Unleashing the Power of Data for Financial Inclusion
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Financial Inclusion Indaba insight2impact July 14, 2017 Celina Lee
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Unleashing the Power of Data for Financial Inclusion
- 1. Unleashing the Power of Data for Financial Inclusion Financial Inclusion Indaba July 14, 2017
- 2. Over the last decade we have moved from a data desert to a world of data abundance – but there is still a disconnect between the data and decision making
- 3. Mobilizing the market Catalysing innovation Testing and learning How are we doing this? Unleashing the power of data for financial inclusion
- 4. Where are we doing this? GIS CoP: Tanzania, Kenya, Uganda, Zambia, Pakistan Measurement frameworks pilot: Nigeria, Kenya, Malaysia, Philippines, Mexico, Zimbabwe In-country communities: Rwanda, Kenya, Ghana, Uganda, Tanzania, Senegal, Mozambique, Zambia TAs: KCB (Kenya), Airtel (Uganda) Case Study: Branch (Tanzania) GIS Innovation Grant: Central Bank of Nigeria
- 5. Presentation Overview 1. Data + Financial Inclusion Strategies 2. Data + FSPs 3. It’s your turn
- 6. Where are we doing this? GIS CoP: Tanzania, Kenya, Uganda, Zambia, Pakistan Measurement frameworks pilot: Nigeria, Kenya, Malaysia, Philippines, Mexico, Zimbabwe In-country communities: Rwanda, Kenya, Ghana, Uganda, Tanzania, Senegal, Mozambique, Zambia TAs: KCB (Kenya), Airtel (Uganda) Case Study: Branch (Tanzania) GIS Innovation Grant: Central Bank of Nigeria
- 7. Policies must be informed by appropriate measurement What is the current state? What is the problem and what do I need to do? What indicators and what’s an appropriate target? Measure progress towards goal. Why are there deviations from the goal?
- 8. A closer look at uptake 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Dormant (0 transactions in a typical month) Mailbox (1 - 2 transactions in a typical month) Used (3 or more transactions in a typical month) Source: Findex 2014 % of adults with a bank account
- 9. Zambia: Are people’s needs met? 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Sending over distance Life events Bill payments Growth Resilience Receiving income Local payments Liquidity Savings Credit Insurance Payments Source: Zambia FinScope 2015 % of Zambian adults that indicated using specified financial products to meet each use case
- 10. Survey Pilots: Needs, Drivers, Usage, Outcomes Needs • Resilience, • Ability to Meet Goals, • Liquidity, • Transfer Value (e.g. receive income, make payments, local and int’l remittances) Drivers • Functional • Non-Functional (Behavioral, Cultural, etc.) Usage • Frequency • Value • Recency • Duration Outcomes • Financial Health. Is the person sufficiently: • Resilient? • Able to meet his/her goals? • Liquid
- 11. Presentation Overview 1. Data + Financial Inclusion Strategies 2. Data + FSPs 3. It’s your turn
- 12. Where are we doing this? GIS CoP: Tanzania, Kenya, Uganda, Zambia, Pakistan Measurement frameworks pilot: Nigeria, Kenya, Malaysia, Philippines, Mexico, Zimbabwe In-country communities: Rwanda, Kenya, Ghana, Uganda, Tanzania, Senegal, Mozambique, Zambia TAs: KCB (Kenya), Airtel (Uganda) Case Study: Branch (Tanzania) GIS Innovation Grant: Central Bank of Nigeria
- 13. Potential benefits for FSPs Potential benefits Increased Revenue Decreased Costs Improved customer experience • Enter new markets • Make more informed decisions • Organisational efficiency • Improve scalability • Get distribution and sales channels right; reducing churn • Understand the customer better and tailor offerings to meet needs
- 14. Key Barriers to financial inclusion in SADC? Affordability. • Income too small/irregular or uncounted for Identification • Lack of proof of business address Accessibility / proximity • Banks too far & agents illiquid) Value for money • Product uptake/ Low usage of products-high charges vs low income Awareness • More than 10% adults in Zimbabwe do not use insurance & Mobile Money because they do not know anything about it Source: FinScope Consumer Surveys
- 15. What data sources are available to FSPs to address these barriers? Data Sources Geo-location /GIS data Social Media Data Psychometric Data Mobile application data Sensor / IoT data Satellite Data Biometric identification data Source: i2i, 2016
- 16. How are FSPs using these sources of data? Embracers Incrementalists Ground breakers Collaborators Innovationinobtainingandanalysingdata Innovation in types of data used Airtel Uganda overlays call data records (CDR) with GIS data to identify consumer clusters to design an agent deployment strategy to reach previously inaccessible markets Branch uses alternative data and new analytical capabilities for credit scoring to expand access for underserved and unserved adults in Tanzania Kenya Commercial Bank (KCB) used new segmentation methodology to identify 300 000+ clients working in their agricultural sector from their existing transactional data to deliver targeted services that meet farmer needs Source: i2i, 2016
- 17. What are the barriers for FSPs to use this data? Barriers Uncertainty around business case High initial investment Shortage of required skillsets drive up the cost Sourcing and aggregating data is resource intensive Fear around data security Study in the USA highlighted their need for: • Evidence on risk assessment improvements • Creation of new profitable markets Lenddo reported that they incurred significant costs in the initial data gathering, development and training of their algorithm and the initiative took several years. The increasing variety of alternative data sources means that data integration and quality will become a bigger factor in the success of new data and analytical methodologies Banks are cautious to outsource consumer data to specialist firms for fear that they will be perceived as taking risks with personal data. Source: i2i, 2016
- 18. Presentation Overview 1. Data + Financial Inclusion Strategies 2. Data + FSPs 3. It’s your turn
- 20. Nkosi Ncube T: +27(0)11 315 9197 E: nkosi@i2ifacility.org Celina Lee T: +27(0)21 913 9510 E: celina@i2ifacility.org
Editor's Notes
- Introduce i2i without a slide, all the same intro as the lunch presentation
- ~302 Billion emails are sent ~2.6 Million blog posts are written ~4.2 Million minutes are spent on Facebook ~984,560 hours of video are uploaded on YouTube
- Countries are the 25 World Bank UFA 2020 priority countries FI = Financial Institution Interaction: FI Account: Either a withdrawal or a deposit. Therefore, an account is classified as dormant if the account holder does not make either a withdrawal or a deposit in a typical month. Statistical universe: FI Account: The bars represent the % of adults with an account at a formal financial institution. The percentages within the bars, indicate the usage of accounts of those formally served. I.e. the percentage of formally included adults that have a dormant account, mailbox account or use their account. Additional speaker notes: While mobile money seems to be used in Uganda, Kenya and Tanzania this seems to be the exception that proves the rule. The Findex Global Survey found that just two percent of adults globally own a mobile money account. However, in Sub-Saharan Africa, the average was much higher at 12%. This represents more than 64 million adults. While this highlights significant penetration of mobile money, the MAP evidence from the MAP countries where mobile money was present showed shows a similar trend to bank accounts: despite high initial up-take in mobile money subscribers, only between a quarter and a third of accounts are active. Active accounts are usually can be defined as those accounts that have been used at least once in the last 90 days. In Mozambique, mobile money subscribers increased from 485,000 in 2013 to 887, 000 in 2014. However, usage levels have remained low with the proportion of active users estimated to be between 20 – 25%. In both Swaziland and Malawi, despite rapid initial uptake, the proportion of active users has remained below 30% and the rate of growth of subscribers has slowed substantially. In Malawi from 2013 to 2014 mobile money subscribers grew from about 230,000 subscribers in 2013 to more than one million in 2014. However, less than a third of these adults are active customers. Similarly, in Lesotho, rapid initial uptake was partially driven by the fact that the providers offered generous incentives - in the form of an airtime bonus - for signing up. Based on in-country consultations, it is estimated that only between one-third and one-quarter of mobile money subscribers in Lesotho are active.
- Terminology: Incrementalists are located in the bottom left of the spectrum and use traditional data sources with long-established methodologies for conventional business decision-making processes. The use of new data sources or analytical processes is more incremental in nature and is applied to improving existing processes or set up as experimental units. Collaborators are located in the bottom right of the spectrum. Collaborators are traditional players who access new data by partnering with other organisations, typically not in the same sector, to gain additional insight into their customers which they can then use in the design of new services or to reach new markets. The most famous examples are: Mshwari in Kenya EcoCash Loans in Zimbabwe Embracers, located in the top left of the spectrum, recognise the need and power of incorporating new approaches into traditional business practices. They have either adopted new sources of data or new analytical methodologies. In Kenya, Superfluid Labs enhances their client’s existing customer profiles by combining internal client data with third party data (e.g. credit bureau and MNO data) and public data sets such as social media. Ground breakers are located in the top right corner of the spectrum. They use both new data sources and analytical methodologies. Many of these organisations are focused on creating new markets and tend to be in the fintech start-up or entrepreneurial space. Jumo, an African mobile money marketplace provider, uses over 10 000 variables derived from their mobile network and mobile telephone use data, as well as machine learning methods to assess a customer’s credit risk. Data points used include calling records (how many and to whom), airtime usage (amount and top up location), the type of phone used, whether text messages are being sent, data purchases and mobile money transactions. Branch, a digital credit provider, scrapes an applicant’s mobile phone for thousands of data points, including their contact list, phone make and model, and GPS location when a customer downloads Branch’s mobile application and applies for a loan. Through the use of machine learning algorithms, they then assess creditworthiness and have the applicant’s loan in their M-Pesa account in minutes. InVenture, whose mobile application scans applicants’ phones for detailed data points, such as the proportion of their contact list with both first and last names, to predict credit risk and provide credit decisions within 20 seconds. Interestingly, InVenture has found that if at least 40% of an applicant’s contact list were organised with both first and last names, they were 16 times more likely to pay on time.
- Ghana, Mozambique, Pakistan, Rwanda, Tanzania, Uganda, Zambia