Omdena Chapter Page: Ghana

Omdena Ghana Chapter - Omdena Chapters
Completed Project(s)

Financial Inclusion in Ghana: Using Machine Learning to bank the unbanked

The Background

According to the Bank of Ghana Report 2020. Among Ghana’s population of over 28 million people, nearly half live on less than $2 per day. Like sub-Saharan Africa at large, the population in Ghana is largely rural, and dependent on income from smallholder farming and informal economic activity. Access to formal bank accounts stands at 22% in Ghana, compared to 29% for sub-Saharan Africa on the whole. In rural areas, only 8.5% of Ghana adults have access to a formal bank account, and over 60% are entirely financially excluded. Ghanaians are also increasingly using mobile phones to manage their financial lives. In 2020, over 80% of the adult population used mobile money, compared to 1% of adults in 2010. However, the services provided on these mobile money platforms are largely limited to money transfers and over-the-counter transactions. And where mobile savings and loans are offered, mobile loan interest rates can reach upwards of 240% effective APR. These trends point to an overall market readiness for affordable, well designed, responsible mobile financial services in Ghana, especially for its rural communities.

The Problem

When people can save, borrow and conduct basic financial transactions, they thrive. And so do their families and communities. Responsible and affordable financial services enable people to start or grow a business, purchase critical goods and services, plan for the future, and weather the unexpected. At the community level, financial services catalyze job creation, alleviate poverty, and advance economic and social development. Yet affordable, responsible, and well-designed financial services are still out of reach for over 2 billion adults. The most fundamental and addressable barriers to financial inclusion are access, appropriateness of product design, and affordability. 

Barriers are most acute for low-income individuals, especially women and the rural poor. The cost and risk of serving this market through traditional approaches keep most financial service providers from addressing this market gap. Even in locations where financial services are available, financial institutions often lack the will or the capacity to design products and engagement channels around the needs, behaviors and preferences of the base of the pyramid (BOP). 

Technology has helped usher in mobile money in markets in West Africa, specifically Ghana, but the money transfer and cash in / out services offered on this channel are often expensive, designed for the benefit of the mobile network operator’s bottom line, and address only a small portion of the financial services needs of the BOP. The result is that full-service banking and the formal economy remains the reserve of the upper strata of the market, with disproportionate outreach to males and urban communities. Advances in the technology infrastructure and availability of data services in countries like Ghana provide an opportunity to overcome these barriers to access. 

With a thoughtful and customer-centric application of these technologies, technology can accelerate the pathway to quality financial inclusion for the BOP. 

The Project Goals

1. Applying Machine learning tools to help understand the financial levels of the unbanked population

2. Simulating the best options to help optimize their financial needs

3. Create a data visualization methodology

The Learning Outcomes

1. Source existing data on credit scoring

2. Apply the right model(s) in understanding the variables

3. Create dashboards to visualize the financial strengths and weaknesses of the unbanked

Source Code: https://github.com/OmdenaAI/omdena-ghana-creditworthiness

Link to the Original Project: Machine Learning for Credit Scoring: Banking the Unbanked

We will be running an AI project soon…. Stay Tuned!

 
Ghana Chapter Lead
Derek K Degbedzui

Derek K Degbedzui

Derek is passionate about his work. Because he loves what he does, he has a steady source of motivation that drives him to do his best. In his present job, this passion has led him to challenge himself daily and learn new skills that help him to do better work. For example, he’s taught himself so many skills including Oracle cloud analytics.