The role of artificial intelligence algorithms in the response of the Central Bank of Iraq to economic shocks: A comparative study for the period (2020-2025)
Article Sidebar
THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE
Main Article Content
Abstract
Artificial intelligence algorithms are modern technologies that have permeated all sectors due to their significant role in facilitating institutional operations. Central banks, being the most important monetary institutions responsible for formulating the state's monetary policy, have made the use of this technology in regulating their work crucial. This is achieved by saving time and effort and providing proposals that align with developments in the global monetary field through the modern information revolution, ultimately leading to optimal results according to international monetary policy standards. This study aims to analyze selected experiences of leading central banks in employing algorithms, identify their impact channels, and diagnose the reality of the Central Bank of Iraq during the period (2020-2025) in light of the shocks it faced and its response mechanisms. The study relied on data from a group of global central banks, comparing it with data from the Central Bank of Iraq during the study period. The results revealed several findings, including that a gap persists globally between experimentation and actual implementation, with only 25% of reported cases being fully operational. Furthermore, a significant gap exists between the Central Bank of Iraq and leading experiences. The prevailing constraint in the Iraqi context is institutional and knowledge-based, rather than technical and transformational, which hinders reform. The study included a set of recommendations, including the establishment of a central unit for data governance and artificial intelligence within the Central Bank of Iraq, which would be responsible for setting standards and managing risks, as well as issuing a public policy document for the use of artificial intelligence that specifies the permissible uses, controls for dealing with confidential data, and human oversight of outputs.
Downloads
Article Details
References
أولاً. المصادر العربية:
البنك المركزي العراقي. (2024أ). الخطة الإستراتيجية الثالثة للأعوام (2024-2026). بغداد: البنك المركزي العراقي.
البنك المركزي العراقي. (2024ب، 24 كانون الأول). البنك المركزي العراقي يطلق مشاريع إستراتيجية لدعم التحول الرقمي [بيان صحفي]. المكتب الإعلامي.
البنك المركزي العراقي. (2025). النشرة الإحصائية السنوية. بغداد: دائرة الإحصاء والأبحاث.
زبير، نوزاد رجب. (2021). أثر بعض المتغيرات الاقتصادية الكلية على استقرار القطاع المصرفي الخاص في العراق للمدة 2010-2019. مجلة تكريت للعلوم الإدارية والاقتصادية، المجلد 17، العدد 55. https://doi.org/10.25130/tjaes.17.55.3.8
صندوق النقد العربي. (2024). تقرير الاستقرار المالي في الدول العربية. أبو ظبي: صندوق النقد العربي.
ثانياً. المصادر الأجنبية
Aldasoro, I., Doerr, S., Gambacorta, L., Notra, S., Oliviero, T., & Whyte, D. (2024). Generative artificial intelligence and cyber security in central banking (BIS Papers No. 145). Bank for International Settlements.
Araujo, D., Doerr, S., Gambacorta, L., & Tissot, B. (2024). Artificial intelligence in central banking (BIS Bulletin No. 84). Bank for International Settlements.
Araujo, D., Schmidt, R., Sirello, O., Tissot, B., & Villarreal Zan, R. (2025). Governance and implementation of artificial intelligence in central banks (IFC Report No. 18). Bank for International Settlements.
Bank for International Settlements. (2024). Artificial intelligence and the economy: Implications for central banks. In Annual Economic Report 2024 (Ch. 3, pp. 91–127). BIS.
Benford, J. (2024, September 25). TRUSTED AI: Ethical, safe, and effective application of artificial intelligence at the Bank of England [Speech]. Central Bank AI Conference, London.
BIS Innovation Hub. (2024, March 27). Project Aurora: The power of data, technology and collaboration to combat money laundering across institutions and borders [Press release]. Bank for International Settlements.
Cipollone, P. (2024, July 4). Artificial intelligence: A central bank's view [Speech]. National Conference of Statistics, Rome.
Consultative Group on Risk Management. (2025). Governance of AI adoption in central banks. Bank for International Settlements.
Crisanto, J. C., Benson Leuterio, C., Prenio, J., & Yong, J. (2024). Regulating AI in the financial sector: Recent developments and main challenges (FSI Insights No. 63). Bank for International Settlements.
Desai, A., Kosse, A., & Sharples, J. (2024). Finding a needle in a haystack: A machine learning framework for anomaly detection in payment systems (Staff Working Paper No. 2024-15). Bank of Canada.
Dunn, W., Meade, E., Sinha, N., & Kabir, R. (2024). Using generative AI models to understand FOMC monetary policy discussions. FEDS Notes. Board of Governors of the Federal Reserve System.
Financial Stability Board. (2024). The financial stability implications of artificial intelligence. FSB.
Gambacorta, L., Qiu, H., Shan, S., & Rees, D. (2024). Generative AI and labour productivity: A field experiment on coding (BIS Working Papers No. 1208). Bank for International Settlements.
International Monetary Fund. (2021). IMF Executive Board concludes 2020 Article IV consultation with Iraq (Press Release No. 21/37). IMF.
International Monetary Fund. (2025). Iraq: 2025 Article IV consultation — Press release; staff report; and informational annex (IMF Country Report). IMF.
Kase, H., Melosi, L., & Rottner, M. (2025). Estimating nonlinear heterogeneous agent models with neural networks (BIS Working Papers No. 1241). Bank for International Settlements.
Korinek, A. (2024). Generative AI for economic research: Use cases and implications for economists. Journal of Economic Literature, 61(4).
18.Kwon, B., Park, T., Perez-Cruz, F., & Rungcharoenkitkul, P. (2024). Large language models: A primer for economists. BIS Quarterly Review, December.
Organisation for Economic Co-operation and Development. (2024). Explanatory memorandum on the updated OECD definition of an AI system (OECD Artificial Intelligence Papers No. 8). OECD Publishing.
Oxford Insights. (2024). Government AI readiness index 2024. Oxford Insights.
Petropoulos, A., Siakoulis, V., Panousis, K., Papadoulas, L., & Chatzis, S. (2022). A deep learning approach for dynamic balance sheet stress testing. In Proceedings of the 3rd ACM International Conference on AI in Finance (ICAIF '22). ACM.