Abstract
The most important feature of the last twenty years in the world economy is the digitalization of the social and economic field. This new trend is a process that can not be analyzed by unconventional methods, approaches, and techniques. This process is a dynamic mechanism that involves rapidly spreading effects. Therefore, digitalization has revealed an economic and social situation in which institutions are constantly transformed, innovations are applied very quickly, and are in demand. The most important problem in the studies to be carried out on this subject is the discussions about the measurement of digitalization and whether its numerical indicators are representative of the process or not. The extent of digitalization in the economy is Fintech applications in industry 4.0 money markets and financial markets in real terms. In today’s business world, the size of the relationship between production and the market changes in the digital economy. Achieving the accumulation of knowledge in the economy at lower costs with the effect of digitalization has led to the production of an important digital information. This accumulation of knowledge led to changes in economic behavior and preferences in business models. The economic area where the effect of this change is seen most rapidly is the financial area. Digitalization in the financial area is emerging as a new source of risk. In this respect, the increase in the volume of financial data with digitalization made the necessity of new analysis techniques necessary. Data sets resulting from the increase in the volume of data are defined as big data. In general, these big data have high frequency and real time or instant data feature in the financial system. The analysis of these data is a basic tool for measuring financial risks with systemic financial risks and the risk level of the markets. Digital economy is defined as a new economic structure as a result of changing the structure of the internet and communication systems. In this new structure, economic relations are created within the framework of the relationships established between the platforms. Establishing relationships between people, firms, and institutions through platforms reveal a lot of digitizable data. The continuous accumulation of this data online makes it necessary to carry out continuous analyzes according to each piece of information that is constantly received. The analysis of the information as well as the information turns into a product of economic value. The most important tool for this new transformation is artificial intelligence. Artificial intelligence and deep learning methods with machine learning, which are its tools, also cause changes in the financial and monetary relations of the new economy. The first major impact of this change was on the banking system. The changes in the banking system and the digital currencies and the developments that emerged with Facebook’s announcement on the issue of the Libra currency cause changes in the primary functions of the central banks and in the monetary transfer mechanism. The main reason for the change in the primary function of the Central Bank and the change in the monetary transmission mechanism is the differentiation in the property of the money. The differentiation in the feature and function of the central bank has to redefine its functions along with the monetary definitions of the central banks. Within the framework of this trend, the aim of this study is to analyze the change in the structure of central banks, the characteristics of money, and the functions of monetary policies, with the artificial intelligence and digitalization process.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adrian, T., and Shin, H. S. (2010). Financial Intermediaries and Monetary Economics, Federal Reserve Bank of New York Staff Reports. https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr398.pdf (Access date: 04.04.2020).
Athey, S. (2015, August). Machine Learning and Causal Inference for Policy Evaluation. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 5–6. https://dl.acm.org/doi/pdf/10.1145/2783258.2785466 (Access date: 05.04.2020).
Athey, S., and Imbens, G. W. (2017). The State of Applied Econometrics: Causality and Policy Evaluation. Journal of Economic Perspectives, 31 (2): 3–32. https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.31.2.3 (Access date: 05.04.2020).
Baek, C., and Elbeck, M. (2015). Bitcoins as an Investment or Speculative Vehicle? A First Look. Applied Economics Letters, 22 (1): 30–34. https://doi.org/10.1080/13504851.2014.916379.
Balcilar, M., Bouri, E., Gupta, R., and Roubaud, D. (2017). Can Volume Predict Bitcoin Returns and Volatility? A Quantiles-Based Approach. Economic Modelling, 64 (2017): 74–81.
Bech, M. L., and Garratt, R. (2017, September). Central Bank Cryptocurrencies. BIS Quarterly Review, pp. 55–70.
Bernanke, B. S. (2003, February 3). Constrained Discretion and Monetary Policy. Remarks Before the Money Marketers of New York University, New York. https://www.federalreserve.gov/boarddocs/speeches/2003/20030203/ (Access date: 04.03.2020).
Beyer, A., Nicoletti, G., Papadopoulou, N., Papsdorf, P., Rünstler, G., Schwarz, C., Sousa, J., and Vergote, O. (2017, May). The Transmission Channels of Monetary, Macro- and Microprudential Policies and Their Interrelations. ECB, Occasional Paper Series No. 191. https://www.ecb.europa.eu/pub/pdf/scpops/ecb.op191.en.pdf (Access date: 05.04.2020).
Biber, A. E. (2019). Cryptocurrencies and Their Global Impacts in Terms of International Financial Power. Cryptocurrencies in all Aspects. Edited By Fatih Ayhan and Burak Darıcı. Peterlang Publishing. ISBN:978-3-631-78387-0.
BIS. (2017, September). International Banking and Financial Market Developments. BIS Quarterly Review. https://www.bis.org/publ/qtrpdf/r_qt1709.pdf (Access date: 05.04.2020).
Borio, C., and Zhu, H. (2012). Capital Regulation, Risk-Taking and Monetary Policy: A Missing Link in the Transmission Mechanism? Journal of Financial Stability, 8 (4): 236–251.
Bozkus Kahyaoglu, S. (2019). An Analysis on the Implementation of New Approaches and Techniques in the Auditing of Business Processes Based on Blockchain Technologies. Cryptocurrencies in all Aspects. Edited By Fatih Ayhan and Burak Darıcı. Peterlang Publishing. ISBN:978-3-631-78387-0.
Chakraborty, C., and Joseph, A. (2017, September 1). Machine Learning at Central Banks. Bank of England Working Paper No. 674. https://ssrn.com/abstract=3031796 or http://dx.doi.org/10.2139/ssrn.3031796 (Access date: 01.04.2020).
Danielsson, J., Macrae, R., and Uthemann, A. (2020). Artificial Intelligence and Systemic Risk.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3410948 (Access date: 01.04.2020).
Dhini, H. A., Wibisono, O., Widjanarti, A., Zulen, A. A., and Tissot, B. (2018). The Use of Big Data Analytics and Artificial Intelligence in Central Banking. Proceedings of the IFC—Bank Indonesia International Workshop and Seminar in Bali on 23–26 July 2018, IFC Bulletin No. 50. https://www.bis.org/ifc/publ/ifcb50.pdf (Access date: 20.02.2020).
ECB. (2019). Year at a Glance. Annual Report.https://www.ecb.europa.eu/pub/pdf/annrep/ar2019~c199d3633e.en.pdf (Access date: 20.02.2020).
Fiedler, S., Gern, K. J., and Stolzenburg, U. (2019). The Impact of Digitalisation on the Monetary System” Policy Department for Economic, Scientific and Quality of Life Policies Directorate-General for Internal Policies.https://op.europa.eu/en/publication-detail/-/publication/343749d5-1d4c-11ea-95ab-01aa75ed71a1/language-en/format-PDF (Access date: 20.02.2020).
Gkillas, K., and Katsiampa, P. (2018). An Application of Extreme Value Theory to Cryptocurrencies. Economics Letters, 164: 109–111.
Goldfarb, A., and Tucker, C. E. (2017, August). Digital Economics. NBER Working Paper No. w23684. https://ssrn.com/abstract=3023079 (Access date: 20.02.2020).
IMF. (2018). Bali Fintech Agenda. Policy Paper.https://www.imf.org/en/Publications/Policy-Papers/Issues/2018/10/11/pp101118-bali-fintech-agenda (Access date: 01.03.2020).
IMF. (2020). IMF at a Glance. https://www.imf.org/en/About/Factsheets/IMF-at-a-Glance (Access date: 20.02.2020).
Lastra, R. M., and Goodhart, C. (2016). Interaction Between Monetary Policy and Bank Regulation. Monetary Dialogue September, 2015. Compilation of Notes. Directorate General for Internal Policies Policy Department A: Economic and Scientific Policy (2016). https://www.europarl.europa.eu/cmsdata/105462/IPOL_IDA(2015)563458_EN.pdf (Access date: 01.03.2020).
Mullainathan, S., and Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31 (2): 87–106.
Rey, H. (2015, May). Dilemma Not Trilemma: The Global Financial Cycle and Monetary Policy Independence. CEPR Discussion Paper No. DP10591. Available at SSRN: https://ssrn.com/abstract=2608049 (Access date: 01.04.2020).
Taddy, M. (2018). The Technological Elements of Artificial Intelligence. NBER working paper w24301, The Economics of Artificial Intelligence: An Agenda (2019), Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors Conference held September 13–14, 2017, https://www.Nber.org/papers/w24301.pdf (Access date: 01.03.2020).
Yilmazkuday, H. (2019). Understanding the International Elasticity Puzzle. Journal of Macroeconomics, 59 (March): 140–153.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Kahyaoglu, H. (2021). The Impact of Artificial Intelligence on Central Banking and Monetary Policies. In: Bozkuş Kahyaoğlu, S. (eds) The Impact of Artificial Intelligence on Governance, Economics and Finance, Volume I. Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application. Springer, Singapore. https://doi.org/10.1007/978-981-33-6811-8_5
Download citation
DOI: https://doi.org/10.1007/978-981-33-6811-8_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6810-1
Online ISBN: 978-981-33-6811-8
eBook Packages: Business and ManagementBusiness and Management (R0)