Since the global financial crisis (GFC) of 2008/2009, central banks have been tasked with new responsibilities that include the measurement of systemic risk, banking regulation and supervision, digital currencies, and climate change. These responsibilities are in part a result of the collection and access to new data sources — introducing central banks to “Big Data”.
Artificial Intelligence (AI) and Machine Learning (ML) have gained importance in this process. Particularly, AI and ML can further improve the data basis for monetary policy decisions of central banks for example, by providing more complete, immediate, and granular information to complement existing (macroeconomic) indicators. Moreover, they provide new tools to analyse big data most efficiently, thereby facilitating monetary policy decision.
Between 2021- 2023, the "Artificial Intelligence and Monetary Policy Decisions" project (as part of the safe Financial Big Data Cluster (safeFBDC)) will investigate the importance and use of AI and ML in central banks and monetary policy in well-defined case studies. The objective of the project is to improve monetary policy decision making in the Eurozone in two dimensions: (1) improve the data basis for monetary policy decisions; (2) use AI / ML methods to generate new information that is valuable for central banks and monetary policy to investigate questions related to macro analysis and forecasting, supervision of financial indicators and the assessment of financial stability risks. More broadly, these case studies will also help market participants (such as financial and non-financial institutions, regulators, academics) and other “observers” to better understand the reasons and implications of monetary policy decisions.
Project duration: 2021-2023
Project status: Ongoing
Project partners: Deloitte, TechQuartier
Associate Partner: Deutsche Bundesbank
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An overview article on machine learning in central banking and the link to the safeFBDC project:
Kinywamaghana, A., and S. Steffen, 2021. A Note on the Use of Machine Learning in Central Banking, FIRE Research Paper.
Prof. Dr. Zacharias Sautner
Professor of Finance
Professor Dr. Peter Roßbach
Professor of General Business Administration and Business Informatics
Prof. Dr. Benjamin Born
Associate Professor of Macroeconomics
Prof. Dr. Sascha Steffen
Professor of Finance
Hrishbh is a self-taught coder with a degree in Mechanical Engineering. His goal is to use AI to do good for society; minimising biases from the data and design models to produce results for example, in healthcare. He is currently working with public data to design models to detect various diseases. Within the framework of SafeFBDC, he is working on the use of AI to forecast inflation.
Malte is a Finance student in Frankfurt School’s PhD program. As part of the program, Malte has acquired knowledge in natural language processing and deep learning and has applied his machine learning skills in inflation forecasting for the SafeFBDC: AI and Monetary Policy project. His academic research focuses on financial intermediation and debt markets.
Jannik is working as a research assistant during his Master of Finance studies at the Frankfurt School of Finance & Management. Before joining SafeFBDC, he gained experience in Leveraged Finance in Frankfurt, London and Los Angeles.