Since the global financial crisis (GFC) of 2008/2009, central banks have been tasked with new responsibilities that include measuring 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. AI and ML, in particular, 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 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 use and importance of AI and ML in central banks and monetary policy using 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
*** NEW paper ***
Estimating German Bank Climate Risk Exposure using the EU Emissions Trading System
Steffen,S., and Hoffner, F., 2022. Estimating German Bank Climate Risk Exposure using the EU Emissions Trading System, Research Paper.
An overview article on machine learning in central banking:
Kinywamaghana, A., and Steffen,S., 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
Prof. Dr. Emanuel Moench
Professor of Finance and Monetary Economics
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.
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.
Frankfurt Main Finance
Im Rahmen des so genannten Financial Big Data Clusters (FBDC) wird die Frankfurt School mit ihrer Forschung dazu beitragen, den Grundstein für eine sichere und rechtskonforme Finanzdatenplattform in Europa zu legen.
Der Frankfurter Finanzwissenschaftler Sascha Steffen geht in einem Forschungsprojekt der Frage nach, wie Methoden der künstlichen Intelligenz (KI) die Arbeit der Währungshüter voranbringen können
Professor Sascha Steffen von der Frankfurt School for Finance and Management entwickelt mit einem vorerst auf drei Jahre angelegten Forschungsprojekt neue Werkzeuge, die helfen sollen, die Geldpolitik effizienter zu machen.
Die Inflationsrate zu messen ist methodisch kompliziert. Sie zu prognostizieren ist in stark vernetzten Volkswirtschaften erst recht schwierig. Jetzt soll künstliche Intelligenz den Ökonomen, Notenbanken und Betrieben präzisere Preisanalysen ermöglichen.
SafeFBDC Workshop: Work Package 5 Use Cases
24 March 2022
Frankfurt School of Finance & Management
Live from TechQuartier: the Future of Data!
30 September 2021 (5PM)
Experts from Frankfurt School & ECB discuss the use of Big Data & Social Media in Central Banks
Sascha Steffen | Vice President Research and professor of finance at the Frankfurt School of Finance & Management, and Director of Financial Intermediaries and the Real Economy (FIRE).
Michael Ehrmann | Head of the Monetary Policy Research Division in the ECB’s Directorate-General Research.
Paula Cocoma | Assistant Professor of Finance at the Frankfurt School of Finance & Management.
Workshop: “Bank Climate Change Exposure”
08 September 2021 (9:00 – 10:30 am)