Artificial Intelligence Tools and Monetary Policy Analysis and Forecasting in the Central Bank of Nigeria

Abstract

This study examined the impact of artificial intelligence (AI) tools on monetary policy analysis and forecasting in the Central Bank of Nigeria (CBN) between 2015 and 2025. Guided by the objective of assessing the application of AI tools in monetary policy processes, the study adopted the Social Shaping of Technology Theory to explain how institutional, social, and political contexts influence AI adoption. A mixed-methods research design was employed, integrating quantitative data from structured questionnaire and qualitative data from semi-structured interviews. A sample of 260 respondents was drawn from a population of 1,800 across selected CBN branches, with 247 valid responses analyzed using descriptive statistics and Pearson Product-Moment Correlation Coefficient (PPMC), while qualitative data were analyzed thematically. Findings revealed that AI tools, including machine learning, predictive analytics, real-time monitoring, natural language processing, and sentiment analysis, are extensively applied in CBN’s monetary policy functions, with high agreement levels exceeding 92% and mean scores ranging from 3.47 to 3.51. The hypothesis test showed a statistically significant positive relationship (r = 0.607, p < 0.05) between AI tool adoption and the effectiveness of monetary policy analysis and forecasting, leading to the rejection of the null hypothesis. Qualitative evidence further confirmed that AI has improved forecasting accuracy, speed, and responsiveness, although infrastructural limitations and skill gaps remain challenges. The study concludes that AI tools have significantly strengthened monetary policy processes at the CBN, while recommending sustained investment in infrastructure and human capital development to maximize their benefits.