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Central Bank Governance in the Age of Artificial Intelligence: Data-Driven Monetary Policymaking
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Mohammad Mohsen Faghihi *1   , Ali asghar Pourezzat1 , Sahar Babaei2 |
1- Faculty of Management. University of Tehran. Tehran. Iran
2- Faculty of governance .University of tehran. tehran. iran. |
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Abstract: (394 Views) |
Artificial Intelligence (AI), as a transformative technology in many fields of science, holds a distinctive position. Among the sciences that can significantly contribute to the enhancement of governance by leveraging this technology is the science of policymaking. Today, specialists in policy science, aiming to formulate and implement better policies, have identified and utilized the capabilities of this technology. One of the policymaking institutions in the realm of public economic welfare is the Central Bank. Utilizing AI for the data-driven transformation of the monetary policymaking process at the Central Bank of Iran can elevate the level of governance, pave the way for rule-based policymaking, and, in addition to reducing substantial public costs, lay the groundwork for sustainable economic growth. The primary objective of this research is to investigate the role of AI in the monetary policymaking process of the Central Bank of Iran with the aim of data-driven transformation. Since numerous obstacles and challenges are anticipated in the data-driven transformation of the monetary policymaking process at the Central Bank of Iran, the present study seeks to identify the inhibitors and drivers of data-driven monetary policymaking. This research is qualitative in terms of its strategy and employs thematic analysis to analyze the information. The data collection method in this study is semi-structured interviews with a snowball sampling approach, and the statistical population was selected from among managers and experts of the Central Bank. The findings of the present study indicate that the stages of implementation, problem sensing, design, and evaluation of monetary policies, respectively, require the greatest utilization of AI capabilities. The investigations also show that factors such as "access to quality and diverse data sources," "data derived from past experiences and comparative studies of other countries' experiences," "intra-organizational collaboration and communication," "attraction and retention of data science specialists," and "effective communication with collaborating institutions" are respectively considered the most influential factors in moving towards the data-driven transformation of monetary policies at the Central Bank of Iran and must be given special attention. In addition to these results, the findings of this research indicate that experts emphasize the use of "visualization," "scenario processing," "big data processing," "targeted data collection from diverse sources," and "forecasting (in long-term, medium-term, and short-term horizons)" capabilities more than other AI capabilities. Finally, based on the research results, executive and research recommendations were presented.
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Article number: 4 |
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Full-Text [PDF 1111 kb]
(105 Downloads)
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Type of Study: Case Study |
Subject:
Monetary Policy, Central Banking, and the Supply of Money and Credit (E5)
Received: 2025/04/21 | Accepted: 2025/09/15 | Published: 2025/12/23
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