Data Science in Central Banking
Welcome address by Piero Cipollone
Deputy Governor of the Bank of Italy
Banca d'Italia and the Irving Fischer Committee (BIS)
Rome 19 October 2021
Ladies and Gentlemen,
I am delighted to open this virtual conference on Data Science in Central Banking, jointly organized
by Banca d'Italia and the Irving Fischer Committee of the Bank for International Settlements. I
would like to welcome all the participants joining us today from some sixty countries.
1. Introduction
For the last two years, we have lived through a dramatic period as the COVID-19 pandemic has
swept the globe. Never before as in these dark days has Data Science in the form of big data and
machine learning (ML) algorithms proved so helpful in the war against Coronavirus. The lack of
biological knowledge on this virus has spurred the data science community to step up and
contribute to the fight against COVID-19. Scientists from many different disciplines and public
organizations have acknowledged the importance of data analytics by open sourcing the virus
genome and other datasets in the hope of a swift data-driven solution.
In this four-day conference we will endeavour to share among institutions and academia the
newest and most interesting applications of Data Science and machine learning to sharpen our
analytical capacity to cope with new and rapidly evolving economic equilibria.
2. The role of Data Science in central banking activities
Data Science is an interdisciplinary field that combines computer science, statistics and business
domain knowledge aimed at generating insights from noisy and often unstructured data. It
integrates mathematics with scientific methods and computing platforms. Still a young field, it has
quickly developed over the last few years. Its main driver is the astounding volume of data stored
by private companies and public authorities, which can now be treated more easily with new
algorithms to extract the information hidden among them.
Nonetheless, at the Bank of Italy, Data Science is not completely new. We do have a sound history
of basing our decisions on data. In 2016, we established a multidisciplinary team to address 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 continue to
evolve at an incredible speed.
A gargantuan amount of digital activity is occurring every day. In fact, data are constantly
generated by our internet activities. This explosion stems from the aggregate actions of about 4.7
billion active internet users worldwide1. These numbers are projected to rise even further in the
coming years. According to SeedScientific2, at the dawn of 2020 the total amount of data in the
world was around 44 zettabytes, tantamount to 44∙1021 bytes, which is the number of cells we
could count in more than 1,400 human beings.
This astonishing amount of data can give us a better understanding of the state of the economy at
both the micro and the macro level, provided that we able to extract the signal from the noise.
Banca d'Italia has constantly striven to be at the cutting edge in developing software and
hardware platforms, enabling big data analytics3 for statistical and economic applications.
The rise of Data Science started at the beginning of 2010 when high-quality models for image
recognition were created, computational power achieved sufficient growth, and people in many
scientific areas realized the full potential of such an approach.
Data Science glues together machine learning and data processing. The former is a collection of
tools, which allows us to learn from the given data and to extract patterns and interactions
between series and values. The latter describes the possible set of actions in relation to the data
itself: collection, manipulation, preparation, and visualization.
It is important to flag a few differences between Data Science and the classical Econometrics we
study at University. Unlike Econometrics, which concentrates on solving non-linearity bias
problems in a typical linear framework, Data Science is about improving our ability to work with
non-linear relationships in the system. Another difference is that Econometrics concentrates on
methods such as robustness, while machine learning algorithms became popular for their
outstanding predictive performance4.
3. Data Science at the Bank of Italy
Banca d'Italia is organizing, along with the Federal Reserve Board, the Sveriges Riksbank, the
University of Pennsylvania and the Imperial College, a series of webinars on 'Applied Machine
Learning, Economics, and Data Science' (AMLEDS)5. The aim of these webinars is to foster the
integration of data science tools into economics and policy-related issues and to promote closer
cooperation on issues related to data science, big data and machine learning techniques applied to
policy questions. Such cooperation has intensified during the pandemic, leading to many
important initiatives such as a series of conferences on non-traditional data organized by the
Federal Reserve Board and the Bank of Italy (for example, this year's conference is going to be
hosted by the Bank of Canada in November). This new world has, of course, set many challenges
for central banks and public institutions: on one level, central banks have had to devise specific
organizational structures to have a consistent and more efficient approach to the big data and
machine learning tools used by each institution. At the same time, they have had to increase their
data production, leveraging on non-traditional data to get more timely and high-frequency
indicators of economic activity, which are important during unprecedented shocks like the
COVID19 pandemic.
4. The challenges and risks of Data Science
We need to exert extreme caution when employing these new tools.
Let me briefly go over some of them.
First, big/web data might lose their statistical relevance when they are employed in an unsound
way. Such data typically entail selection bias because of the features of the population; increasing
the sample size will not shrink the sampling error if the estimation algorithm does not correct for
this kind of distortion.
Second, the availability of a huge amount of data raises the importance of its integrity,
confidentiality and privacy. Personal and company data protection is central to our societies.
The sheer amount of personal data now available, and the growing ease with which individual
information can be merged across databases, have far-reaching implications for privacy,
competition and freedom. Indeed, this has prompted the development of regulations concerning
the treatment of digital data (think of the GDPR or the CCPA in California).
Rules on data management often differ across jurisdictions and data domains. Therefore,
international cooperation is to be encouraged as far as possible.
Technologies enabling the processing of personal data are already available. In 2019, Google made
available its differential privacy library, which allows sensitive data to be processed privately.
To reach these welfare-improving goals, further investment from both the public and the private
sector are required. Close cooperation between the private sector, which typically owns most of
the new data, and the public sector, which uses (or would like to use) such data for policy reasons
and for the common good, will also be essential.
This is why central banks have always made great efforts when it comes to collecting and analysing
data. Throughout its history, Banca d'Italia has drawn extensively on data published by the
National Statistical Institute and other national and international agencies. It has also been an
active producer of statistics, not only on banking, financial and fiscal variables, but also on firms
and households.
Banca d'Italia has been collecting micro-level statistical information on companies since the early
1950s. These data are now enriched with non-traditional sources such as social media, blogs,
newspapers, and private company datasets6.
5. Conclusions
Let me conclude my talk by thanking, once again, all the speakers and participants for joining us
today, even in this virtual fashion. We hope to welcome you here in Rome in person in the near
future. Special thanks go to those who have helped to organize this workshop, which brings
together leading economists, statisticians, artificial intelligence and machine-learning specialists,
data scientists from about fifty-five central banks and fifteen participants from Universities and
government agencies. This guarantees a broad variety of perspectives and a lively discussion.
I am sure that you are going to have a very interesting and productive workshop.