Mobile Imaging Technology Fingerprints-based Biometrics 83
    • 85. Use of mobile imaging and biometrics adds value to the mobile health insurance delivery and addresses BOP adoption and fraud issues Mobile Imaging - Proposed Value Chain Quoting Policy Management Premium Payments Claims Processing Claims PayoutSign-up & Biometric card issuance Mobile Imaging Fingerprints Biometrics Biometrics authentication Agent processes ID and relevant customer documents automatically for subscription and policy issuance Agent uses a fingerprints capture device to register customers and for biometric insurance card issuance Health care providers authentify policyholders using the smart insurance card and the patient‟s fingerprints Customers use mobile-money platform to pay premiums and to get information on policy status Agent assists customers in claims reporting and in processing required documents (Doctor receipts for ex.) Customers receive claims disbursement via mobile-money platform ActivitiesTechnologyIntegration + Insurance database + + Insurance database Mobile Imaging Claims ManagementPolicy Life Cycle Services Customer On-Boarding 84
    • 86. The business model requires partnerships between complementary players in the insurance and mobile space in Kenya Insurance Companies Mobile money Transfer Providers Technology Providers Agents Insurance company agents/ Insurance brokers/ MNO‟s agents/Recruited Independent agents Mobile Imaging - Stakeholders in the Business Model Health care providers Leader insurance companies that provide individual health insurance Hospitals, clinics Claims ManagementPolicy Life Cycle Services Customer On-Boarding Insurance company agents/ Insurance brokers/ MNO‟s agents/Recruited Independent agents 85
    • 87. 86 Partnerships and technology use are key success factors to achieve scalability and reduce costs Pros Cons • Multi-distribution channel: insurance agents, insurance brokers, MNOs agents or specifically recruited agents in rural area is key to target mass BOP market • Use of innovative technologies is key to reduce overall costs and achieve low premium prices to BOP • Extendable to other financial services like savings and loans • Material costs at the agents and at the health care providers levels for handheld devices • Low premium prices might not be sustainable as a result of rising inflation and increasing costs of medical related material, bed and consultation charges • Kenyan regulation has acknowledged electronic documents and e-signature use • High penetration rate of mobile money usage in Kenya • Well advanced information technology and mobile payment systems in Kenya Favorable market and regulatory trends in Kenya Mobile Imaging - Business Model Analysis
    • 88. There are several benefits for BOP customers and stakeholders across the value chain Mobile Imaging - Benefits of the Business Model Technology Providers BOP Customers Insurance Companies Mobile money Transfer Providers • Convenience:  Insurance agents perform easy customer registration and provide assistance for claims management via mobile devices  Mobile platform for premiums payment, policy follow-up and claims payout • Affordability with low premium prices • Access to appropriate health care services • Achievement of operational efficiencies that reduce costs:  Reduced time in sales cycle completion and claims processing  Paperless transactions • Risk mitigation based on fraud elimination by reliable and quick identification of genuine policyholders • Increased revenues through a bigger mass market customer base • Increased revenue from:  Partnership with the insurance company  Money transactions fees related to insurance payments • Increased revenue from delivering technology applications Health care providers • Effectiveness improvement of health care service 87
    • 89. The solution requires mobile capabilities development and agents/providers equipped with appropriate handheld devices Mobile Imaging - Implementation Requirements Insurance Companies Mobile money Transfer Providers Technology Providers Agents • Develop and implement an insurance mobile app that covers:  e-application and claims management with mobile imaging features  customer finger prints association to the insurance back office • Develop and implement functionalities linked to the mobile money platform to enable customers to interact with the insurance company and get policy status information • Provide the sales force with appropriate handheld devices for mobile app use and fingerprints capture Health care providers • Provide health providers with smart card readers and fingerprints capture devices 88
    • 90. Mobile Imaging - Recommendation • Identify appropriate stakeholders and provide guidance and incentives for the partnership construction:  Insurance company with:  Large agents network  An existing individual health insurance product  Mobile Money provider with large and spread agents network  Technology providers with:  Mature and scalable technology  Proven application cases • Engage studies for financial construction of the business model • Provide subsidies to cover as needed:  The development and roll-out of required front and back-end mobile capabilities  The required handheld devices for the agents and the health care providers Gates Foundation should engage key players to build the partnerships and financially support implementation of the suggested business plan 89
    • 91. 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 90
    • 92. Contactless Technologies to enable inter-operability 91 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. C : • Near field communication (NFC) is a proximity payments system that has evolved from a combination of the contactless smart card and RFID concepts. • Contactless cards (smart cards, proximity cards and vicinity cards) are most commonly used for security, access and payment applications. • The combination of these technologies, particularly when offered as a mobile phone application, has become known as NFC. • The phone uses the built-in NFC technology to communicate with the merchant‟s contactless payment-capable point-of-sale (POS) system. Contactless Technologies - Overview
    • 93. NFC solution enabled phone requires two key elements – an NFC antenna in phone PCB (printed circuit board) and a SE (secure element) functionality (NFC + SE) + SIM: • NFC package contains both the connectivity chip and the secure element (SE) • Payment/transport application is resident on the secure element (SE) • The Secure Element is different from the Mobile Network provider SIM chip. NFC + (SE + SIM): • NFC + antenna in PCB while SE functionality embedded into MNO‟s SIM • Payment/transport application is resident on SIM operating system • Preferred solution from point of view of MNO‟s since control of accessing / encryption keys inside SIM 92 Source: Mobile Wireless Summit, NXP (Philips) Contactless Technologies - Implementation
    • 94. NFC technology can provide many benefits beyond mobile payments to the customer 93 NFC enabled phones can be used by consumers for: • Mobile payments at POS terminals • Online, offline, and P2P payment applications • Transit fare payments, top-up, toll gate, airline check-ins and access control • Information download from smart posters • Receipt and redemption of mobile promotional coupons • Personal account management • Social networking & peer to peer data transfers Contactless Technologies - Applications
    • 95. Each participant in the value-chain would benefit from their involvement in NFC-based services in different ways… 94 Categories Potential Sources of Revenue from NFC Benefits of Involvement in the NFC market • Consumers are the source of revenue for NFC services, but may benefit from loyalty rewards and other offers • Convenience and faster payment options • Enables consumers to monitor their spending • Hosting and managing third-party applications on the SIM (set-up fees, recurrent fees while the application is active and transaction fees) • Provision of mobile services that complement contactless applications • Increasing subscriber base through partnership with application owners • Revenue opportunities from downloads to the UICC (SIM) • Subscribers to „mobile wallet‟ applications may be less likely to switch service providers • Possible co-branding and cross- marketing opportunities with other stakeholders • Sales of products or services • Convenience and faster payment options • Increased customer loyalty and reach • Increased usage (according to studies commissioned by MasterCard) • Possible co-branding and cross-marketing opportunities with other stakeholders • Possible share of end-user spend • New revenue stream • Market extension Consumers Telecom Providers Contactless Technologies - Benefit for Players in Ecosystem Merchants Payment Platform Operator
    • 96. …and the benefits accrued would depend on the scope and significance of the player’s activities in the value chain 95 Categories Potential Sources of Revenue from NFC Benefits of Involvement in the NFC market • End-user transaction fees • New revenue stream and customer acquisition • May reduce customer churn by deepening the customer relationship • Reduction in cash handling (for micro-payments) and in card issuing costs (for macro-payments) • OTA provisioning allows applications to be remotely disabled or re-enabled • Possible co-branding and cross-marketing opportunities with other stakeholders • NFC-enabled handset sales • Share of end-user spend if providing mobile application • Sales to merchants and payment platform operators • New revenue stream • Market extension • Potential for differentiation from other handset vendors • Possible co-branding and cross-marketing opportunities with other stakeholders • Hosting and managing third-party applications on the SIM (set-up fees, recurrent fees while the application is active and, possibly, transaction fees) • Provision of mobile services that complement contactless applications • New revenue stream • Market extension Financial Service Provider or Bank Contactless Technologies - Benefit for Players in Ecosystem Device Manufacturer Chip Manufacturer
    • 97. Implementing proximity mobile payment is complicated by the number of stakeholders that are involved in establishing the eco-system* 96 Source: Smart Card Alliance Contactless Payments Council White Paper, Sept 2007 * Mobile payment requires the deployment of new technology to consumers, merchants, mobile operators, and the financial community. New business partnerships must be formed among mobile operators, financial service providers, and mobile device manufacturers. Successful implementation must overcome these challenges while delivering benefits to all stakeholders. Contactless Technologies - Gap Analysis
    • 98. Despite technological progress, there are privacy and security concerns about using a proximity system for payments 97 Source: ABI Research, Mobile Payments in China, Sept 2012 Contactless Technologies - Gap Analysis • Inadequate attraction to motivate users • Costs of changing handsets or purchasing NFC peripherals are a roadblock • Theft and resulting loss of personal financial information are major concerns • Market perception needs to change/ consumer has to be educated
    • 99. Lack of infrastructure and high costs of deployment are the critical barriers to deploying NFC in the short-term in Kenya 98 • NFC solution implementation requires three critical infrastructure components: NFC enabled phone, NFC enabled POS, and transaction management server • Incremental cost of deploying NFC enabled devices is quite high ($3-5 per chip) but declining over time. Limited NFC handsets mean a limited potential-user base • More viable as a longer-term solution since networks effects are currently absent in Kenya. Contactless Technologies - Gap Analysis
    • 100. Possible Business Models: Model is named after the player leading market development Pros Cons Suitability for Kenya Option A: The third-party-controlled model High Octopus card in Hong Kong is an example of an applications provider controlling the value chain. • Can be easily deployed in as an embedded card, dongles or even watches • Provides a platform for all players in the value chain for a fee • Can easily onboard players beyond financial services and MNOs (e.g. transportation services, ID services, access to buildings etc) • Incentives among players are generally aligned • The size and complexity of the value chain can vary depending on the players • Requires some level of co- operation among the players • Contactless smartcards can serve as a pre-cursor but might be hard to displace if they get precedence Option B: The collaborative model Medium SK Telecom‟s Moneta card in South Korea is an example of an MNO and financial institutions sharing the value chain. • Partner banks can issue credit and provide banking services • Cross-marketing opportunities between the MNO and partnering banks to each others customers • Shared cost of infrastructure deployment and wide range of services provided • Complexity of the relationships in Kenya make it hard to implement • Complexities of the revenue model and revenue sharing • Complexities in sharing and owning the customers relationship 99 Our research reveals that 3rd party led or collaboratively led NFC business models are better suited for Kenya… Contactless Technologies - Business Model Options
    • 101. Possible Business Models: Model is named after the player leading market development Pros Cons Suitability for Kenya Option C: The financial services led model Low Visa‟s mobile payment trials in Malaysia is an example of a financial-services-driven model. • Uses the established payment value chain • Banks can issue credit and provide banking services • Banks can address security and alleviate customer concerns • Compared to financial institutions, MNOs usually achieve better economies of scale and lower costs when deploying infrastructure (e.g. handsets, POS etc.) • MNOs have a larger established customer base compared to banks Option D: The operator (MNO) led model Low Through its acquisition of stake in Sumitomo Mitsui Card, NTT DoCoMo‟s Osaifu-Keitai („mobile wallet‟) service in Japan demonstrates a mobile proximity payment structure in which the MNO leads the initiative • Kenya has widespread use of mobile phones / services and Safaricom already has an established customer base • Provides additional user convenience and increased services • MNO controls both the mobile and banking relationships with customers increasing monopoly • Requires flexibility in developing and operating the payment value chain and incentives to onboard new merchants • Mobile operators cannot issue credit or banking services 100 ..and financial services led or MNO led NFC business models are not well suited for Kenya Contactless Technologies - Business Model Options
    • 102. NFC is not ready for near-term deployment in Kenya but a potential future candidate • Currently, infrastructure for NFC deployment is absent and the incremental cost ($3- 5 per chip) are fairly large for the Kenyan market – but declining as volumes rise. • The adoption can be increased by collaboration across different players, such as operators, who have existing billing relationships with customers, merchants, OEMs and financial institutions. However, the complexity of the value chain and existing relationships in Kenya are potential barriers. • The 3rd party enabled model is the best suited for Kenya followed by the collaborative model because they address the interoperability issue • The collaborative model is difficult to implement because of the complexities in the value chain in Kenya • The financial services-led model and the MNO led model are poor choices because they fundamentally don‟t address the interoperability issue, and limit customer benefit 101 Contactless Technologies - Recommendations for Client
    • 103. 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 102
    • 104. Among all the players in the mobile money ecosystem, consumers, agents/super agents, and MNOs are most impacted by liquidity challenges 103 Liquidity - Management Tools Player Impact Consumers Service/ Products Providers Consumers are impacted by not being able to receive and/or send cash when they need to do so. MNOs/ Telecom Providers MNOs suffer from damage to their brand (and revenues) from any sustained liquidity shortage challenges. Though third party service providers are not directly impacted by the liquidity challenges at the agents, they suffer from consumers not having liquidity to pay for their services and goods. Financial Institutions Government No direct impact. No direct impact. Agents/ Super Agents Agents make trips to nearest banks or super agents multiple times a day to rebalance their portfolios to be able to service their customers. D 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.
    • 105. Improved liquidity benefits the entire mobile money ecosystem, and more directly the end consumers and the mobile money agents 104 Liquidity - Agent Liquidity Management Tools Consumers Service/ Products Providers Consumers benefit from increased availability of liquidity and e-float when they need it. Beyond these immediate benefits, availability of liquidity everywhere in the country indirectly contributes to the uptake of mobile money services. Categories Descriptions MNOs/ Telecom Providers To ensure return business from the consumers, it is important for MNOs to ensure their agents have liquidity and e-float anytime a consumer needs those. However, implementing any fixes to the agent liquidity management can be expensive. Third party service providers are secondary beneficiaries of improved liquidity in the mobile money ecosystem. They benefit from consumers having liquidity to pay for their services and goods. Financial Institutions Government This can be a positive development for the government whose intent is to increase the usage of mobile money, and to ensure more financial and other services can be delivered through the mobile platform to the people at the bottom of the economic pyramid. No direct benefit other than indirectly benefitting from the improved liquidity in the ecosystem. Agents/ Super Agents Agents, especially, benefit from reduced expenses by eliminating trips to the nearest bank or the super agents and thus save money.
    • 106. • Non-interoperability between the MNOs • Low density of mobile relay tower and the low usage of GPS enabled phones can make location identification challenging. • User (agents or simple user) would post requests for cash, or other products/services that would get crowd sourced to nearby users that were registered with the service • This solution can be thought of as a location aware electronic market place that is supported by crowdsourcing • Agents 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 Description Benefits GAP Analysis Solution Investigated 1: Location based crowdsourcing to provide cash and other products/services on demand 105 Liquidity - Agent Liquidity Management Tools Application Middleware1 Broadcasts requests to registered users Selects the optimal respondent & connects with the requester Cash & e-float exchanged 2 3 1
    • 107. Solution Investigated 2: Standard demand optimization solutions can be used to address the agent liquidity problem 106 Liquidity - Agent Liquidity Management Tools • Agent liquidity problems can be viewed as scheduling and routing problem faced by an MNO in planning its liquidity delivery schedule and routes. • The goal is to minimize the total cost of transportation and cash-on-hand while satisfying a customer service requirement and a time-varying demand at the end-customers. • No incentive to address the issue Liquidity OptimizationM-PESA transactio ns Real-time agent liquidity information Real-time liquidity optimization solution Proposed Technology 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. Description Benefits GAP Analysis • Non-interoperability between the MNOs • Machine learning component where the recommendations and analyses are constantly optimized as new data comes in.
    • 108. Solution Investigated 3: Fund development of geographical information system (GIS) that supports a variety of public good initiatives 107 Liquidity - Agent Liquidity Management Tools • A geographic information system (GIS) deployed either by government, private company or the Gates Foundation and that links locational (spatial) and databases (tabular) information and enables visualization of patterns, relationships, and trends. • The data mining and linear optimization models combine geographical data with demographic information and provides an optimal solution based on a given set of parameters and objectives. Description Solution Benefits • The solution depends on public and government sources of information for population demographics and geographical information. • Questionable quality of the data for certain public and government sources (eg: older census data) • Significant upfront implementation costs. GAP Analysis • Logistics Routing for liquidity management. • Site Selection for financial service providers. • Spread and diffusion for healthcare/vaccination initiatives. • E.g.: Insurance companies, banks, and other service providers can utilize this underlying solution to optimize the locations of their offerings, to effectively utilize their marketing efforts, etc. Public sources of data Government sources of data Private sources of data Decision support analytics
    • 109. Description A user would post requests for cash, or other products/services that would get crowd sourced to nearby users that were registered with the service. Minimal number of cell towers and non-GPS enabled feature phones pose Infrastructure challenges where the solution is the most needed. Description Agent liquidity management implemented through supply chain demand optimization solutions. No incentive for the bigger player to address the issue Solution 3 provides most benefit but unlikely to be profitable short term Liquidity - Solution Options Summary and Recommendations 108 Description Geographical information system that will be the foundation for a variety of public good initiatives. Partnerships with the MNOs are critical for the success of this option. Minimal number of cell towers and non-GPS enabled feature phones pose Infrastructure challenges. Solution 1: Liquidity Management through crowdsourcing Solution 2: Agent Liquidity Management Solution 3 : Geographical Information System for Kenya Pros • Provides better service to BOP by easing access to cash and float for a reasonable price. • Agents can take advantage to overcome their liquidity challenges. Pros • Easy to implement and well tried solutions exist in the market. • MNOs have all the data to make this solution easily and rapidly implementable. Pros • Can support a number of initiatives that help the BOP Cons • Lack of interoperability will practically makes this non- implementable Cons • Not likely be a sustainable private enterprise • MNOs are in a better position to implement this solution Cons • Requires significant amounts of long term investments. • Long term profitability not guaranteed (need to view this as a funding for a public cause)
    • 110. A for- profit enterpris e • Significant investments are needed in technologies, data procurement, and application development. • Capture of consumer mobile transactions data, either through partnerships with the MNOs or through custom applications on users‟ phones, will form a critical component for any meaningful usage of the service. • Projected demand is unlikely to keep the business profitable. MNO • MNOs possess a key data component, namely, customer mobile transactions. • A successful GIS implementation will require a variety of public and private data sources, and the resulting operational costs could make the value proposition questionable from a profit earning perspective. 109 Four business models are possible for solution 3, but only one is assessed to be realistic in the short term and only at a limited scale Liquidity - GIS Business Plan Assessment In-house by Gates Foundation • The Gates foundation should start a pilot project by picking a urban/sub-urban area, and test the viability and useful of the proposed idea. • Similarly to the case of implementation by a for-profit enterprise, significant upfront investments are needed. • Solution efficacy is questionable without mobile transactions data from MNOs. Government held GIS • Under this model, the government would create an agency under the authority of the Kenyan National Bureau of Statistics to create a comprehensive national GIS system. • It is unclear if the government would be interested in engaging directly in this initiative or would like to support a private or a non-profit organization to execute on this idea.
    • 111. 110 Liquidity - Recommendation Low penetration of GPS phone and triangulation limited to urban and sub urban area No incentives for Big players to address the issue No profitable business model for third party involvement We recommend that Gates Foundation pilot the creation of a GIS system with the aim to support a number of public good initiatives Create a GIS pilot project limited to urban and suburban area
    • 112. 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 112
    • 113. 113 Social Networks - Overview ROSCAS CHAMAS ASDAS WELFARE CLAN GROUPSETHNIC GROUPS LINGUISTIC GROUPS KIN GROUPS Social networks exist and play an important role in the lives of people at the BOP
    • 114. Social networks solutions address issues across the entire innovation landscape Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics 114 1.11.2 Credit: Credit scoring models Credit: Recording financial activity 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 Social networks solutions 8.1 Agent Network: Liquidity Management Collective purchasing Customer acquisition thru referrals (use social ties, overcome lack of trust in banking, reach remote pop.) Customized products (leverage peer pressure and social enforcement, gamify savings) Alternative credit scoring models Customer verification (use customers‟ social connections) Digitize informal financial groups P2P payments and money transfer Crowdsourced lending Targeted marketing Cross-platform solutions (transactions across different platforms) Social Networks - Benefits
    • 115. Targeted marketing Collective purchasing Customer acquisition thru referrals (use social ties, overcome lack of trust in banking, reach remote pop.) Customized products (leverage peer pressure and social enforcement, gamify savings) Alternative credit scoring models Customer verification (use customers‟ social connections) P2P payments and money transfer Crowdsourced lending Cross-platform solutions (transactions across different platforms) 8.1 2.2 Insurance: Efficient onboarding and fraud reduction Transaction costs: Lack of inter- operability 5.1 1.5 Credit: Consumer data ownership Agent Network: Liquidity Management Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics 115 1.11.2 Credit: Credit scoring models Credit: Recording financial activity 3.1 Savings: Well- designed products Social networks solutions As an example, we have analyzed digitizing informal financial groups as it directly addresses three key issues Digitize informal financial groups Social Networks – Specific Use Case
    • 116. Digitizing informal financial groups can impact approximately 9 million Kenyans Rural Kenyans feel the need to belong to ROSCAs, ASCAs and Welfare Clan Groups to • 53% of adult Kenyans belong to at least one informal social group, not only for financial services but also for social contacts and networks • An estimated 60 billion Ksh (860m US$) exchange hands every year through informal groups in Kenya, which amounts to almost 4% of Kenya GDP 116 Informal groups play an important role in extending financial services to people in Kenya • keep money safe • have contingency money for emergency • keep money for emergency use (illness, deaths) • socialize and meet people Source : FSD Kenya – Role of informal financial groups in extending financial access in Kenya (April 2009) Social Networks - Benefits
    • 117. However, these groups face a number of challenges • Manual book-keeping method is followed • Occurrence of fraud and theft is common • Lending rates for groups are different • Only saving and lending money is facilitated • Often collapse due insufficient funds 117 Social Networks - Challenges
    • 118. Digitizing informal groups will help overcome these challenges 118 Consumers Technology Solution Providers • Less tedious and time consuming • Minimizes the risk of fund • Transparency of information • Opportunity to socialize among a wider group of people • Enables of capture a wider unbanked segment of rural consumers • Offer wider product portfolios • Higher revenue generation • Data captured can be used for analytics (social and behavioral scoring model) • Data generated can be used for consumer analytics and credit risk scoring • Design better mobile applications/ solutions e.g. a chat application • Enhance financial inclusion • Decrease risk of fraud • A step towards legitimizing informal financial groups Players Benefits Financial Institutions Government • Recording all financial operations digitally • Use mobile platform to share information and socialize • Incentivize groups to use formal financial services (zero balance accounts) • Offer customized group products beyond savings and lending • Offer mobile money platform to facilitate money transfer • Back end integration between BOP and financial institutions • Create customized group applications • No direct role in implementation Mobile Network Operators Implementation Social Networks – Implementation
    • 119. We drew an analogy from a business model can be adapted to Kenya 119 CASHPOR • Acts as the banking correspondent and bridges the last mile gap between the poor and the financial service provider by using using its field agent network • All customer acquisition is done digitally ICICI BANK • Offers zero balance accounts to rural women and holds deposits EKO TECHNOLOGIES • Facilitates the financial institutions to bring a variety of products • Integrates the front and end back end platform MFI BANK TECHNOLOGY PARTNER INDIA KENYA Social Networks - Examples This model increased number of customers by 30% within the first year
    • 120. All social network solutions face a common roadblock Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics 120 Collective purchasing Customer acquisition thru referrals (use social ties, overcome lack of trust in banking, reach remote pop.) Customized products (leverage peer pressure and social enforcement, gamify savings) Alternative credit scoring models Customer verification (use customers‟ social connections) Digitize informal financial groups P2P payments and money transfer Crowdsourced lending Targeted marketing Cross-platform solutions (transactions across different platforms) Current informal social interactions (financial and behavioral) are not tracked This lack of information is inhibiting innovation We recommend to focus on addressing this roadblock: 1st step Engage with companies to explore income generating opportunities offered by the data generated through BOP social networks 2nd step Do pilot projects with selected players in order to digitize informal financial groups to test social data capture and data exploitation Social Networks – Recommendations Social networks solutions
    • 121. 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 121
    • 122. 122 Key Takeaways 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 In partnerships with key players, Credit and Social Networks should be funded for pilot projects and Mobile Imaging should be subsidized, while other solutions are not recommended
    • 123. Table of Contents  Executive Summary  Project Objectives  Phase 1 Output  Phase 2 Output  Phase 3  Recommendations and Next Steps  Appendices  List of items  List of items  List out items 123
    • 124. Specific Focus Areas To Achieve Broader Financial Inclusion Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics 124 • Provide customers with last mile touch points (agents or other channels) • Identify and screen agents effectively • Manage liquidity at agent locations • Provide customers with expanded opportunities to use digital money • Design secure interfaces that are multi- functional and easy to use for customers • Develop cost efficient and secure interface/syste ms for businesses to enable B2B/B2C transactions • Automate self-service transactions • Educate the consumer about product/service benefits • Simplify and speed up the customer sign up process • Fulfill Know your Customer (KYC) and Anti-money laundering (AML) requirements at sign-up to address risk • Invoke decision analytics to match clients with products • Design robust, flexible and efficient payments infrastructure that is low cost and fault tolerant • Automate back-end processing to handle large volumes of transactions reliably • Develop standards and protocols to achieve inter- operability • Design and implement open loop architecture • Identify customer needs, map latent demand • Utilize mobile usage data to design financial products and other services • Utilize push/pull marketing strategies to drive product /service adoption • Develop credit risk models based on mobile usage data • Use geo- mapping and analysis of social networks for targeted marketing of products and services to customers • Minimize incidence of fraud through predictive analytics models Appendix: Innovation Landscape Focus Areas
    • 125. 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) 125 9) Mobile Devices: Mobile phone penetration is still low for the BOP We 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 Integration Products Analytics 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… Appendix: Pain Points NOTE: Pain Points are not numbered in any particular order
    • 126. 126 Pain Points Impact Score (max 5) Underlying Issue(s) Issue Specificity Score (max 5) 1) Credit: adequate credit based financial products are not available to under- banked and un-banked people Access to credit-based products is a significant problem impacting the BOP but the impact is somewhat reduced due to informal personal lending options 4 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 Issue is well-defined and solvable using data mining tools 5 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 Barriers to incorporating informal channels into a formal system/process are not very clear 4 Available data is not shared among players - MNO consumer airtime usage and payment activities are not available to banks or financial institutions Clear issue is around co- operation among various players 5 Regulations around credit reporting do not require positive events e.g. only negative events (non-payment) are required to be recorded Not very clear in terms of types of regulations required, especially in Kenyan context 3 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 Barriers involved (offline and online financial history) are clear, though integration with mobile platform is not very clear 4 1.1 1.2 1.3 1.4 1.5 Consolidated Pain Points and Issues Scoring (1/6) Appendix: Pain Points and Issue Scoring Indicates No Clear Technology Solution and Dropped From Further Consideration Issues Prioritized for the next phase
    • 127. 127 Consolidated Pain Points and Issues Scoring (2/6) Pain Points Impact Score (max 5) Underlying Issue(s) Issue Specificity Score (max 5) 2) Insurance: Penetration of insurance products in Kenya is very poor - only 6.8% of Kenyans currently use insurance products Lack of insurance can be financially disruptive at the occurrence of one-time events 5 Limited availability and access of need- based, affordable and easy-to understand micro-insurance products to the base of the pyramid Though the issue of limited availability of micro-insurance products is clear, the design/implemen tation of easy-to- understand micro-insurance products is not clear 3 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 Though inefficiencies and fraud resulting from paper records, the agents, tax evasion etc. are clear, this is not an exhaustive list 4 2.1 2.2 Indicates No Clear Technology Solution and Dropped From Further Consideration Issues Prioritized for the next phase Appendix: Pain Points and Issue Scoring
    • 128. 128 Consolidated Pain Points and Issues Scoring (3/6) Pain Points Impact Score (max 5) Underlying Issue(s) Issue Specificity Score (max 5) 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) Lack of adequate savings products (e.g. for education, medical emergencies etc.) has a wide-scale impact on multiple aspects of the lives of people at the BOP 5 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 Although standard savings products are available, Banks have not clearly identified the needs / concerns of the poor and ways to translate them into appropriate savings accounts 4 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 (including the specific example illustrated above) There are a number of barriers - both cultural, social, and structural (eg: education, awareness, banking infrastructure, etc.) that are contributing to the limited adoption of savings products by the poor. Banks have not performed adequate research to identify all these barriers 1 3.1 3.2 Indicates No Clear Technology Solution and Dropped From Further Consideration Issues Prioritized for the next phase Appendix: Pain Points and Issue Scoring
    • 129. 129 Consolidated Pain Points and Issues Scoring (4/6) Pain Points Impact Score (max 5) Underlying Issue(s) Issue Specificity Score (max 5) 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) Although farmers are greatly impacted by pricing inefficiencies and fraud, they comprise only modest sub- section of the BOP 2 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/exchange between local channels is currently limited. Limited number of co-operatives exist (currently only 11 milk sheds with significant processing facilities exist). Collective selling could increase selling power The barrier to information sharing among diary farmers is clear, but the complexity of the dairy value chain makes the interdependencie s unclear 4 5) Transaction costs: High mobile money transaction costs (could be as high as 30%), especially on transfer of small amounts negatively impact BOP population Over 95% of mobile users, who utilize mobile money are impacted by this but the impact is relatively less severe considering more expensive / inconvenient alternatives 3 Lack of interoperability 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 The barriers associated with MPESA's monopoly are very clear 5 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 Lack of agent sharing among banks is a well- understood issue but requires banks to co- operate 5 5.1 4.1 5.2 Indicates No Clear Technology Solution and Dropped From Further Consideration Issues Prioritized for the next phase Appendix: Pain Points and Issue Scoring
    • 130. 130 Consolidated Pain Points and Issues Scoring (5/6) Pain Points Impact Score (max 5) Underlying Issue(s) Issue Specificity Score (max 5) 6) Transaction Convenience: Bank branches/agents are less widespread than mobile money agents, hence limiting availability of traditional banking services Transaction inconvenience primarily affects the BOP in the most remote parts of the country and is not as widespread 1 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 The issue of differential regulations for banks compared to MNOs is well- understood and clear. 5 7) B2B / C2B transactions: Businesses prefer cash vs. mobile money but MM can provide several advantages (e.g. accounting, fraud, safety, operational ease etc.) Converting B2B and C2B transactions to mobile money can have significant impact for small businesses and merchants and an indirect impact on the BOP 2 No easy integration of MPESA with either business IT platforms or traditional banking services because most businesses using MPESA’s Pay Bill or Bulk Payment services don’t have access to application programming interfaces (APIs) or the skills to use them. As a result, MPESA transactions are not automatically reflected in their corporate IT systems. They must enter their MPESA transactions manually into their corporate IT system, introducing delays, errors and risk of fraud into what should be a fully electronic process Limited understanding of MPESA's API and the interdependency with each small business' IT system makes this problem complex and unclear. 2 6.1 7.1 Indicates No Clear Technology Solution and Dropped From Further Consideration Issues Prioritized for the next phase Appendix: Pain Points and Issue Scoring
    • 131. 131 Consolidated Pain Points and Issues Scoring (6/6) Pain Points Impact Score (max 5) Underlying Issue(s) Issue Specificity Score (max 5) 8) Agent network: Consumers have trouble depositing or withdrawing cash because local agents run out of either cash or e-float Agent liquidity issue has a larger impact on the BOP in the most remote parts of the country unlike densely populated areas where alternatives are more readily available 1 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 The issue of agents running out of e- float/cash is well- understood 5 9) Mobile Devices: Mobile phone penetration is still low for the BOP A fairly large section of the BOP does not have access to mobile phones limiting their access to mobile financial services and products 2 BOP cant afford to buy the phone (of those Kenyans living on less than $2.5 USD/day, 60.5% owned a mobile phone (RIA - Research ICT Africa, 2012)) The is a clear issue primarily related to low- income levels of the BOP in Kenya 5 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) The is a clear issue primarily related to lack of infrastructure in Kenya 5 8.1 9.1 9.2 Indicates No Clear Technology Solution and Dropped From Further Consideration Issues Prioritized for the next phase Appendix: Pain Points and Issue Scoring
    • 132. Secondary Research Sources 132 1. Interoperability and the Pathways towards Inclusive Retail Payments in Pakistan. 2. Can Digital Footprints Lead to Greater Financial Inclusion? 3. Financially Inclusive Ecosystems: The Roles of Government Today 4. Branchless Banking in Pakistan: A Laboratory for Innovation 5. Bank-led or Mobile-led Financial inclusion ? 6. The Emerging Global Data Architecture of Branchless Banking 7. The Emerging Landscape of Demand-side Data in Branchless Banking 8. Measuring Financial Access ar ound the World 9. 10 things you thought you knew about M-PESA 10. Non-Bank E-Money Issuers: Regulatory Approaches to Protecting Customer Funds 1. Geospatial Analysis for Financial Inclusion Tracking 2. Can mobile money work for merchant payments? 1. Africa-Brazil Agricultural Innovation Marketplace 2. Financial sector development in Africa – Opportunities & Challenges 3. Ease of doing business in Kenya 4. Mobile Technology: One Core Lesson, Many possible Solutions 5. 12 Country comparisons on research, technology & more 6. Linking Up and Reaching Out in Bangladesh – ICT for Microfinance 7. New Technologies, New Risks? 8. Using Biometric Technology in Rural Credit Markets: The Case of Malawi 9. The Lighting Africa Program – Designing Products for Bottom of the Pyramid. Appendix: Phase 1 Secondary Research
    • 133. Secondary Research Sources 133 1. Why doesn‟t every Kenyan business have a mobile money account? 2. Using credit to grow savings : Results from a mobile pilot in Kenya 3. Markets and poverty in Northern Kenya 1. FSP Strategy Overview 2. An Emerging Platform: From Money Transfer System to Mobile Money Ecosystem 3. Mobile Payments Go Viral: M-PESA in Kenya 4. The Year in microfinance 5. Of Bank Accounts and Behavioural Economics 6. A light Bulb Goes On : M-Kopa innovation 7. Going Beyond Remittances 8. The Big Idea: Domestic Remittances in Africa 9. Does „The Cloud‟ have a Silver Lining for the Poor? 10. Money on the Move: Payments and Money Transfer Behavior of African Households 11. Bridges to Cash: the retail end of M-PESA 12. Out of thin air: The behind-the-scenes logistics of Kenya‟s mobile- money miracle 13. Reach the World‟s Poor with a Transaction-Based Approach 14. Regulating New Banking Models that Can Bring Financial Services to All 1. Mobile Phone Usage at the Kenyan Base of the Pÿramid Appendix: Phase 1 Secondary Research
    • 134. 134 1. Tracking Mobile Money Use in Tanzania 2. The Financial Inclusion Tracker Surveys Project 3. Supporting Strategies in Mobile Money 4. Lessons from Tanzania: Mobile for the Unbanked 5. M-Pesa Helps Farmers Get Insurance Claims 6. Pakistan on The Cutting Edge of Mobile technologies 7. The Mobile Money Revolution in Ghana and Kenya Appendix: Phase 1 Secondary Research Secondary Research Sources
    • 135. 135 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 Appendix: Solution Information Capture and Assessment Framework
    • 136. 136 List of Innovators (provided by the Gates Foundation) Company Insights Authentec IT security. Identity management. Content protection. Fingerprint technology. Has been acquired by Apple in July 2012. Card.io Software library for mobile applications. Scans credit cards using the device's camera, no additional hardware. 15 cents per scan. Acquired by PayPal. Docusign esignature. Sign, send, store documents in the cloud. Has partnered with PayPal for online payment signatures. FinSphere Identity authentication (card payment, e-commerce, online banking). Application authentication. Idscan idscan.net launched a handheld device that can do age and identity verification instantly. It can identify fake IDs, the repeated use of the same ID, and can save the data to prove due diligence on the part of the business operator. InAuth Mobile identity platform for counter-fraud and anomaly detection. Behavioral authentication, location authentication, voice biometrics. Jumio Mobile imaging and ID verification technology. MiiCard Online identity solution. Use offline photo ID check. Reduces the expense of a physical ID check, avoids having to visit a physical bank branch. Veritas Vitae uses it as part of online cv certification service. Works with Yodlee. MiTek Mobile imaging solutions that enable consumers to make a deposit, pay a bill or apply for a new credit card by simply taking a picture of a document using the camera on their smartphone. Adopted by U.S. Bank. Saves deposit handling costs (95% down). Nuance Voice recognition technology. Remote Harbor Wireless data management solutions. Enables remote, anonymous, and confidential capture, storage and retrieval of participant information using biometric data as the sole participant identifier. Voicepay Voice technology. Can authorize financial transactions via a mobile phone using voice signature. XYVerify Mobile payment authentication solutions. Geo-location verification (such as cell tower triangulation + GPS). Got approved to work in Nevada's online poker industry (XYVerify does geotargeting to ensure all players are located in Nevada). 1. Customer Activation
    • 137. 137 Company Insights 7-Eleven Offers pre-paid cards, payment services and cash services in stores. Absa Bank Provides branchless banking via merchants, retailers and employers using just a smartphone. Allpoint Largest surcharge free ATM network driving value through its mobile ATM locator apps, online tools, and financial institution marketing. Amex Serve Offers Serve prepaid card, and can also use the account to send/receive money by email, text, and Facebook. Boom Uses retailers / kiosks to register your Boom account, which allows consumers to easily send/receive money over mobile phone. Cielo Delivers high impact, interactive mobile campaigns for leading brands, agencies, publishers and ad networks using a unified, technology platform and services. Eko Provides bank accounts and banking services via branchless banking infrastructure to customers (~ 80% of whom are migrants or unbanked) through mobile banking. Garanti Provides wide range of financial services to its more than 11 million customers through an extensive distribution network and mobile banking platforms built on cutting-edge technological infrastructure. Green Dot Offer reloadable prepaid cards at over 60k retail locations or online. Lianlian Pay Develops and operates the mobile payment, mobile top-up, and mobile commerce services. Operates a network of over 300,000 small business agents across China where customers can top up minutes on their mobile phones Loopt Loopt produces mobile location-based services via smartphones. MoneyGram Global money transfer service / systems to enable sending and receive money worldwide, primarily through a global network of third-party agents. PayNearMe Offers mfinance transactions with mobile phone and PayNearMe Cards - available at more than 6,200 7-Eleven stores across the U.S. Plastyc Offers prepaid Visa cards iBankUp and UPside. Provides branchless internet based payment and money management services. Qiwi QIWI has an integrated proprietary network that enables payment services across physical, online and mobile channels. QIWI has deployed over 9 million virtual wallets, over 169,000 kiosks and terminals, and enabled over 27,000 merchants in Russia. Smart Smart Money (Philippines) platform provides mobile-based financial solutions to subscribers composed of farmers, airtime retailers, market vendors and other low-income earners, who remit capital build-up deposits and loan amortizations using their Smart mobile phones. Starbucks With Opportunity finance, provides capital grants to select Community Development Financial Institutions (CDFIs). The CDFIs then provide financing to underserved community businesses which include small business loans, community center financing, housing project financing and microfinance. Tesco Tesco has teamed up with O2 to provide a new alternative to overall mobile service, with special Tesco Mobile price plans, a focus on family mobile needs, and Clubcard points. 2. Distribution List of Innovators (provided by the Gates Foundation)
    • 138. 138 Company Insights Tio Networks TIO, a cloud-based bill payment processor, partnered with Mobilicity to providing smartphone-based account management and payment options to their in-store account payment solutions. Wal*Mart Walmart, Target and Bestbuy formed Merchant Customer Exchange, to develop a mobile application, which can work with all smartphones to transact as many forms of payment as possible. Zoona To enable more Zambians to get access to mobile banking technology, Zoona uses a distributed network of 'champion agents'. Zoona provides Kiva loans. 2. Distribution – cont’d List of Innovators (provided by the Gates Foundation)
    • 139. 139 Company Insights Airpay Digital wallet, replaces cards, cash, coupons… Yet to be launched Alipay Third-party payments. Looks very similar to PayPal. Apple Passbook Digital wallet. Stores loyalty cards, vouchers. Works with other iPhone apps. Adoption of Passbook has been disappointingly slow. Beep & Go Contactless payment technology. Launched in Tonga in 2012 by Digicel Tonga. Bitpay P2P digital currency ("bitcoin"). Alternative to Visa, Mastercard, PayPal. Available anywhere there's internet. Charges 0.99% fee for no matter what the transaction amount (PayPal ranges from 3.06% to 33%). Boku Charges purchases to mobile bill (mobile number serves as the ID). No bank accounts, no registration. Customer receives a text, replies "Y" to authorize purchase. Seems to be already linked with several MNOs across several countries (Kenya isn't one of them for the moment). Clover Open, cloud-hosted system that turns any Android-based device into a POS. Service is has recently shut down apparently Echo (Protean) Digital wallet. Stores card inside mobile. Echo card can mimic the cards (can store 3 at a time). Seems expensive ($80 - $100). Hasn't launched yet. Ezetap Mobile POS. Converts feature phones, smartphones, and tablets into POS terminals. Ezetap card-reader fits into the headphone jack. Has partnered with Mastercard and Equity Bank. John Staley had mentioned them at our January meeting. Geode (icache) Digital wallet. Project is dead. Had been funded thru Kickstarter. Google Wallet Digital wallet. Relatively better known than the competition. Google is behind the roll-out of Beba in Kenya (bus fares paid with NFC-enable cards) Groupon Groupon Payments. Turns smartphones into POS. Unclear how successful it'll be given the state of Groupon the company itself. ISIS Digital wallet. Seems to be limited to the Salt Lake City and Austin areas for the moment. izettle Turns smartphones into POS. Use PIN seems to make it more secure. AMEX has invested in izettle. Jib Digital wallet. Claims to be Dwolla + Google Wallet. LevelUp Digital wallet. Works across systems, not tied to a specific credit card or bank. Provides targeted marketing services for more effective advertising. Mopay Online payments using mobiles. Uses mobile number as ID. Present in several countries, Kenya included. mSwipe Turns smartphones into POS. Looks similar to Ezetap. Has partnered with Axis Bank and Prizm Payments to launch Swipeon (present in India). 3. Payments (Front-End) List of Innovators (provided by the Gates Foundation)
    • 140. 140 Company Insights Payfirma POS roll-out, but not just on phones. Seems to have a wider range. Present in Canada Payfone Online payment thru mobiles. Seems to further simplify process by automatically authentifying the customers thru the network, making purchases "1 touch checkout" Paymency Mobile payment. Customer moves funds to payment account first. Merchants can set up loyalty programs. PayOne Online payment. Works across devices (computers, tablets, mobiles, TVs, gaming consoles). Currently suing Home Depot for offering PayPal in- store checkout. PayPal Digital wallet. Online payment. Branching into other forms. PayPal is eyeing the retail market, wants to be an in-store payment method. 125 million digital wallets as of 3/2013. PreCash (Flip) Digital wallet for unbanked customers. Being field tested Seconds Digital wallet. Text payments. Text payments (such as texting "RENT - 1,000" to landlord's Seconds number). Could be even simpler to use than existing mobile money systems. Square Turns smartphones into POS. Visa has invested in Square. Has partnered with Starbucks. SumUp Turns smartphones into POS. TaxiPass Can pay for transportation thru several channels including phones. Could be worth considering depending how many in the BOP segment take public transportation regularly UBL United Bank Limited. Omni: "Branchless banking" thru "Dukaans". G2P, P2P, C2B. Strong performance, success in Pakistan. Got approval to operate in Tanzania, will launch in the coming months. Venmo "Collaborative consumption" (like splitting the bill restaurants). Bought by Braintree. Has Venmo Touch. Customers use a credit card at one merchant, becomes available when shopping at others. Verifone Core business consists of in-store terminals that process credit and debit card payments. Has mobile line of products. Company if bad shape. At risk of LBO. Virtual Piggy Online purchases... by children. Parents can set up accounts, set allowance amounts, spending limits. Appears to be a tool to teach children how to e-shop. Wipit Online and mobile payments. "Cash preferred" market. 10000 locations where consumers can reload cash. Launched mid 2012. Targets the unbanked and underbanked in the U.S. (60+ million people). Has partnered with loyalty program Punchcard. Zong Mobile payments, PIN based. Used on Facebook. Bought by PayPal. Sued by PayOne. 3. Payments (Front-End) – cont’d List of Innovators (provided by the Gates Foundation)
    • 141. 141 Company Insights Bitcoin Digital wallet. "Crypto-currency". Has been in use for over 3 years. Nearly $1 billion now in circulation. Some call it a bubble. Braintree Bought Venmo. Can simplify payouts, no need to keep payees' banking info. Customers include Airbnb, Uber. Simplifies payouts. Can coexist with other payment platforms. Capital One Has introduced mobile solutions Dwolla Charges a flat 25 cents per transaction (no fee for <$10 transactions). Maximizes profits to businesses as credit card fees are eliminated. Emida Prepaid products ranging from wireless to mobile wallets and more. Mobile wallet system already launched in Africa. Used for some lottery systems in Mexico. Freerisk Makes data, algorithms, and tools necessary to perform financial modeling freely available. Alternative to rating agencies. Fundamo Platform provider for mobile financial services. Has processed over 250 million transactions so far, worth over $1.5 bn. Technology provider. Wouldn't reach BOP on its own. Genpact Has acquired Atyati. GANASEVA: end-to-end mobility based technology platform that delivers the complete suite of banking services to the rural population. GloboKasNet Agent network that represents several banks. Agents are connected via satellite network. Present in 6000 localities across Peru. Is able to deploy agents to any urban/semi-urban area using GPRS network. Jana Marketing tool for emerging markets. Can conduct mobile surveys, push promotions, run loyalty programs. In place since 2009, partnerships with MNOs across 100 countries, access to nearly 3.5 bn mobile customers. KlickEx P2P currency exchange. Save bank fees. Can exchange using mobiles. M-Paisa Mobile money platform JV set up in Afghanistan by Roshan (MNO) and Vodafone. Greatly cut down corruption (was first used to pay policemen's salaries). M-Pesa Obvious MCX Merchant Customer Exchange. Retailer-led m-commerce platform. Hasn't launched yet, but could be big since it has big names behind it. Meta Creates card programs mHawala M-Paisa competitor in Afghanistan MintChip Digital currency created by the Royal Canadian Mint. It's also a competitions hosted by the same institution where software developers create apps for currency evolution. MPC Mobile Payments Committee. Committee made of all four major US carriers (AT&T, Sprint, T-Mobile, Verizon) and payment providers (Google, ISIS, PayPal, Verifone, Intuit). Will tackle interoperability, mobile payment law. Prepaid.com Can recharge prepaid mobile phone accounts internationally 4. Payments (Back-End) List of Innovators (provided by the Gates Foundation)
    • 142. 142 Company Insights Prizm Payments POS. Has partnered with Axis Bank and mSwipe Payments to launch Swipeon (present in India). Rêv Payment methods for those who lack ready access to financial institutions. Products: remittances, bill payments, loyalty programs, mobile apps, recharging systems. Stripe Online payments. Partenered with Parse to offer mobile payments The Currency Cloud International payments, currency exchange Tigo Cash Mobile money platform in Ghana, Rwanda Vantiv Full range of back-end services. Vesta Full range of back-end services. Visa Delving into mobile payment. Has invested in Square. Wari Gateway CSI) Shared service provision platform in French and English speaking African countries. Covers postal networks, financial institutions, mutual credit and savings companies, gas stations, shops, businesses. WIZZIT Full banking services thru mobiles 4. Payments (Back-End) – cont’d List of Innovators (provided by the Gates Foundation)
    • 143. 143 Company Insights ACI Offers end-to-end payment infrastructure for Retail, Wholesale, Merchant retail, Community banks and credit unions, anti-fraud Amazon Amazon Payments. Offers innovative ways for way for consumers / businesses to purchase goods and services (including mobile payments) Bango Platform that streamlines the process of collecting payments from mobile users for content and service providers, and web analytics. Billeo ZipThru Buy/Pay offers financial brands the ability to support instant card bill payments made through mobile devices. Byndl Mobile transaction services company providing payments, permissions and relationships for unattended retail environments. Cellulant Offers a mobile money network connected to different platforms across different value chains in Africa Citrus Offers expertise and solutions (development, platforms and APIs) for companies to fast-track their mobile products and services. Corfire Leading expert in providing mCommerce technology solutions and products for financial institutions, MNOs, OEMs, Merchants and tech innovators Frontline SMS Credit Enables organizations and business to easily integrate mobile money with existing systems Homesend Global hubbing service that allows integration of mobile wallets and money transfer systems across different providers. IMPS India gov‟t initiative. Offers interbank e-transfer service via mobile in India. InspirePay Offers POS solutions, mobile retail payment systems, and a universal API that connects to all online payment methods. Intersango Offers Bitcoin - a decentralized electronic cash system using P2P networking, digital signatures and cryptographic proof to enable payments. iPay By Intrepid Data System. Payments processing system incorporating various credit/debit card and mobile money players into a single online/POS gateway. Kopo Kopo A web based mobile payment gateway that enables SME owners to accept mobile money payments from multiple systems. LocationLabs Offers platform that provides access to real-time location, geofencing, security etc. Luup Luup offers a universal mobile payment solution platform that solve a very wide-range of business challenges and enable new services. mFoundry Provides a wide range of mobile banking and retail services including SMS/mWeb/App interface and analytics. 5. Integration List of Innovators (provided by the Gates Foundation)
    • 144. 144 Company Insights Mistral Mobile Mistral Mobile provides innovative and disruptive solutions including apps and middleware to banks, mobile operators and financial service providers. Monitise Offers an enterprise platform to enable financial services, such as, mobile banking, payments and commerce. Obopay Offers comprehensive tailored secure universal mobile solutions platform to help partners launch branded mobile finance services. Paydiant Cloud-based mobile wallet and payment solution that enables banks, retailers, and processors to deploy a branded mobile financial services. PesaPal Provides a simple and secure way for individual and business to make and accept payments in Africa (similar to PayPal but via mobile handset). PlaySpan PlaySpan‟s payments platform and digital goods monetization platform enables producers to power monetization and payments of their content. Remitly Offers a low-transaction cost platform for international remittances ROAM Data Global mCommerce platform provider extending physical POS and virtual eCommerce to the mobile environment. Symbiotic (Moca) Provides mobile application solutions to enable online merchants to accept payments from various MM/card options. Moca: Customers buy „Moca credits‟ via mobile money, which can be used as "MM". Tangazoletu Spotcash. SMS-based system that enables withdrawal of money from a SACCO member's savings/FOSA account to their MPesa account. Western Union Allows consumers to transfer money directly to a mobile phone. Xoom Xoom is an online international money transfer service. Yodlee Provider of personal financial management (PFM) data platform and revenue-generating payments solutions. Zooz Provides a payment platform to enable a quick and easy way to integrate into your app, and pay using mobile devices. 5. Integration – cont’d List of Innovators (provided by the Gates Foundation)
    • 145. 145 Company Insights Angaza - Embedded pay-as-you-go technology that makes high quality solar energy solutions immediately affordable to rural off grid customers. -Launched in June 2010 -Countries the product is available in are Kenya, Tanzania, Zambia & Uganda. -Technology involves cloud-based systems that communicate via the cell phone channel. Azuri Indigo 1. Azuri brings power at scale to off-grid customers in rural emerging markets. 2. Indigo is a solar power technology product that enables users to benefit from clean renewable energy and simultaneously halve their emery spending. 3. Headquartered in Cambridge, UK 4.The products are available across Africa, including Kenya Bill.com 1. Bill.com delivers a complete web-based financial solution for businesses and accountants that provides the tools, information, and collaboration required to better manage their financial tasks and optimize cash flow. 2. Bill.com's game-changing technology allows users to access online bill payments, e-invoicing, document management, and automated workflow through one easy system. 3.In addition to seamlessly integrating with businesses' existing accounting software programs, Bill.com provides financial leaders with a comprehensive view of their cash forecast - making it the only solution that connects a user's banks, bookkeeping, and business. biNu App platform that runs on smartphones & feature phones to provide web-based apps & internet services to users. Cachet Financial solution provider of remote deposit capture solutions for banks, credit unions and MFIs Chexar 1.Provides technology and solutions for converting paper checks into Good Funds defined as irreversible value. 2.Enables any consumer to convert any type of check into Good Funds from mobile and other remote capture devices. Cashpor 1. Not for profit Company that provides microfinance exclusively to Below Poverty Line women in eastern U.P. and Bihar. Doxo Cloud based solution for a digital filing cabinet. Organizes, pays bills, manages paperless documents and stores account info online in free, secure cloud storage. Edo Provides shopping & savings solutions by giving personalized offers and making them automatically available through credit or debit cards and mobile devices. Eyeona Closes the loop between retailers and consumer by increasing customer engagement with mobile gaming through DealMaker, and alert consumers to price adjustments as they happen. Goalmine Online social sharing and investment site that makes investing and saving affordable and accessible to those who are new to the world of investing money. It also allows users to buy, use or give gift cards towards savings goals. Juntos Finanzas Juntos designs and implements empowering personal finance tools for cash-based households. Offers tools that helps families save for a particular goal in Latino Communities. 6. Products List of Innovators (provided by the Gates Foundation)
    • 146. 146 Company Insights Money on Mobile Mobile payment solution company Movenbank A banking service that measures financial health and gives credibility scores to consumers. Manilla Free online account and bill organizer for smartphones Nano Ganesh An electronic automation product that allows farmers to use mobile phones to remotely monitor and switch on irrigation pumps used for watering crops in remote locations. The application was developed by Ossian Agro Automation in Pune, India, and works in conjunction with Tata Teleservices phones. Pestapata Micro credit service that gives loan to poor people without bank accounts in Kenya. Here is how it works. A vender, normally a small shop or kiosk owner, gives a client they trust a scratch card worth between Ksh 250 ($3 USD) and Ksh 5,000 ($63). The client scratches the card to reveal a secrete number that they load onto their mobile phone and are credited a short- term loan on a Safaricom's M-Pesa account. They must repay the loan principle, plus a five to ten percent interest, in less than thirty days. The kiosk owner derives income from the interest. Saveup Savings solution that offers people a reward program to incentivize them to save money and pay debt. Webtribe (Jambopay) Online payment gateway system in Kenya and Africa Zumbox Digital postal mail system that allows electronic mail to be sent. People can claim their Zumbox via a paper mail confirmation code. 6. Products – cont’d List of Innovators (provided by the Gates Foundation)
    • 147. 147 Company Insights 4Info Location based targeted advertising awhere Monitor and evaluate a project objectives using location intelligence Banno Banno builds solutions (interactive, customizable, intelligent, websites and applications) for financial institutions BillFloat Helps avoid late fees for consumers by paying their bills for a fee Cardlytics Cardlytics unites banks and merchants to provide rich rewards to customers based on their individual purchase behavior. Its technology tracks consumers‟ actual purchases, providing the first digital channel that can guarantee offline sales and help consumers realize savings of hundreds of dollars per year on the products they purchase every day. Centrifuge Systems Interactive data analytics and visualization technology Cignifi Cignifi has developed the first proven analytic platform to deliver credit and marketing scores for consumers using mobile phone behavior data ClearStory ClearStory combines and harmonizes data from disparate sources such as relational databases, Hadoop, web and social applications, and third party providers, and enables interactive data analysis Cloudera Cloudera is the commercial Hadoop company, develops and distributes Hadoop, the open source software that powers the data processing engines of the world‟s largest and most popular web sites Datahero Datahero makes it easier for individuals, small companies and even enterprises to visualize and understand their data without having to worry about data formats and SQL queries. DataKind A non-profit that provides data analysis services to non-profits and NGOs Datameer Datameer provides end-to-end Big Data analytics solutions. They use Hadoop for data storage and is fully extensible to integrate with other data warehousing/enterprise solutions. DemystData Aggregate data from a wide variety of sources (social, demographic, geographic, sentiment, etc.) to make actionable predictions Devintel Uses predictive modeling and data mining to enable development organizations to be more proactive in their services. Entrepreneurial Finance Lab Connects lenders to SME borrowers. Uses computer based questions and data analysis to identify low risk borrowers. Experian Microanalytics Offers credit risk management tools for the microfinance sector (uses data mining, predictive analytics, etc.) First Access First Access generates instant personal profiles to facilitate access to financial services and other products designed for consumers at the bottom of the economic pyramid. They use data from a wide range of sources including mobile phones. G-Analytix Consulting services and technology solutions for risk management in developing and emerging markets. 7. Analytics List of Innovators (provided by the Gates Foundation)
    • 148. 148 Company Insights Global Analytics Offers a Big Data platform they call Zebit that combines data from multiple sources to provide transaction level risk assessment (one more level deeper than the consumer level risk assessment) Guardian Analytics Provides security products to financial institutions to prevent breach of their clientele accounts IBM Big Data End-to-end Big Data solution built on Hadoop InsightsOne Offers Hadoop based cloud solutions that offer predictive analytic solutions by extracting data from mobile, social, structured/unstructured data sources. Optimizes offers delivered by email, mobile, in-app, etc. Kabbage Provides working capital for small businesses for a fee Kaggle Provides platforms for predictive modeling Karmasphere End-to-end Big Data solution built on Hadoop L2C L2C leverages alternative payment and asset data to create new predictive credit, collection and marketing scores for over 260 million Americans Lenddo Combines community based microfinance techniques with social media data to serve the underbanked LendFriend Provides an online platform and associated services for lending to people in your social network LendUp Slightly cheaper alternatives to payday loans Microbilt MicroBilt provides online access to consumer and commercial credit bureau data with automated decisioning and collection services. MicroBilt also offers additional data products, which include a basic person search, criminal conviction, bankruptcy, liens, judgments, motor vehicle, employment background and bank history reports OnDeck Provides fast loans to small businesses for a fee Platfora Provides in-memory BI analytics built on Hadoop platform Prior Knowledge Offers predictive analytics based on inferences from data relationships. Acquired by salesforce.com Shopkick Shopkick created a new location technology that allows the app to verify the user is actually present inside a store (GPS is too inaccurate for that). Shopkick broadcasts an inaudible audio signal at a high-pitch frequency that can be picked up by a smartphone‟s microphone, either through the store‟s existing music system or through a small transmitter. The shopkick app decodes the signal and the user receives shopkick‟s universal reward currency called “kicks.” Sociogramics Geared towards underserved banking customers to generate credit profiles based on non-traditional data sets 7. Analytics – cont’d List of Innovators (provided by the Gates Foundation)
    • 149. 149 Company Insights SoFi SoFi provides student loans by connecting students and alumni through a dedicated lending fund. Telefonica Dynamic Insights Telefonica collects mobile data to understand how segments of the population collectively behave. uses anonymized and aggregated mobile network data to enable companies and public sector organizations measure and compare the movements of crowds visiting a location at any given time. Tiaxa TIAXA provides the mobile telecom market with infrastructure, clearinghouse and revenue enhancement services. Tuition.io Offers mint like solution for managing student loans Victrio Victrio secures enterprises against fraud attacks that are originated, progressed or completed on the phone. 7. Analytics – cont’d List of Innovators (provided by the Gates Foundation)
    • 150. 150 Company Insights Altobridge Provide with light and low energy consuming 2G/3G infrastructure to allow mobile coverage of rural areas. Use solar panel and battery as only energy source 2. Distribution List of Innovators (from GSMA conference) Company Insights Telepin Mobile money platform with money transfers data analysis. They are working on real time credit scoring to provide financial institutions W-HA Third party payment platform using scan code as price tag Clear2pay Mobile money platform using an open payment framework to unified under a same platform all means of payment of a given entity Novatti Mobile money platform allowing payment to limited pre-setup recipients and automated savings to dedicated accounts Creova Mobile money platform integrating payment facility to general and health insurance and working in partnership with social financial institution 3. Payments (Front-End) Company Insights Wysips Provide 90% transparent solar panels that allow cellphone to self recharge when put under sun light. Solar panel are placed between touch and protection screen. They are totally invisible and do not undermine scree visibility and comfort. 6. Products Company Insights Trendium Provide analytics tools allowing diagnosis of network malfunction. They are thinking of extending their algorithm‟s application to mobile finance Systex Data analysis of consumption behavior, mainly aimed at targeted direct marketing purpose but which can be use for credit scoring. 7. Analytics
    • 151. 151 Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics A multitude of innovators are introducing products that take advantage of the expanding use of social networks Innovators working on social networks
    • 152. Customer acquisition & evaluation Consumers Mobile Money Platforms Telecom Providers Service / Products Providers Financial Institutions 152 Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics User activity in social networks Data generated on the unbanked Customer referrals Alternative customer verification More efficient marketing & sales Alt. credit scoring models Fewer physical visits NEW CUSTOMERS Social Network Innovations: Increase Financial Inclusion Social connections Data analysis
    • 153. Consumers Mobile Money Platforms Telecom Providers Service / Products Providers Financial Institutions 153 Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics Market research More efficient marketing & sales Targeted marketing & product design NEW CUSTOMERS Targeted marketing and product design Social Network Innovations: Increase Financial Inclusion User activity in social networks Data generated on the unbanked Data analysis
    • 154. Consumers Mobile Money Platforms Telecom Providers Service / Products Providers Financial Institutions 154 Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics User activity in social networks (communications, money transfers, payments) Data generated on the unbanked Data analysis Market research Link platforms together Software applications Provide development platform Provide financial tools Ease of use of mobile money NEW CUSTOMERS Increased accessibility Social Network Innovations: Increase Financial Inclusion Ease use of mobile money
    • 155. Consumers Mobile Money Platforms Telecom Providers Service / Products Providers Financial Institutions 155 Customer Activation Distribution Payments Front End Payments Back-End Integration Products Analytics User activity in social networks (communications, money transfers, payments) Data generated on the unbanked E – Social Networks Data analysis Customer referrals Alternative customer verification More efficient marketing & sales Alt. credit scoring models Fewer physical visits NEW CUSTOMERS Market research Link platforms together Software applications Provide development platform Provide financial tools Ease of use of mobile money Targeted marketing and product design Increased accessibility
    • 156. Consumers by definition are the source of most, if not all of the data, however, capturing data from individual phones might not be scalable Solution A: Credit Data Source and Capture Consumer’s Phone Mobile Money Platforms Telecom Providers Service/Product s Providers Financial Institutions Government Type of Data* • Digital transactions such as - P2P transactions - Bill payment data - MFIs loans and savings - Cash revenues and expenses • Behavioral data such as - Accessing data (e.g. checking frequent weather data by farmer might predict better crop yield) - Contact list, call logs What is the quality of data (comprehensive, relevant) for credit scoring? • Be definition, consumers‟ phone is an excellent source of behavioral data such as contact list, call logs, etc. • For financial data, a mobile phone is only as good as the transactions performed by the consumers. Per our interviews, P2P transactions form the vast majority of mobile money transaction, limiting the breadth of financial data that can be used How easily can we access the data? • Since the data resides on users‟ phone, we need to rely on consumer to install application on their phones • However, basic phones might not be support sophisticated apps on the phone Players In Kenya Consumers with phone 156 Credit Data Capture Credit Modeling Credit Reporting Credit Data Source Analysis Consumer‟s phone are a great source financial and behavioral data. The main issue with relying on consumer‟s phone is scaling, types of phone and breadth of the data, especially financial data * Consumers have tremendous amount of data “offline”, however, we are not considering that as part of this analysis
    • 157. Telecom providers, because of omnipresence of phones, are among the most promising source of data for credit modeling Solution A: Credit Data Source and Capture Consumer’s Phone Mobile Money Platforms Telecom Providers Service/ Products Providers Financial Institutions Government Type of Data • Financial data such as - Airtime, SMS and Data charges • Behavioral data such as - Contact list (who is my friend) - Duration of call - Time of call - Accessing data (e.g. checking frequent weather data by farmer might predict better crop yield) What is the quality of data (comprehensive, relevant) for credit scoring? • With 78%* penetration, mobile phones and hence Telecom providers have the data that is very relevant • Data is limited to airtime (usage, recharge, etc.), but does not include other financial data How easily can we access the data? • The data can be provided by MNO based on additional agreement • Safaricom, with 65%* market share is the largest telecom providers and by definition has the most amount consumer data Players In Kenya 157 Analysis To capture the data, the greatest barrier is getting agreements with MNO. If working with Safaricom proves to be difficult, it might be worth partnering with Airtel, Orange and Yu as these providers will benefit from shared cost and benefit (better risk profiles for their customers) Credit Data Capture Credit Modeling Credit Reporting Credit Data Source * CCK report 2013 (Oct – Dec 2012 review)
    • 158. As more transactions go through mobile money platforms, it will continue to become an important source of credit data Solution A: Credit Data Source and Capture Consumer’s Phone Mobile Money Platforms Telecom Providers Service/Product s Providers Financial Institutions Government Type of Data • Domestic and International remittances • Cash deposit and withdrawal • Service payments • Insurance fees and repayments • Utility payments • Savings and loans What is the quality of data (comprehensive, relevant) for credit scoring? • 31% of Kenya‟s GDP is spent through mobile phones*, making these platforms extremely important part of any credit analysis • This is among the only source of financial data for under banked customers How easily can we access the data? • M-pesa has over 90% of the market share in mobile transactions** and their participation is required for any scalable credit data capture solution (such as CBA for M-Shwari) Players In Kenya 158 Analysis Current services provided through Mobile Platforms are a good substitute for bank statements as a basis to build up financial history, and are desired by many MFIs. Though additional partnership is required to share this information (e.g. M-Shwari) Credit Data Capture Credit Modeling Credit Reporting Credit Data Source *http://qz.com/57504/31-of-kenyas-gdp-is-spent-through-mobile-phones/ ** Our analysis based on amount of cash moved by M-pesa (Ksh 116.6 billion) v/s Tangaza (Ksh 1.31 billion), the closet rival
    • 159. Majority source and use of credit data comes from service providers/FIs/Government, making their participation a critical for credit scoring Solution A: Credit Data Source and Capture Consumer’s Phone Mobile Money Platforms Telecom Providers Service/ Products Providers Financial Institutions Government Type of Data • Service payments • Insurance fees and repayments • Utility payments • Savings and loans What is the quality of data (comprehensive, relevant) for credit scoring? • 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 How easily can we access the data? • Most of these services have partnered with MNOs and Mobile Platform providers in providing the services Players In Kenya 159 Analysis For capturing data for the services provided by these, agreement with both Mobile Platforms and the Service Provider/FI/Government might be required Credit Data Capture Credit Modeling Credit Reporting Credit Data Source
    • 160. Project team Name Shipra Baranwal Education and Qualifications  MBA Participant (2012-2013), HEC Paris  Bachelor of Commerce with a specialization in Travel & Tourism Management (2005), Mount Carmel College, Bangalore, India Experience  Strong team player with an ability to work in a highly customer focused environment. 6 years of diverse Operations in British Airways coupled with Marketing and Business Development experience in retail sales. Formulated and implemented new marketing campaigns. Negotiated progressive improvement in customer facing processes through strategic team management and enhancing supplier relations. Led the operations team in British Airways during transition of operations to new airports in Indian cities. Name Surachita Bose Education and Qualifications  MBA Candidate, The Wharton School, University of Pennsylvania, 2013  Master of City and Regional Planning (MCP with honors), Policy and International Development, University of Cincinnati, 2004  Bachelor of Architecture (B.Arch), Bangalore University, India, 2001 Experience  Sr. Strategy and Policy Planner, Alliances and Partnerships Lead, City of Sunnyvale, CA, Aug 2006 – Oct 2012  Associate Planner, County of San Mateo, CA, 2005-2006  Planner and Business Development Manager, Lane Kendig Inc., Chicago, 2004-2005 160 Team
    • 161. Project team Name Elena Buravleva Education and Qualifications  MBA Participant (2012-2013), HEC Paris  Diploma with honors in ”Finance and Credit (2001), Moscow Humanitarian- Economic Institute  Diploma with honors in “Banking Management” (1999), Moscow Banking School of the Bank of Russia Experience  12 years' experience in Investment Banking. Successfully led integration of Absolut Bank into the KBC Group after acquisition. Implemented Murex system. Start up of Trading & Sales business at Absolut Bank. Managed liquidity of the Bank and proprietary positions of ITD. Successful crisis management of banking portfolio. Best MM Dealer 2006 by MICA. Name Nilesh Khandelwal Education and Qualifications  MBA Candidate, The Wharton School, University of Pennsylvania, 2013  MS, EECS, Ohio University, 2000 Experience  Accomplished consulting executive with nearly 13 years of experience and a proven track record of delivering mission-critical projects to mobile, communications and media companies  Managed full scope of consulting engagements, ranging from strategy and new product/business launch projects to large-scale front- and back-office system implementation project 161 Team
    • 162. Project team Name Asma Ben Gamra Education and Qualifications  MBA Participant (2012-2013), HEC Paris  Engineering degree in Industrial Engineering (2000), Ecole Nationale d‟Ingénieurs de Tunis Experience  11 years' international experience in consulting and project management, implementing financial solutions for retail banks and financial institutions. Managed large-scale integration projects, leading multi-cultural teams and delivering on time and on budget. Excellent analytical skills, strategic planning and risk management. Name Pavan Kota Education and Qualifications  MBA Candidate, The Wharton School, University of Pennsylvania, 2013  Master of Information Management and Systems, University of California, Berkeley  Bachelor of Technology, Indian Institute of Technology, Chennai Experience  Current :Finance Manager, Strategic Planning and Projects, Genentech  Co-Founder: 360 Solar Vertex, a non-profit that builds affordable portable solar energy systems  7+ years of experience delivering technology solutions to address business intelligence and analytics needs. Speaker at SAP‟s annual conference. 162 Team
    • 163. Project team Name Tejas Krishnamohan Education and Qualifications  MBA Candidate, The Wharton School, University of Pennsylvania, 2013  MS & Ph.D., Electrical Engineering, Stanford University  B.Tech., Electrical Engineering, Indian Institute of Technology, Mumbai Experience  Controller, Finance and Business Strategy, Intel Corporation.  Consulting Professor, Department of Electrical Engineering, Stanford University. Published over 80 journal papers and 3 book chapters  Co-Founder: 360 Solar Vertex, a non-profit that builds affordable portable solar energy systems Name Nick Hwang Education and Qualifications  MBA Participant (2012-2013), HEC Paris  BS in Mechanical Engineering (2006), California State Polytechnic University – Pomona Experience  Multi-cultural, efficiency-driven, innovative team leader with over 6 years of worldwide experience in the Energy industry. Demonstrated analytical, communication, and management skills. Keen interest in Consulting, Corporate Strategy, and Business Development.  Diversified experience in Oil & Gas (engineering, project management, business development, cost estimation) 163 Team
    • 164. Project team Name Andrey Vinogradsky Education and Qualifications  MBA Candidate, The Wharton School, University of Pennsylvania, 2013  BA, Pre-Law/Business Administration, University of California, Berkeley Experience  Current: Regional Sales & Operations Manager, at 7-Eleven, Inc  10+ years experience in merchandising, sales and operations with responsibility for 100+ stores in both corporate and franchise environment.  Previous work & volunteer background in various sectors, including real estate, merchandising, sales, operations, logistics, consulting and non-profit. Name Tristan Paris de Bollardière Education and Qualifications  MBA Candidate, HEC-Paris, 2013  Ms in nuclear physics, National institute for nuclear science (Saclay, France)  Ms in telecommunication network, French Naval Academy (Brest, France) Experience  Financial analyst at the French Navy head-quarter (2011-present)  Nuclear submarine propulsion engineer (2007-2011)  Chief of fire fighting brigade (2005-2007) 164 Team

    ×