GCP Kenya Mobile Finance Deliverable

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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.

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GCP Kenya Mobile Finance Deliverable

  1. 1. Kenya Mobile Money Final Deliverable Colloquium, April 30th, 2013
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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