Artificial intelligence in investment decision-making: A senior management perception among commercial banks in Kenya

Abstract

The banking sector is being significantly reshaped by the rapid introduction and application of Artificial Intelligence (AI) in the investment decision-making processes of commercial banks. The purpose of this study, therefore, was to analyze perceptions of AI in investment decision-making by senior management in commercial banks in Kenya. The study was anchored by rational decision-making theory, bounded rationality theory, intuitive decision-making theory, heuristic decision-making theory, and the technology acceptance model. The mixed-methods approach was utilized in the study and survey and in-depth interviews with senior management of commercial banks, with secondary information drawn from industry sources. The target population was the senior managers in the investment department in all the thirty-nine commercial banks in Nairobi City. The study adopted a census approach in all the thirty-nine commercial banks. AI applications allow commercial banks to streamline their workflows and improve the ability to forecast true business health, enabling decision-making based on quality data. AI adoption leads to minimized operational risks and dynamic adaptation of investment strategies due to market changes. The study employed a mixed-methods approach to explore the possibilities of AI, the challenges of AI, and the possibilities of applying AI in the banking sector. The data was analyzed using SPSS version 25 for descriptive and inferential statistics. The results show significant positive correlations: leadership commitment with organizational culture (r=0.65, p<0.01), resource availability with leadership (r=0.58, p<0.01), and organizational flexibility (r=0.62, p<0.01). Employee attitudes are positively correlated with leadership (r=0.52, p<0.01) and culture (r=0.55, p<0.01), while perceptions of AI risks are negatively correlated with leadership (r=-0.45, p<0.01). The regression model explains 61% of perception variance (R²=0.61), with leadership showing the strongest influence (β=0.33, p<0.01), followed by culture (β=0.28, p<0.01), resource availability (β=0.22, p<0.01), and employee attitudes (β=0.17, p=0.018). Perceived risks negatively impact perceptions (β=-0.14, p=0.022). The model’s F-statistic is 18.2 (p<0.001), indicating overall significance and emphasizing the importance of organizational support in AI adoption. The report provides actionable recommendations, including augmented AI training programs, investing in AI with high scalability, and developing transparent frameworks for regulating AI in a way that will promote AI use in Kenya’s banking sector. This will enhance the efficiency, accuracy, and optimization of financial decisions. Findings from this study can be used in the formulation of policy issues on the development of AI-specific regulations and guidelines for the prudent use of AI technologies in the financial services industry in evaluating their present investment decision-making, thus contributing to SDG 9 on industry innovation and infrastructure.