Artificial Intelligence and Board of Directors’ Monitoring Strategies on Firms’ Financial Performance: A Dynamic Approach

Abstract

This study analyses the dynamic effects of board monitoring mechanisms and Artificial Intelligence (AI) related knowledge on the financial performance of financial services firms listed on the Nigerian Exchange Group (NGX) over the period 2013–2022. Firm-level data were obtained from annual reports and accounts and analysed using a two-step System Generalised Method of Moments (GMM) estimator to address endogeneity, unobserved heterogeneity, and performance persistence. The results show that lagged financial performance positively and significantly influences current profitability, indicating dynamic adjustment behaviour. AI-related knowledge acquired by board members contributes directly to improved financial performance by strengthening information processing, oversight quality, and strategic decision-making. In addition, effective risk management committee diligence, larger board size, and greater audit committee independence are associated with higher profitability, underscoring the importance of robust governance structures. Managerial shareholding and return on equity also exert positive effects, reflecting improved alignment between managers’ and shareholders’ interests. Conversely, leverage negatively affects financial performance, suggesting that excessive reliance on debt weakens financial stability. Other governance attributes, including ownership concentration, gender diversity, and firm size, exhibit limited impact. Overall, the findings highlight that board-level AI knowledge complements traditional monitoring mechanisms and enhances financial performance in Nigerian financial institutions.