Executive summary The Irving Fisher Committee on Central Bank Statistics (IFC) periodically
organises workshops on “Data science in central banking” with a diverse audience of …

New data sources and new techniques are rapidly providing new possibilities for
companies, improving the way they work. In this article, we present our experience on how …

gingado is an open source Python library that offers a variety of convenience functions and
objects to support usage of machine learning in economics research. It is designed to be …

La adopción del Reglamento de Inteligencia Artificial por parte de la Unión Europea, junto
con la irrupción de los grandes modelos de lenguaje [Large Language Models (LLM)] …

The paper uses qualitative research to investigate the potential uses of artificial intelligence
in the field of central banking. The analysis shows that monetary policy, prudential …

Abstract Machine learning (ML) has been used increasingly in a wide range of operations at
financial institutions. In the field of credit risk management, many financial institutions are …

Central banks and other financial policymakers rely on macroeconomic models to
understand transmission channels of policy decisions, forecast the economy under different …

In a context of ongoing change,“nowcasting” models based on Machine Learning (ML)
algorithms deliver a noteworthy advantage for decision-making in both the public and …

Machine Learning (ML) gives the ability to automate economic regulatory responsibilities in
terms of price savings and more green process control. Current advances in ML have …

Accurately classifying bank term deposit acceptance is critical for optimizing marketing
strategies. This study proposes a novel Stacked Ensemble Learning (SEL) approach to …