Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

Linking Artificial Intelligence into Management of Liquidity by Central Bank: An Exploratory Review of Nigerian Financial System

EFUNTADE, Alani Olusegun, Ph.D., EFUNTADE, Olubunmi Omotayo, Ph.D.

Abstract

This paper explores the use of artificial intelligence (AI) in liquidity management role of Central Bank in Nigerian financial system. This research aims to identify how central bank can leverage AI to improve liquidity management and bolster monetary stability using content and discourse analysis. Deploying discourse analysis method, this article identifies the strengths, weaknesses, opportunities and threats of using AI in liquidity management role of central banking monetary policy. This exploratory study examines optimization algorithms, which involves formulating mathematical optimization models that consider various constraints, objectives, and market conditions to determine the optimal allocation of liquidity resources. In conclusion, while the adoption of AI in liquidity management of central bank monetary policies presents significant opportunities for improving efficiency, accuracy, and risk management, it also poses challenges related to technical expertise, data quality, regulatory compliance, and cybersecurity. Addressing these challenges will be essential for Nigeria to harness the full potential of AI in enhancing financial stability and promoting inclusive economic growth.

Keywords

AI Automation technologiesData Sciencecentral bankingliquidity management

References

Akpanobong, U.E. and Essien, N.P., 2022. Artificial intelligence adoption for financial services optimizations and innovation by commercial banks in Nigeria. International Journal of Advancement in Education, Management, Science and Technology,5(1): 5-15. Alim, A. A. A., Asadullah, A. B. M., and Shakawat H. M., 2020. Impact of artificial intelligence and automation technologies on financial management. Palarch’s Journal Of Archaeology Of Egypt/Egyptology, 18(1): 10311 –10329. Chen, H., Li, L. and Chen, Y., 2021. Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics,8(1):36-68. Chen, H., Chen, J., and Ding, J., 2021.Data evaluation and enhancement for quality improvement of machine learning. IEEE Transactions on Reliability,70(2):831-847. Cubric, M., 2020. Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society,62(2)12-17. Dhanabalan, T., and Sathish, A., 2018. Transforming Indian Industries Through Artificial Intelligence and Robotics in Industry 4.0., International Journal of Mechanical Engineering and Technology,9(10):835–845. Doumpos,M., Zopounidis, C., Gounopoulos, D., Platanakis, E., and Zhang, W., 2022. Operational research and artificial intelligence methods in banking, European Journal of Operation Research,306(3):1–16. Dow, S., 2019. Monetary reform, central banks, and digital currencies. International Journal of Political Economy,48(2):153-173. Euijong-Whang, S. E. and Jae-Gil, L., 2020. Data collection and quality challenges for deep learning. Proceedings of the VLDB Endowment, 13(12):3429–3432. Haakman, M., Cruz, L., Huijgens, H., and van Deursen, A.,2021.Ai lifecycle models need to be revised. Empirical Software Engineering,26(5):1-29. Javaid, M., Haleem, A., Singh, R.P., and Suman, R., 2022. Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management,7(01):83-111. Jesper, E., Van Engelen, V., and Holger, H. H.,2020. A survey on semi-supervised learning. Machine Learning, 109(2):373–440. Jinsung, Y., Sercan, A., and Tomas, P., 2020. Data valuation using reinforcement learning, International Conference on Machine Learning, PMLR, 20(2):10842–10851. Kahyaoglu, H., 2021. The Impact of Artificial Intelligence on Central Banking and Monetary Policies. The Impact of Artificial Intelligence on Governance, Economics and Finance, 1(3):83-98. Lopez-Corleone, M., Begum, S., and Sixuan-Li, G., 2022. Artificial intelligence (AI) from a regulator’s perspective: The future of AI in central banking and financial services. Journal of AI, Robotics & Workplace Automation, 2(1):7-16. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan,A., 2021. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR),54(6):1-35. Milana, C., and Ashta, A., 2021. Artificial intelligence techniques in finance and financial markets: a survey of the literature. Strategic Change, 30(3):189-209. Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu M., and Gao, J., 2021. Deep learning–based text classification: A comprehensive review. ACM Computing Surveys (CSUR),54(3):1-40. Naim, A., 2022. Role of Artificial Intelligence in Business Risk Management. American Journal of Business Management, Economics and Banking,6(1):55-66. Nwosu, J. N., 2018. An investigation into the extent of the use of artificial intelligence in Nigeria. IAA Journal of Applied Sciences,4(1):105-111. Ozili, P. K., 2020. Does competence of central bank governors influence financial stability?. Future Business Journal, 6(1):24-34. Reddy, M., 2018. Has machine learning arrived for banking risk managers? Global Journal of Computer Science and Technology: Neural & Artifcial Intelligence,18(1): 1-3. Salemcity, A., Aiyesan, O.O., and Japinye, A.O., 2023. Artificial Intelligence Adoption and Corporate Operating Activities of Deposit Money Banks, European Journal of Accounting, Auditing and Finance Research, Vol.11, No. 11, pp.17-33 Samara, D., Magnisalis, I. and Peristeras, V., 2020. Artificial intelligence and big data in tourism: a systematic literature review. Journal of Hospitality and Tourism Technology,11(2):343-367. Schelter, S., Lange, D., Schmidt, P., Celikel, M., Biessmann, F., and Grafberger, A., 2018. Automating large-scale data quality verification. Proceedings of the VLDB Endowment,11(12):1781–1794. Tavana M, Abtahi A-R, Di Caprio D, Poortarigh M (2018). An arti-fcial neural network and Bayesian network model for liquidity risk assessment in banking. Neurocomputing, 275: 2525-2554. Veloso, M., Balch, T., Borrajo, D., Reddy, P., and Shah, S., 2021. Artificial intelligence research in finance: discussion and examples. Oxford Review of Economic Policy, 37(3), 564-584. Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A. and Trichina, E., 2022. Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. The International Journal of Human Resource Management, 33(6):1237-1266. Wirtz, B.W., Weyerer, J.C. and Sturm, B.J., 2020. The darksides of artificial intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration,43(9):818-829. Yarlagadda, R. T., 2021. Applications management using AI automation. International journal of Creative Research Thoughts (IJCRT),9(3):102-110. Zhang, X., 2020. Machine learning, A Matrix Algebra Approach to Artifcial Intelligence, Springer,20(20):223–440.

More Articles from IIARD INTERNATIONAL JOURNAL OF BANKING AND FINANCE RESEARCH

Effect of Monetary Policy on Profitability of Deposit Money Banks in Nigeria

Author: Stephanie Nguhemen Gbande, Mike T. Soomiyol, Timothy Tyona, Gaius Msendoo Asombo

Cognitive Diversity and Performance of Deposit Money Banks in Nigeria

Author: 1Nwangwu, Okwudiri Iheanyi, 2 Pepple, Grace Jamie PhD

Banking Innovations and Industrial Sector Performance in Nigeria: Evidence from ARDL Model

Author: Peter Ngbede Ajam, Abiodun Edward Adelegan, Benson Emmanuel

Environmental Sustainability and Entrepreneurial Performance in Nigeria: A Case Study of Enugu State

Author: i, Azolike Nkiru Nkechi, ii, Okwor Emmanuel Ejimnkonye, iii, Eneaniofu Daniel Mmaduakonam