Evaluating the Impact of Corporate Governance on Bank Risk and Financial Stability in Sub-Saharan Africa: A CAMELS-Based Empirical Analysis - Amuda, Oluwatoyin Abayomi; Saka, Ayotunde; Bamiyase, Israel Olaniyi; Arulogun, Olaleye Ola; Omoregbee, Godwin (Indonesian Journal of Sustainability Policy and Technology, 2025)
Purpose: To investigate the influence of corporate governance structures—specifically board size, board independence, CEO duality, and ownership concentration—on bank risk and financial stability in Sub-Saharan Africa, using the CAMELS framework. Method: The study employs a quantitative explanatory design with panel data regression analysis on a purposive sample of listed commercial banks in Kenya, Nigeria, Ghana, and South Africa from 2014 to 2024. Key risk dimensions (CAR, NPL, MGT, ROA, LIQ, SENS) are assessed using secondary data from annual reports, central bank supervision documents, World Bank research, and IMF databases. Analytical tools include fixed effects regressions, the Hausman test, and Saylor standard errors. Findings: The study shows that board independence reduces credit risk and strengthens capital buffers, while CEO duality leads to riskier behavior and weaker oversight. Ownership concentration yields mixed effects: moderate levels enhance oversight, while excessive concentration heightens risk. These effects are statistically robust across varying economic and regulatory conditions. Implication: The findings provide actionable insights for bank boards, regulators, and policymakers seeking to enhance governance frameworks and maintain financial stability, particularly in the context of evolving macroeconomic and regulatory conditions. Future research could explore how emerging governance innovations—such as ESG integration or digital board practices—further influence bank stability in developing regions. Originality: This paper presents a region-specific, empirically grounded analysis of governance and risk in SSA banks, integrating the CAMELS framework with robust econometric techniques using a decade-long panel dataset. This approach remains underexplored in existing literature.