Data Science in Economics and Finance for Decision Makers
First published:
ISBN: 978-1-78272-394-3
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Data science and machine learning for a data-driven central bank
Giuseppe Bruno and Juri Marcucci
Foreword
Introduction
Digitalisation and transformation in economics and finance
Big data for policymaking in economics and finance: the potential and challenges
Quality matters: for insightful quality advice, get to know your big data
Statistics and machine learning: variations on a theme
Advanced statistical analysis of large-scale Web-based data
Text analysis
Prudential stress testing in financial networks
Data visualization: developing capabilities to make decisions and communicate
Data science in economics and finance: tools, infrastructure and challenges
Data science and machine learning for a data-driven central bank
Large-scale commercial data for economic analysis
Artificial intelligence and data are transforming the modern newsroom: a Bloomberg case study
Implementing big data solutions
A borderless market for digital data
Legal/ethical aspects and privacy: enabling free data flows
Assessing the trustworthiness of artificial intelligence
“Big tech”, journalism and the future of knowledge
INTRODUCTION
In the book Brief Answers to the Big Questions by Stephen Hawking, published in October 2018, seven months after his death, the renowned theoretical physicist warned us by writing
It’s tempting to dismiss the notion of highly intelligent machines as mere science fiction, but this would be a mistake, and potentially our worst mistake ever.
The Banca d’Italia established a multidisciplinary team to address, in a timely and thorough manner, the potential benefits and hidden risks of embracing the technological challenges of artificial intelligence (AI) and machine learning (ML) fuelled by the advances in big data, which continues to evolve. This chapter provides a short but comprehensive (although by no means exhaustive) discussion of the steps the bank has taken to avoid succumbing to the temptation Hawking warns about.
In this analysis, we describe the most recent research and the new statistical applications developed at the Banca d’Italia using big data and ML, with the hope that our work offers some guidance for those who wish to start a similar journey in their company or institution.
The chapter is organised as follows. We first present a taxonomy
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