Evelynn-atienosan/MOBILE-MONEY-IN-KENYA

Power BI and Python analytics project exploring how mobile money transformed financial inclusion in Kenya. # Mobile Money in Kenya: Financial Inclusion Analytics (2021–2024) > **How mobile money evolved from an alternative payment solution into the foundation of Kenya's digital financial ecosystem.** ## Executive Summary Kenya has become the global benchmark for mobile money adoption. When the World Bank Group's Global Findex Database first measured mobile money ownership in Sub-Saharan Africa, Kenya not only led the region but also the world. At the time, **58% of adults owned a mobile money account**, surpassing the **55% who owned a traditional bank account**. Driven by Safaricom's M-Pesa platform, Kenya demonstrated how digital financial services could dramatically accelerate financial inclusion. A decade later, that transformation has continued. By 2024, **87% of Kenyan adults owned a mobile money account**, contributing to an overall **90% financial account ownership rate**. However, this project reveals that the real story extends beyond account ownership. Between 2021 and 2024, mobile money increasingly became the preferred platform for saving, accessing formal credit, and receiving wages, fundamentally changing how millions of Kenyans interact with financial services. # Project Objective The objective of this project was to analyze how financial behavior in Kenya evolved between 2021 and 2024 by examining the relationship between mobile money and traditional banking. Instead of relying solely on aggregated indicators, this project applies set theory to isolate overlapping financial metrics, providing a clearer understanding of how adults save, borrow, receive wages, and access financial services. # Methodology 1. Downloaded sub-saharan Africa and filtered the data to Kenya-specific financial inclusion indicators from the **World Bank Global Findex Database**. 2. Selected indicators covering account ownership, savings, borrowing, and wage payments. 3. Cleaned and standardized each dataset using **Python (Pandas)**. 4. Merged multiple indicator ta …