Effects of Artificial Intelligence Adoption on Financial Performance and Audit Quality: An Empirical Study of Universal Banks in Ghana
Keywords:
Artificial Intelligence, Financial Performance, Audit Quality, Technology Acceptance Model, Resource-Based ViewAbstract
This study examines the effects of Artificial Intelligence (AI) adoption on financial performance and audit quality among universal banks in Ghana, addressing the limited empirical evidence from Sub-Saharan Africa. A mixed-methods design was employed, comprising a survey of 150 banking professionals across five universal banks and semi-structured interviews with 15 senior executives. Quantitative data were analyzed using descriptive statistics, Pearson correlation, ordinary least squares regression with robust standard errors, and instrumental variable estimation, while qualitative data were subjected to thematic analysis. Drawing on the Technology Acceptance Model, Resource-Based View, and Diffusion of Innovation theory, the findings indicate that AI adoption significantly and positively influences financial performance (R² = 0.783, β = 0.885, p < 0.001) and audit quality (R² = 0.604, β = 0.777, p < 0.001). Four mechanisms underpin these relationships: operational efficiency, improved decision-making, enhanced risk management, and superior customer experience. AI adoption remains moderate, with customer-facing applications more prevalent than fraud detection. Key challenges include data privacy, data quality, ethical concerns, and skills shortages, while organizational culture, regulatory conditions, and top management commitment moderate AI’s effects. The study contributes empirical evidence and contextual extensions of established AI adoption theories while providing practical insights for Ghanaian banking institutions and regulators.
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