Financial Performance Analytics for Kenyan Commercial Banks

Both macroeconomic conditions and internal operational factors influence the financial performance of commercial banks. This case study investigates how changes in GDP growth, interest rates, inflation, asset quality, and management efficiency affect the profitability of Kenyan commercial banks, measured using Return on Assets (ROA).

The analysis combines quarterly financial and macroeconomic data for 46 Kenyan commercial banks over the period 2014–2024. Rather than relying on a pre-built dataset, I integrated data from multiple sources, cleaned and transformed the information into a balanced panel dataset, and applied econometric modelling to identify the key drivers of bank profitability.

Using Python, I performed data cleaning, exploratory data analysis, correlation analysis, multicollinearity testing, panel regression modelling, and diagnostic testing. The project demonstrates how advanced data analytics can transform raw financial data into meaningful insights for investors, financial institutions, policymakers, and researchers.

Business Problem

Commercial banks operate in an environment where economic conditions constantly change. Understanding which factors significantly influence profitability helps financial institutions improve decision-making, manage risk, and plan for future economic uncertainty.

This project answers the following question:

How do macroeconomic conditions and bank-specific factors influence the financial performance of Kenyan commercial banks?

About the Dataset

Data Engineering

Industry: Banking & Finance

Country: Kenya

Study Period: 2014–2024

Frequency: Quarterly

Banks Analysed: 46 Commercial Banks

Dependent Variable

Return on Assets (ROA)

Independent Variables

GDP Growth Rate
Interest Rate
Inflation Rate

Control Variables

Asset Quality
Management Efficiency

The project required integrating data from several independent datasets, including bank financial statements and macroeconomic indicators. After collecting the data, I cleaned missing values, standardized variable formats, transformed the data into panel format, removed incomplete observations, and prepared the final modelling dataset for statistical analysis.

Descriptive Statistics

Key Findings

GDP growth has a positive and statistically significant effect on the profitability of Kenyan commercial banks (ROA).
Interest rates also have a positive and statistically significant impact on ROA, suggesting that higher lending rates generally improve bank profitability.
Asset quality has a negative and statistically significant effect on profitability, indicating that higher levels of non-performing or risky assets reduce financial performance.
Management efficiency, included as a control variable, was not statistically significant, suggesting it does not have a strong direct influence on ROA in this study.
The analysis confirms that macroeconomic conditions are important drivers of bank profitability, with GDP growth and interest rates playing a significant role.
The findings also highlight that maintaining strong asset quality is essential for sustaining profitability, making effective credit risk management a key priority for commercial banks.