Solution Overview

Solution Name:


One-line solution summary:

Transforming Communities By Empowering African Women in Artificial Intelligence and Machine Learning

Pitch your solution.

The gender disparity in STEM education and disruptive technologies is well documented. The situation is particularly acute in Africa. By assisting African women to excel in data, machine learning and AI, we will combat the negative stereotypes and lack of role models that historically have limited female participation in STEM education and related professions across the world. 

We propose to leverage the existing Zindi machine learning solutions platform and its community of more than 13,000 data scientists, to launch a coordinated program that Attracts, Encourages, Educates and Supports African women throughout their data science and machine learning careers.

By scaling our initiative globally, we will create a vast cohort of female role models working to solve the world’s most critical problems. This will inspire women and help create pathways whereby marginalised women in emerging economies around the world, are encouraged to undertake STEM education and careers in technology. 

Film your elevator pitch.

What specific problem are you solving?

Learning for Girls and Women
According to the World Economic Forum 2020 Gender Gap Report, Data and Artificial Intelligence (AI) is one of the eight micro-clusters with the highest employment growth rate. However women represent only 26% of this category. 

AI and machine learning skills are readily applied in fields as diverse as environmental policy, healthcare, agriculture and logistics. Given the proven ability of AI to transform societies, the continued under-representation of women in AI makes us all worse-off. 

Of Zindi’s 13,000 predominantly African data scientists, approximately 2,600 are female. Female under-representation begins very early in a girl's education and career. African females in particular suffer disproportionately from restrictive cultural expectations and systemic discrimination that sees them cut short their education to seek livelihoods in manual work or in the home.

In the United States, only 18% of data science roles are occupied by females. While the corresponding African numbers are hard to come by, we can safely expect the percentage of African female data scientists to be much less. It is evident that the lack of female role models in data science and related careers makes it much harder for disadvantaged women to break out of this cycle.

What is your solution?

Our solution is made up of 4 pillars.

University and high school outreach
: By expanding our network of Zindi ambassadors and by enlisting female data scientists, we will increase the awareness and attractiveness of data science for girls in African universities and high schools. 

Mentorship programs
: We will create mentorship program where experienced female data scientists, can nurture the skills of their less proficient counterparts throughout their careers.

Competitions and Tutorials
: We will expand our existing library of tutorials and competitions to increase learning opportunities. Incentives will be a combination cash, points and badges.

Job placements
: We will further develop our jobs proposition and build a female-friendly jobs portal that will make it easier for Zindi data scientists, and especially women, to find data science jobs.

Women-only competitions: In order to nurture the skills of the women on our platform, we routinely reserve a number of competitions for female data scientists. This provides a safe place for women to explore and develop skills in machine learning. It also allows us to identify, develop and showcase the best female data scientists in Africa. We will host more of these events.

Who does your solution serve, and in what ways will the solution impact their lives?

Female Zindi data scientists on Zindi are smart and passionate and are committed to supporting each other and providing opportunities to younger, less advantaged women. 

Zindi maintains close partnerships with women-focused data science communities across Africa including: 

  •  R Ladies (Local chapters in South Africa, Kenya, Ethiopia, Egypt, Nigeria, Benin, Ghana, Morocco, Tunisia, Uganda) 
  • Women in Machine Learning & Data Science (Local chapters in Kenya, Nigeria, Morocco, Botswana, Mauritius)
  • Py Ladies (Local chapters in Nigeria, Uganda, Madagascar

We engage these communities regularly through discussions, webinars and online and physical meet-ups. This has given us a sound understanding of the African female data scientist’s career journey and insight into how to overcome the challenges she faces along the way.

Our solution will address the needs of aspiring female data scientists and support girls in STEM education by:

  • educating young girls in high school and universities on the possibilities of a career in data science
  • providing an organised outreach program through which established female data scientists can “give back” to, and support younger women in schools and universities
  • supporting female data scientists to grow their skills by solving real world challenges 
  • offering experienced mentorship throughout their careers
  • opening up data science job opportunities 

Which dimension of the Challenge does your solution most closely address?

Strengthen competencies, particularly in STEM and digital literacy, for girls and young women to effectively transition from education to employment

Explain how the problem, your solution, and your solution’s target population relate to the Challenge and your selected dimension.

In a post COVID-19 world, it will be critical to find intelligent, data-driven solutions to the world’s most pressing problems. African women will be especially vulnerable to the dislocations of poverty, job loss, and illness that the pandemic has laid bare. 

At Zindi, we have several proof points of how machine learning and AI has changed lives for the better. Our initiative is well aligned to the Challenge in that it: 

  • strengthens African women’s skills in data science 
  • helps them contribute to the solutions we desperately need
  • provides girls in high schools and universities with positive role models. 

What is your solution’s stage of development?

Scale: A sustainable enterprise working in several communities or countries that is looking to scale significantly, focusing on increased efficiency

In what city, town, or region is your solution team headquartered?

Cape Town, South Africa

Who is the primary delegate for your solution?

Celina Lee, CEO of Zindi

More About Your Solution

If you have additional video content that explains your solution, provide a YouTube or Vimeo link here:

Which of the following categories best describes your solution?

A new business model or process

Describe what makes your solution innovative.

Zindi’s top 4 competitors are Kaggle, AnalyticsVidhya, Omdena and Driven Data. Like Zindi, these platforms curate challenges and crowd-source machine learning solutions. They have each taken a different approach to the core competition format.

The largest is Kaggle, a US platform with more than 2 million registered users and acquired by Google in 2017. 

Omdena has adopted a collaborative as opposed to a competition format, and invites technology practitioners to work together on problems. 

AnalyticsVidhya's community of data practitioners, thought leaders and corporates leverages data to generate value for businesses. 

Driven Data works with international non-profits and focuses on “data science competitions to save the world”.

While we have enormous respect for these platforms, Zindi is unique because:

  • Our focus on African issues, provides hyper-local relevance to our data scientists and the organisations we work with. Indeed, many of our data scientists were registered on other platforms prior to the launch of Zindi. With Zindi, they relish the opportunity to work on problems that they intuitively recognise as being of importance to their communities.
  • We have a growing network of ambassadors who engage with budding and established AI communities in their respective countries across Africa. This allows us to be much closer to the real issues on the ground than our competitors can
  • We have a superior network into the sources of African AI talent. e.g. In March 2020, Zindi ran the first ever pan-African AI hackathon which attracted more than 1000 participants from over 50 African universities.

Describe the core technology that powers your solution.

Zindi’s platform has been custom-built to host a community of data scientists solving AI challenges. The platform is hosted on a cloud service. There are three major components to the platform - a frontend, a backend and an administrative portal. Various cloud services are used to host frontend and backend assets as well as administrative assets. 

Data scientists sign up and create and edit their user profiles including data science interests, education and work history. As data scientists compete in Competitions and Hackathons, we receive and store all submissions.  A number of scoring metrics have been built. These are selected depending on the type of data problem. Data scientists learn and share knowledge through our blog and discussions board. Certificates of competition results are available to data scientists. 

Two email systems exist; transactional emails such as discussion notifications and promotional emails. A number of databases are hosted storing all platform information. A reporting tool queries these databases and helps us understand and track user behaviour. 

We use an agile approach to our software development and host our code base on a leading cloud-based software development and collaboration platform ensuring versioning and code quality. We also host a cloned version of the platform which we use as an environment for testing new platform functionality.

Provide evidence that this technology works.

Since our launch 18 months ago, we’ve achieved a number of important milestones.

  • 13,000+ data scientists registered on Zindi
  • Completed more than 50 competitions
  • Up to 1500 data scientists participate in each competition
  • More than $100,000 paid in prizes to data scientists
  • ~20% of our audience are female
  • ~67% are 25-34 years old
  • ~80% are 18-34 years old
  • Key clients/partners include UNICEF, Devex, CERN, AI4D, Microsoft and IBM

Here is a testimonial from one of our clients involved in conserving marine habitats in the Indian Ocean. 

“Machine learning greatly reduced the time and resources we had to allocate to the data cleaning process. Zindi helped us find machine learning solutions and implement them on our database, creating outputs we can use to inform our decision-making.”  
Justin Beswick, CEO Local Ocean Conservation.

And here are two video links featuring a South African and a Nigerian data scientist. Liemiso Mpota and Khaulat Ayomide typify the young, African women we are working hard to attract and support throughout their data science and machine learning careers.

Please select the technologies currently used in your solution:

  • Artificial Intelligence / Machine Learning
  • Big Data
  • GIS and Geospatial Technology
  • Imaging and Sensor Technology
  • Internet of Things

What is your theory of change?

Our Theory of Change comprises 4 distinct themes: Attract, Encourage, Educate and Support

: Deploy an expanded network of Zindi ambassadors and female data scientists in university and high school outreach
Immediate output: Increased awareness of machine learning as a viable career option
Longer-term outcome: More African females take up STEM related fields
Evidence: The lack of female role models is widely cited as an important barrier to increasing the representation of women in STEM education and related fields. The World Economic Forum 2020 Gender Gap Report further highlights a 26/74 under-representation of women in Data and AI.

: Introduce a structured mentorship program where experienced female data scientists support their less experienced counterparts throughout their careers
Immediate output: More women rise to the top of Zindi competition leaderboards
Longer-term outcome: More African female data scientists are eligible for senior machine learning roles
Evidence: Experienced data scientists must demonstrate technical depth and breadth in data science, and real world impact. See "What do Hiring Managers Look For in a Data Scientist’s CV?" See link here

: Increase learning opportunities by expanding our existing library of tutorials and competitions
Immediate output: As 2 above
Longer-term outcome: As 2 above
Evidence: As 2 above

: Provide job opportunities to female data scientists through a dedicated jobs portal
Immediate output: African female data scientists earn more income in senior roles and reinforce their credibility as role models
Longer-term outcome: Economic progress, improved health and education outcomes for local communities 
Evidence: According to the Clinton Global Initiative, “When women work, they invest 90 percent of their income back into their families, compared with 35 percent for men. By focusing on girls and women, innovative businesses and organizations can spur economic progress, expand markets, and improve health and education outcomes for everyone.” See link here.

: Host women-only competitions as a safe way for women to explore machine learning
Immediate output: More female data scientists enrol on Zindi
Longer-term outcome: The appeal of machine learning and STEM education for women is enhanced
Evidence: As 1 above

Select the key characteristics of your target population.

  • Women & Girls
  • Rural
  • Peri-Urban
  • Urban
  • Poor
  • Low-Income
  • Middle-Income

Which of the UN Sustainable Development Goals does your solution address?

  • 1. No Poverty
  • 2. Zero Hunger
  • 3. Good Health and Well-Being
  • 4. Quality Education
  • 5. Gender Equality
  • 6. Clean Water and Sanitation
  • 7. Affordable and Clean Energy
  • 8. Decent Work and Economic Growth
  • 9. Industry, Innovation, and Infrastructure
  • 10. Reduced Inequalities
  • 11. Sustainable Cities and Communities
  • 12. Responsible Consumption and Production
  • 13. Climate Action
  • 14. Life Below Water
  • 15. Life on Land
  • 16. Peace, Justice, and Strong Institutions
  • 17. Partnerships for the Goals

In which countries do you currently operate?

  • Algeria
  • Angola
  • Benin
  • Botswana
  • Burkina Faso
  • Burundi
  • Cameroon
  • Chad
  • Congo, Dem. Rep.
  • Djibouti
  • Egypt, Arab Rep.
  • Ethiopia
  • Gabon
  • Gambia, The
  • Ghana
  • Guinea
  • Côte d'Ivoire
  • Kenya
  • Lesotho
  • Madagascar
  • Malawi
  • Mauritania
  • Mauritius
  • Morocco
  • Mozambique
  • Namibia
  • Nigeria
  • Rwanda
  • Senegal
  • Seychelles
  • Sierra Leone
  • Somalia
  • South Africa
  • Sudan
  • Eswatini
  • Tanzania
  • Togo
  • Tunisia
  • Uganda
  • Zambia
  • Zimbabwe

In which countries will you be operating within the next year?

  • Algeria
  • Angola
  • Benin
  • Botswana
  • Burkina Faso
  • Burundi
  • Cameroon
  • Chad
  • Congo, Dem. Rep.
  • Djibouti
  • Egypt, Arab Rep.
  • Ethiopia
  • Gabon
  • Gambia, The
  • Ghana
  • Guinea
  • Côte d'Ivoire
  • Kenya
  • Lesotho
  • Madagascar
  • Malawi
  • Mauritania
  • Mauritius
  • Morocco
  • Mozambique
  • Namibia
  • Nigeria
  • Rwanda
  • Senegal
  • Seychelles
  • Sierra Leone
  • Somalia
  • South Africa
  • Sudan
  • Eswatini
  • Tanzania
  • Togo
  • Tunisia
  • Uganda
  • Zambia
  • Zimbabwe

How many people does your solution currently serve? How many will it serve in one year? In five years?

We have estimated our Serviceable Obtainable Market in 2021 to be 32,000 data scientists across Africa of which 30% are women. This market continues to grow as successive cohorts graduate from African universities.

  • Total number of data scientists served today: 13,000
  • Total number of African female data scientists served today: 2,600 (i.e. 20% of total)
  • Total of data scientists served in one year: 32,000
  • Total number of African female data scientists served  in one year: 9,600 (i.e. 30% of total)
  • Total of data scientists served in five years: 100,000
  • Total number of African female data scientists served  in five years: 50,000 (i.e. 50% of total)

What are your goals within the next year and within the next five years?

1 year

  1. Increase female participation in machine learning: Our goal is to increase the number of African female data scientists on the Zindi platform from 2,600 to 9,600
  2. Increase exposure to organisations: While Zindi has done well in attracting data scientists, we need to increase awareness of the Zindi offering for organisations operating in Africa. 
  3. Develop platform infrastructure: Not all our data scientists across Africa have access to the computing power required to train large data sets. This has caused some disparity in competition performance. We have solved for this previously by approaching cloud providers to donate cloud credits to our users on a case-by-case basis. We now aim to develop our platform infrastructure further so that all users can access the same computing power within the Zindi environment.

5 years

By 2025, Zindi will be the pre-eminent destination for machine learning solutions, mentoring, jobs and learning experiences in Africa. Zindi will also be regarded as the place where African women in data science find an empathetic and nurturing environment to develop their skills and professional network. Our outreach programs to universities and high schools will also be fully developed. 

At that point we will replicate the Zindi model and methodology in other emerging markets (such as Latin America and South East Asia) where many of the same issues of underdevelopment, uneven access to technology, and female-under representation in STEM related fields, still exist.

What barriers currently exist for you to accomplish your goals in the next year and in the next five years?

Funding has been an important, but not insurmountable, barrier.

1 year

  • Increase female participation in machine learning
    • University and high school outreach : $30,000 a year (pan-African travel expenses
    • Structured mentorship program : $10,000 (Platform development)
    • Expanding library of tutorials and competitions : $30,000 (Many developmental challenges will come from organisations and communities that will not have the budget to host a competition or fund prizes in the usual way. We need to ensure such challenges are funded and are accessible to the Zindi community to solve.
    • Jobs portal : $10,000 (Platform development)
    • Women-only competitions : $20,000 a year (Cash prizes)
  • Increase exposure to organisations: We need better access to international development and mission-based networks. We have considered hiring a marketer with technology and Africa experience, and a strong network. (Estimated cost: $100,000 a year)
  • Upgrade platform: We need to engage a (Ruby) Developer to build additional functionality on the platform. This will give all users the same enhanced compute power and memory and improve access. It will also allow the preloading of datasets in the Zindi environment, thus eliminating the need for users across Africa, to copy large datasets over often unreliable networks.  (Estimated cost: $25,000 - $50,000)

5 years

The key barrier here will be the extent to which we prove the Zindi model in Africa over the next 5 years. We are confident that with the right partners, we can overcome language and cultural barriers in other emerging markets.

How do you plan to overcome these barriers?

In the absence of external funding, we are focusing on achieving profitability and overcoming the barriers as described below.

  • Increase female participation in machine learning: We continue to engage in a number of initiatives such as:
    • University and high school outreach : In March 2020, we ran the first ever pan-African inter-university hackathon with more than 1000 students from over 50 universities participating. We’ll be hosting more of these events.
    • Structured mentorship program : We are developing our platform to include a more robust “discussion” functionality. This will make it easier for data scientists to provide technical support to each other.
    • Expanding library of tutorials and competitions : Due to the COVID 19 pandemic, we have successfully pivoted to introduce a series of COVID-19 competitions under the Zindi Weekendz banner. We are also reaching out to our leading data scientists to volunteer tutorials related to each competition or topic.
    • Jobs portal : On hold due to the slow-down in data science job opportunities globally. 
    • Host women-only competitions : We continue to reserve certain competitions for women, as we did here.

  • Increase exposure to organisations: We continue to market by word of mouth, regular online posts and a monthly newsletter. Increasingly, Zindi is attracting positive media attention such as in this Techcrunch article.

  • Upgrade platform: We generally approach cloud computing providers to provide our users with computing credits on a case-by-case basis. We will continue with these arrangements until we can upgrade our platform.

About Your Team

What type of organization is your solution team?

For-profit, including B-Corp or similar models

How many people work on your solution team?

Full time: 5

Part time: 2

Contractor : 1

How many years have you worked on your solution?


Why are you and your team well-positioned to deliver this solution?

Multicultural team: The three co-founders of Zindi hail from South Africa,  USA, and Ghana

Diverse team: 2 of of the 3 co-founders are women. Of the 8 people working on the solution, 5 are women.

Role clarity: The co-founders' roles are split between Technology, Sales and Administration

Background: Our co-founders have lived and worked in countries throughout Africa, North  America, Latin America, Asia and Europe

Relevant Experience: We have experience founding and running a technology start-up. See Collectively the founders have more than 50 years experience spanning international development, data science and engineering

Proven growth: We’ve grown from an idea to more than 13,000 data scientist users in 18 months and continue to grow at a rate of 800 - 1200 users a month

Strong network across Africa: Our team has grown Zindi to the point where it is has representation in almost every country in Africa. We’ve also established a network of ambassadors on the ground who take the Zindi mission into local learning institutions and communities

Trustworthy: Large organisations trust us to deliver impactful solutions. These include the likes of UNICEF, CERN, Microsoft and Uber. 

Sound financial management: We manage scarce resources well. Even in the midst of the COVID-19 crisis, we are close to profitability.

Strong governance: Zindi is incorporated in Mauritius which as an international financial centre, ensures our internal processes and governance are maintained to international standards.

What organizations do you currently partner with, if any? How are you working with them?

Zindi currently has three groups of partners for sourcing machine learning datasets and competitions, and for supporting community growth.

1 - Corporates and startups provide competition hosting fees on commercial terms in return for machine learning solutions. These solutions are either to support their businesses or are aligned with  their mission. e.g. Microsoft, Uber and African Bank. In the case of startups where no funding may be available, we find other partners to fund the competitions

2 - Zindi also partners with development organisations and research institutions who have specific thematic areas where machine learning solutions could be of benefit. e.g. UNICEF (flood prediction), DEVEX (document classification), Human Development Innovation Fund (financial inclusion), CERN (particle identification).

3 - Finally, Zindi partners with local machine learning and technology  groups. We host joint events and generally support each other to grow the data science community across Africa.  e.g. Data Science Nigeria, dLab (Tanzania), R Ladies (South Africa).

Your Business Model & Funding

What is your business model?

We provide organisations with machine learning solutions in return for a fee. Our data scientists get to improve their skills, win prizes and find jobs while solving real-world problems.

Key resources

  • People: Sales, platform development, community coordination,  marketing and administration. 
  • Technical : Online platform 

Partners and key stakeholders

  • African universities, high schools
  • Meet-up groups, innovation hubs
  • Organisations: Development organisations, corporates, NGOs, startups, governments
  • Cloud computing providers

Key activities

  • Sourcing and hosting machine learning competitions
  • Leveraging competitions into learning and mentorship opportunities 

Type of intervention

  • Machine learning competitions 


  • Data scientists
    • Zindi platform
    • Online marketing
    • Newsletters
    • Webinars
    • Zindi ambassador network
  • Organisations
    • Online marketing
    • Referrals
    • Conferences


  • Customers: Organisations with a challenge that can be reduced to a machine learning problem. Organisations typically pay Zindi to prepare and host competitions.
  • Beneficiaries: Other than the organisations who receive the winning solution(s), our data scientists are able to hone their machine learning skills on real-world problems

User Value Proposition

  • Data scientists
    • Learning experiences
    • Mentorship
    • Cash or other prizes 
    • Job opportunities in machine learning.

Customer Value Proposition

  • Organisations
    • Machine learning solutions 
    • Access to talent in return for a cash or other prize

Impact measures

  • Number of data scientists
  • Number of female data scientists
  • Rate of community growth
  • Number of competitions
  • Prize money awarded
  • Range of machine learning skills exposed
  • Range of developmental and other challenges addressed

Cost structure
Our biggest expenditure is salaries. This will increase moderately as the platform scales. 

All profits are reinvested into platform development and data scientist community activities

Competition hosting : 100%

Do you primarily provide products or services directly to individuals, or to other organizations?

Organizations (B2B)

What is your path to financial sustainability?

We are pursuing a diverse range of revenue opportunities as described below:

  • Competition hosting: This is our core source of revenue. (Hosting fees are separate to prize money which is paid directly to the winners with no deductions or set-offs). We are working to build and maintain a rich competition hosting pipeline by doing more COVID-19 related work and by taking advantage of international media coverage.

  • Advertising: Zindi’s pan-African reach makes it attractive for corporate sponsors. We have been very successful in attracting advertisers for our recent events such as the March 2020 pan African, inter-university hackathon. Organisations that have advertised on Zindi include African Bank, Microsoft, Google and Amazon. 

  • Job placements : Our plan is to develop and launch our jobs placement portal and proposition as soon as market conditions allow.

  • Capital raising: This is not a priority right now as we focus on achieving profitability. However we may seek equity funding at a later stage. 

  • Grants: Zindi’s focus areas (i.e. Disruptive technology: Education: Gender inequality; Community development) are well aligned with certain grant providers who have a similarly strong social mission. We will continue advocating for grant funding wherever possible.
Partnership & Prize Funding Opportunities

Why are you applying to Solve?

Zindi reached 9,000 data scientist users at the beginning of 2020 without any external funding. In that time we’ve come to value the power of ideas and  partnerships and the quality of execution. We are applying to Solve primarily to broaden our points of view, sharpen our analytical thinking and learn from other entrepreneurs and experts with experience in scaling an impact-based proposition, to other geographies, and ultimately across the world.

With specific reference to the barriers cited earlier, Solve can help us overcome them in the following ways:

Increase female participation in machine learning: Here we are looking to learn from organisations and experts with a deep understanding and practical experience of encouraging STEM education in young girls in disadvantaged communities, and that have done so successfully at scale. In addition, we believe Zindi can be a valuable partner to such organisations as we aim to develop and support the careers of women in a fast growing and transformative discipline, namely machine learning. 

Increase our exposure to organisations: Here we are looking to grow the awareness of Zindi within Africa-focused development organisations, corporates, mission based organisations and governments agencies. Membership of the Solve network will provide access to influential forums where we can tell the Zindi story. 

In which of the following areas do you most need partners or support?

  • Product/service distribution
  • Marketing, media, and exposure

Please explain in more detail here.

Stimulating supply

There are many barriers to girls seeking a STEM education, let alone progressing into machine learning and AI. Our Solve partners will deepen our understanding of a young girl’s journey into STEM and help us chart and influence structured interventions that not only keep her on that path, but also present machine learning and AI as a viable career option. 

Stimulating Demand

According to the World Economic Forum 2020 Gender Gap Report, “there have been few demand side efforts to create incentives for women and girls to enrol in STEM education programmes or to create an accelerated pathway for women to be hired into the highest-growth roles of the future.” 

Many organisations still need to be educated about the transformative power of machine learning. Zindi’s competition framework, provides organisations with a low-risk means to do this. We therefore seek partners who can help us extend our demand-side reach.

What organizations would you like to partner with, and how would you like to partner with them?

Century Tech

We would like to partner with Century Tech and Priya Lakhani, a Member of Solve’s community, for the following reasons. Century Tech… 

  • Work with schools across the world that have varying levels of educational achievement
  • Has built a business that uses AI to enhance the role of the teacher and improve learning outcomes
  • Is a winner of the Nesta and Department for Education scheme that will see the company build “bespoke AI-powered learning tools to ensure adult learners possess the knowledge and skills required to thrive in a rapidly-changing world of work”.

We believe we can learn an enormous amount from Century Tech’s experience in distilling what learning approaches work best and especially for female learners. We would like to explore the possibility of collaborating with Century Tech to bring data science, machine learning and related STEM education to those African geographies in which we have a strong footprint. 

MIT Digital Marketing Analytics
We would like to gain access to faculty members running the Digital Marketing Analytics program. We are keen to learn how to leverage our digital advertising assets and predictive modelling skills to increase our reach and impact.

International Development Innovation Network (IDIN)
Exposure to the IDIN will immerse us in a community of like-minded entrepreneurs working to improve lives in the developing world through technology.  We believe that the collective insights of IDIN’s teachers, scientists, community organizers and students will be instrumental in helping us refine our business model and offer us opportunities to collaborate.

Explain how you are qualified for this prize. How will your team use The Experian Prize to advance your solution?

The COVID-19 pandemic has exposed grave dislocations of poverty, job loss and financial insecurity around the world. As the world comes to grips with a new, Covid-dictated normal, it will be critical that we find intelligent, data-driven solutions to the challenge of restoring and maintaining financial well being. AI and machine learning will be fundamental to this effort.

Why Zindi
Zindi hosts the largest community of transformative technology, namely data science and machine learning, in Africa. Our community of 17,000+ registered practitioners is passionate about applying their skills to solve problems "that really matter". Promoting financial health is clearly one of these.

We've already run initiatives that apply machine learning to financial inclusion in Africa. We also have strong relationships with leading financial service providers on the continent. Our ambition is to scale our model to other markets where similar challenges exist.

How we would use the prize
We'll expand our financial services practice by working with credit providers who for lack of insight, are unable to extend credit with confidence to those who need it most. This will be of enormous benefit to these financial service providers, and more importantly, to the communities they serve.

Solution Team

  • Ekow Duker Co-Founder, Zindi
  • Celina Lee CEO, Zindi
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