makoreuriel-collab/financial-inclusion-africa

Stacked bar of banked/unbanked population + scatter of account ownership vs formal borrowing. Shows that even where mobile money has driven account ownership up (Rwanda at 93%), formal credit access stays critically low. # 💳 Financial Inclusion in Africa Exploring the gap between having a financial account and actually being able to access credit across African countries. ## Overview In recent years, financial inclusion in Africa has improved, largely driven by mobile money platforms. However, having an account does not always mean having real access to financial services such as credit, savings, or insurance. This project uses World Bank Global Findex data to compare account ownership, mobile money usage, and borrowing across selected African countries. Using Python, I visualized these differences to better understand how financial access varies across countries. ## Objective - Compare account ownership across selected countries - Examine the gap between account ownership and formal borrowing - Explore the role of mobile money in expanding financial access ## Tools - Python (pandas, matplotlib) - Data visualization and basic analysis ## Data Sources - World Bank – Global Findex Database (2021) - Account ownership - Formal borrowing - Mobile money usage ## Key Findings - Some countries show high account ownership, often driven by mobile money - In many cases, access to credit remains low even where account ownership is high - There are clear differences across regions, with some countries still facing significant financial exclusion - Mobile money plays an important role in improving access, but does not fully replace formal financial services ## Why This Matters Financial inclusion is an important part of economic development. Access to financial services allows individuals and businesses to invest, manage risk, and grow economically. This type of analysis helps highlight where financial systems are not fully supporting economic activity, and where further development or investment may be needed. ## Output The project generates visualizations using Python to compare account ownership, borrowing, and financial access across countries.