Big Data & Machine Learning Applications for Central Banks
Closing Remarks by Daniele Franco
Deputy Governor of the Bank of Italy
Rome, 22 October 2019
The workshop on 'Big Data & Machine Learning Applications for Central Banks' is
coming to an end. Let me begin by thanking the speakers, discussants and participants.
This is the third workshop organized by Banca d'Italia on these issues. And it will not
be the last. Why are we so interested in these topics?
A few days ago, the Chairman of the Federal Reserve, Jerome Powell, noted that
monetary policy is data dependent. It is adjusted over time in response to new information
about the state of the economy. Monetary policy must be based on timely and accurate
data.
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. But 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 collected micro-level statistical information on companies since
the early 1950s. Back then only a few aggregate statistics were available. We began by
surveying the balance sheets of a small group of firms, an activity that was progressively
expanded until it became what is now the Survey on Industrial and Service Firms. These
days, we collect detailed data on about 5,000 firms.
In the early 1960s, the Bank started the survey on household finance. This survey
allows us to study the distribution of income and wealth at the micro level in order
to analyse the preferences of individuals and the determinants of their economic and
financial decisions. In recent years, we have interviewed about 8,000 households in each
survey.
The digitalization of the economy is changing our lives, the organization of
production and our jobs. It also poses new challenges, requiring changes in the way
central banks collect and analyse data.
The typical indicators used by central banks to measure inflation, investment or
economic activity may not fully capture the changes determined by digitalization.
For example, online shopping has caused a disruption in the retail sector around
the globe along with changes in pricing behaviour. When measuring inflation we should
take account of the prices of online stores such as Amazon or eBay.
Gross domestic product is one of the pillars of macroeconomic statistics. It is still
the most important measure of economic activity and wellbeing. However, the growth
of the digital economy, whose output is usually immaterial, limits the role of GDP in
measuring economic activity and wellbeing.
Digitalization has dramatically changed how businesses are conducted and
goods and services purchased. This brings us to the productivity paradox: productivity
in industrialized countries is growing less than in past decades while economies are
experiencing rapid digitalization. Jerome Powell noted that current statistics may
understate productivity growth because they miss the value that we increasingly derive
from new technologies, such as internet connections and smartphones.
The measurement of the digital economy is now an important challenge for
statisticians and economists. We need new methodological insights.
To answer some of these questions, in 2016 Banca d'Italia put together a team
to assess the potential benefits and hidden risks of Big Data, Artificial Intelligence and
Machine Learning.
Despite controversies about privacy issues, despite password hacking and other
worrisome aspects of online life, the world continues to embrace the internet and
social media. It is estimated that more than 5.1 billion people (two thirds of the world's
population) use a mobile phone and 4.4 billion people take advantage of internet services.
In 2018 the number of internet users recorded 9 per cent growth (see the We Are Social
blog).
So we have plenty of new data which can be mined to improve our knowledge of
economic developments.
To understand and exploit these data we need to bring together different skillsets:
those of economists, statisticians, engineers and computer scientists.
What are the advantages of these data? In his lecture on 'Big Data and Measurement:
From Inflation to Discrimination', the MIT Professor Roberto Rigobon said that the biggest
advantages of organic data (i.e. Big data) as against designed data (such as administrative
data) are that: (i) they reduce the cost of collecting and providing information and (ii)
they do not introduce distortions arising from the behaviour of respondents. People
don't lie to their GPS or to Google.
The banking and financial industries have also been profoundly affected by these
developments. Central banks must react in a timely manner and develop new tools to
carry out their missions: monetary policy, banking supervision and payment system
oversight.
In these two days you have seen many applications which can improve central bank
activities.
Some papers focused on the extraction of quantitative information from texts and
images: this can be useful for forecasting financial market variables, predicting headline
inflation, and analysing the real estate market.1
We have seen the utility of machine learning algorithms for combating illicit financial
activities and increasing the statistical accuracy of micro- and macro-economic indicators
(for example, forecasts of the Index of Industrial Production).2
Some indicators have moved rapidly from the experimental to the production stage.
For example, at Banca d'Italia we have developed and now regularly use an indicator of
the situation of the main banks obtained using selected tweets.3
We have improved our estimates of Foreign Direct Investment for the Balance of
Payments by introducing Machine Learning classifiers.4
We are also working on other sources of information, such as credit and debit
card payment data, which should help us to assess consumption, retail sales and other
macroeconomic variables.5
These developments present significant challenges. I will mention three.
First, there is a risk of biased statistical inference. Big data and, in particular,
unstructured data collected from social media may reflect only a part of the population.
1
See M. Loberto, 'The rental market in Italian cities', Banca d'Italia, Temi di Discussione (Working
Papers), 1228, 2019. See M. Loberto, A. Luciani, and M. Pangallo, 'The potential of big housing
data: an application to the Italian real-estate market', Banca d'Italia, Temi di Discussione
(Working Papers) ',1171, 2018. See also M. Bernardini, P. Cariello, M. De Leonardis, J. Marcucci,
F. Quarta, and A. Tagliabracci, 'Deriving Indicators from a Large Corpus of Italian Documents',
Bank of Italy Workshop on Big data & Machine Learning Applications for Central banks,
October 2019; V. Astuti, G. Bruno, S. Marchetti, and J. Marcucci, 'News and Banks' Equities:
Do Words Have Predictive Power?', Bank of Italy Workshop on Big data & Machine Learning
Applications for Central banks, October 2019.
2
See V. Aprigliano, G. Bruno, S. Emiliozzi, S. Marchetti, J. Marcucci, and D. Nicoletti, 'Neural
Networks for Macroeconomic Forecasting', Bank of Italy Workshop on Big data & Machine
Learning Applications for Central banks, October 2019.
3
See M. Accornero and M. Moscatelli, 'Listening to the buzz: social media sentiment and retail
depositors' trust', Banca d'Italia, Temi di Discussione (Working Papers), 1165, 2018.
4
A. Carboni and A. Moro, 'Imputation techniques for the nationality of foreign shareholders
in Italian firms', External sector statistics: current issues and new challenges. Irving Fisher
Committee on Central Bank Statistics, 2018.
5
See G. Ardizzi, S. Emiliozzi, J. Marcucci and L. Monteforte, 'News and consumer card payments',
Banca d'Italia, Temi di Discussione (Working Papers), 1233, 2019; V. Aprigliano, G. Ardizzi and
L. Monteforte, 'Using the payment system data to forecast the Italian GDP', Banca d'Italia, Temi
di Discussione (Working Papers), 1098, 2017.
Increasing the sample does not improve the accuracy of the estimates if the source of
the bias is not understood and dealt with. The use of massive amounts of rough data
may result in an exceptionally good in-sample fit but may perform poorly out-of-sample
(the curse of overfitting).
Moreover, when using Big data, special attention should be paid to the robustness
and stability of the estimates over time.
The second challenge is privacy. Protecting the integrity and confidentiality of
personal data is of paramount importance to our societies. Storing personal data in
digital repositories belonging to public and private institutions can be risky. We should
avoid any use for illegitimate purposes such as the second-hand trading of personal
information.
At Banca d'Italia, we are exploring new avenues for utilizing microdata without
jeopardizing their confidentiality. In this way data from different institutions can be
combined to produce aggregate statistics that are important for understanding economic,
financial and welfare issues. We have started experimenting some privacy-preserving
algorithms with Istat. We are also discussing these issues with Eurostat and the National
Authority for Personal Data Protection.
The third challenge concerns the preservation of data over time. While we keep
producing a huge amount of data, we have not yet agreed on a shared set of criteria for
making these data accessible in the future. It is crucial that technology-neutral standards
be established to ensure data availability for future generations. Coordination between
statistical agencies, governments, academia and private companies is equally crucial.
Timely and accurate information derived from non-traditional data sources, as well
as new analytical techniques can help central banks to improve their knowledge of the
economy and society, allowing them to make better-informed data-driven decisions.
Banca d'Italia is firmly committed to carrying out further research on these issues
and to cooperating with other institutions to strengthen methodologies and applications.
Future events will certainly be organized to share the results of our research activity.