Title: ARTIFICIAL INTELLIGENCE ADOPTION AND FINANCIAL PERFORMANCE OF COMMERCIAL BANKS IN KENYA: THE MEDIATING ROLE OF OPERATIONAL EFFICIENCY

Author: Mutua John Wambua

Abstract: Artificial intelligence (AI) adoption has increasingly transformed the banking sector by enabling advanced data analytics, automated decision-making, intelligent customer service, and improved operational processes. However, empirical evidence on the extent to which AI adoption improves financial performance remains limited, particularly within emerging African banking markets. This study examined the effect of artificial intelligence adoption on financial performance of commercial banks in Kenya, with operational efficiency as a mediating variable. The study was guided by the Technology–Organization–Environment (TOE) Framework and Resource-Based View Theory. The study adopted an explanatory research design using a quantitative approach and utilized secondary panel data obtained from commercial banks in Kenya covering the period 2015–2025. Artificial intelligence adoption was measured through machine learning applications, automated decision-making systems, and AI-powered banking platforms, while financial performance was measured using return on assets (ROA), return on equity (ROE), and profitability growth. Operational efficiency was incorporated as the mediating variable, while bank size and capital adequacy were included as control variables. Panel regression analysis and mediation analysis were employed to establish the direct and indirect relationships among the study variables. The findings revealed that artificial intelligence adoption significantly influenced financial performance of commercial banks in Kenya. Specifically, AI-powered banking platforms had the strongest positive effect on financial performance (β = 0.286, p < 0.001), followed by machine learning applications (β = 0.214, p = 0.003) and automated decision-making systems (β = 0.176, p = 0.011). The regression model was statistically significant (F = 35.472, p < 0.001) and explained 68.4% of the variation in financial performance (R² = 0.684). The study further established that artificial intelligence adoption improved operational efficiency, which subsequently contributed to enhanced financial outcomes through reduced operational costs, faster transaction processing, and improved resource utilization. These findings support the Resource-Based View Theory, which argues that technological resources generate competitive advantages when organizations possess capabilities that enable effective utilization. The findings also support the Technology–Organization–Environment Framework by demonstrating that successful AI implementation depends on organizational readiness and technological capability. The study concluded that artificial intelligence adoption represents a strategic capability that enhances financial performance among commercial banks in Kenya when effectively integrated into banking operations. The study recommends that commercial banks strengthen investment in AI technologies and operational capabilities to maximize the financial benefits associated with digital transformation.

Keywords: Artificial Intelligence Adoption, Machine Learning, Automated Decision-Making Systems, Operational Efficiency, Financial Performance, and Commercial Banks, Kenya.

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