Irving Fisher Committee

on Central Bank Statistics

IFC Working Papers

No 21
Big data in Asian central
banks
By Giulio Cornelli, Sebastian Doerr, Leonardo
Gambacorta and Bruno Tissot

February 2022
IFC Working Papers are written by the staff of member institutions of the Irving Fisher
Committee on Central Bank Statistics, and from time to time by, or in cooperation
with, economists and statisticians from other institutions. The views expressed in
them are those of their authors and not necessarily the views of the IFC, its member
institutions or the Bank for International Settlements.

This publication is available on the BIS website (www.bis.org).

© Bank for International Settlements 2022. All rights reserved. Brief excerpts may be
reproduced or translated provided the source is stated.

ISSN 1991-7511 (online)

ISBN 978-92-9259-533-3 (online)
Big data in Asian central banks

Giulio Cornelli, Sebastian Doerr, Leonardo Gambacorta and Bruno Tissot 1

Contents

Abstract ....................................................................................................................................................... 2

1. Introduction ....................................................................................................................................... 3

2. What is central banks’ definition of big data? ..................................................................... 5

3. How do Asian central banks use big data? ........................................................................... 6

4. What are the main challenges in the use of big data? ..................................................... 9

5. Is there a role for policy cooperation? .................................................................................. 12

6. Conclusion ........................................................................................................................................ 14

References ................................................................................................................................................ 15

Appendix: Big data projects in Asian central banks ................................................................. 18

1
Respectively Senior Financial Market Analyst (Giulio.Cornelli@bis.org); Economist
(Sebastian.Doerr@bis.org); Head of Innovation and Digital Economy
(Leonardo.Gambacorta@bis.org); Head of Statistics and Research Support, Bank for International
Settlements (BIS) and Head of the Secretariat of the Irving Fisher Committee on Central Bank Statistics
(IFC) (bruno.tissot@bis.org).

We would like to thank Jose Maria Serena and Fernando Perez Cruz for their advice and input. For
comments and suggestions, we also thank Redentor Paolo Alegre Jr, Gianni Amisano, Douglas Araujo,
Claudio Borio, Agustin Carstens, Stijn Claessens, Jon Frost, Michel Juillard, Julian Langer, Juri Marcucci,
Li Ming, Kuniko Moriya, Luiz Awazu Pereira, Rafael Schmidt, Hyun Song Shin and Helio Vale. The
views expressed are those of the authors and not necessarily those of the BIS or the IFC.

Big data in Asian central banks 1

Abstract

This paper reviews the use of big data in Asian central banks, leveraging on a survey
conducted among the members of the Irving Fisher Committee. The analysis reveals
four main insights. First, Asian central banks define big data in a more encompassing
way that includes unstructured non-traditional as well as structured data sets. Second,
interest in big data appears higher in Asia, including at the senior policy level; the
focus is in particular on projects developed to process natural language, conduct
nowcasting/monitoring exercises, and develop applications to extract economy
insights as well as suptech/regtech solutions. Third, Asian central banks report dealing
with big data to support a wide range of tasks. Fourth, big data poses new challenges,
with specific attention paid in the region to cyber security and data strategy. As a
result, there is a growing need for international policy cooperation, especially among
public authorities in Asia to facilitate the use of payments data and promote
innovative technological solutions.

Keywords: Asian central banks, artificial intelligence, big data, data science,
international cooperation
JEL codes: G17, G18, G23, G32

2 Big data in Asian central banks

1. Introduction

Big data sources are developing fast, and applications for making use of this new
information are flourishing in parallel. This trend, which is particularly pronounced in
Asia, primarily reflects the impact of digitalisation, with the development of the
“internet of things” and the ever-increasing ability to digitally process “traditional”
information, such as text. It is also a consequence of the large databases that have
been created as a by-product of the complex operations taking place in modern
societies. Additionally, vast amounts of data have emerged in the administrative,
commercial and financial realms, an evolution spurred by the important data
collection strategies undertaken after the great financial crisis of 2007–09 to address
the information challenges posed by developments in the financial sector. We now
live in the “age of big data” (Forbes (2012)).
Central banks are no exception to this general picture (Buch (2019)). They have
shown an increasing interest in using big data in recent years, as already documented
extensively by the Irving Fisher Committee (IFC) on Central Bank Statistics (IFC (2017),
Tissot (2017), Nymand-Andersen (2016), Mehrhoff (2019)). Central bank big data-
related work covers a variety of areas, including monetary policy and financial stability
as well as research and the production of official statistics. However, in contrast to the
rapid pace of innovation seen in the private sector, big data applications supporting
central banks’ operational work were developed only slowly initially. This tended to
reflect a number of constraints, such as a lack of adequate resources as well as the
intrinsic challenges associated with using big data sources to support public policy.
Yet, in recent years, central banks’ use of big data has proliferated, especially among
Asian countries.
Will central banks catch up and transform the way they operate to further benefit
from the information revolution? Or will their use of big data sources and applications
progress only gradually due to the inherent specificities of their mandates and
processes? To shed light on these issues, this paper reviews the use of big data and
machine learning in the Asian central bank community, leveraging on a survey
conducted in 2020 among the members of the IFC (IFC (2021a)). To this end, this
paper analyses the responses from seven Asian central banks, with a specific focus on
their reported big data projects (cf Appendix).2
The approach dealt with the following key questions: What constitutes big data
for central banks, and how strong is central banks’ interest in it? Have central banks
been increasing their use of big data and, if so, what were the main applications
developed? And finally, which constraints are faced when using big data and how can
they be overcome? To address these issues, Asian central banks’ answers to the 2021
survey were compared with those of their peers in the rest of the world.
This analysis uncovered four main insights.
First, Asian central banks have a comprehensive view of big data, which can
comprise very different types of data sets. First and foremost, it includes large

2
The list of Asian central banks includes: Bangko Sentral ng Pilipinas (BSP), Bank Indonesia (BI), Bank
of Japan (BoJ), Bank of Thailand (BoT), Bank Negara Malaysia (BNM), Monetary Authority of Macao
(MAM), Reserve Bank of India (RBI). Almost two thirds of the 92 IFC institutional members at that
time answered the survey. More information on the survey is contained in IFC (2021a) and Doerr et
al (2021).

Big data in Asian central banks 3

“non-traditional” (or unstructured) data often characterised by high volume, velocity
and variety and that must be processed using innovative technologies. Yet for the
vast majority (85%) of respondents in Asia, big data also includes large “traditional”
(ie structured) data sets. These can be the outcome of explicit reporting requirements
set by public regulators; they are also often “organic” by-products collected as a result
of commercial (eg payment transactions), financial (eg tick-by-tick price quotes
observed in financial markets) and administrative (eg files collected by public
institutions) activities – these data are often referred to as “financial big data”. In
contrast, only 60% of central banks outside Asia do include such traditional data sets
in the concept of “big data”. Potentially, the relatively large footprint of big techs in
Asia has stimulated the discussion in the region (Cornelli et al (2020)).
Second, interest in big data is high in Asia: around two thirds (60%) of central
banks in the region mentioned that they discuss big data issues extensively, while this
is reported to be the case by only a minority (42%) of their counterparts in the rest of
the world. Moreover, all Asian central banks in the survey indicated a high to very-
high level of interest also at the senior policy level, while this was the case for only
58% of their counterparts in other regions.
Third, and turning to concrete use cases, 68% of Asian central banks report
dealing with big data to support economic research, monetary and financial stability
policies as well as their statistical production tasks. This is comparable to the numbers
reported in the rest of the world (64%). The big data projects undertaken in this
context typically involve four main types of applications: natural language processing
(NLP), nowcasting exercises (including to support their statistical processing tasks),
applications to extract information on the state of the economy from granular
financial data and other non-traditional sources as well as suptech/regtech
applications.
Fourth, the survey shows that Asian central banks discuss extensively the new
challenges posed by the advent of big data. A major one is setting up a reliable and
high-powered IT infrastructure. While many institutions have undertaken important
initiatives to develop adequate platforms to facilitate the storage and processing of
very large and complex data sets (IFC (2020)), progress has varied in the region. This
is in part because of the need to hire and train staff, which is difficult due to the
limited supply of candidates with the necessary skills (eg, data scientists). Other
challenges include the legal basis for using private data and the safety, ethical and
privacy concerns this entails, as well as the “fairness” and accuracy of algorithms
trained on preclassified and/or unrepresentative data sets. Data quality and
governance issues are also significant, since much of the new big data collected as a
by-product of economic or social activities needs to be curated before proper
statistical analysis can be conducted (IFC (2021b)). These challenges are generally
seen as equally important among different central banks across the world. One
notable point is that cyber security and the development of a formal strategy for the
use of big data are topics that appear to be higher on the agenda of Asian central
banks compared to their counterparts in other regions.
The rest of the paper is organised as follow. Section 2 provides an overview of
how Asian central banks define big data. Sections 3 illustrates in which fields they use
or plan to use big data and discusses specific use cases. Section 4 reviews the main
challenges in the use of machine learning and big data. Section 5 discusses how
cooperation among public authorities could relax the constraints on collecting,
storing and analysing big data. Section 6 concludes.

4 Big data in Asian central banks

 2. What is central banks’ definition of big data?

The definition of big data is not unique, as it pertains to the specific angle of its use.
In general, big data can be defined in terms of volume, velocity and variety (the so-
called 3Vs). The reason is that for data to be “big”, they must not only have high
volume and high velocity, but also come in multiple varieties. Yet there are also many
different views on what defines “big data”. 3
In practice, big data can include the information generated from a wide variety
of sources, such as social media, web-based activities, machine sensors, or financial,
administrative or business operations. This comprehensive view of big data is
confirmed by the survey results for Asian central banks. Certainly, no central bank
considers traditional data alone as big data. But as reported in Graph 1, only 14% of
the respondents define big data exclusively as large non-traditional or unstructured
data that require new techniques for the analysis (in contrast, almost 40% of their
counterparts in the rest of the world have such a narrow definition). The remaining
86% of Asian respondents also include traditional and structured data sets in their
definition of big data. These structured data sets comprise those collected for
administrative or regulatory/supervisory purposes, often labelled as “financial big
data” (Cœuré (2017), Draghi (2018)).
Based on the results from the survey, a comprehensive definition of big data
would therefore cover all types of data sets that require non-standard technologies
to be analysed. The reason for this is, in part, that traditional statistical techniques
face hurdles when applied to unstructured data. For instance, to analyse handwritten
text, it must first be turned into structured data, as is done for instance with NLP
algorithms.

Hyperlink BIS
Central bank definitions of big data and main sources Graph 1

As a percentage of respondents

The sample includes 7 Asian central banks and 43 non-Asian central banks. Respondents could select multiple options. Non-traditional data
include “unstructured data sets that require new tools to clean and prepare”, “data sets with a large number of observations in the time
series”, “data sets that have not been part of your traditional pool”, and “data sets with a large number of observations in the cross-section”.

Sources: IFC (2021a); authors’ calculations.

3
Occasionally, veracity is also added, as big data is often collected from open sources; moreover, the
literature is quite diverse and can refer to a much larger number of “Vs” (Tissot (2019)).

Big data in Asian central banks 5

 There is a variety of raw data sources used by Asian central banks for analysis.
These range from structured administrative data sets such as credit registries to non-
traditional data obtained from newspapers and online portals or by scraping the web.
This type of information – including the data produced by the internet itself – may
not necessarily be “big”, but it is complex and cannot be easily analysed with
traditional statistical techniques tailored to numerical data sets. Instead, it requires
specific tools to be cleaned and properly prepared. However, in some instances it is
possible to acquire these data from private providers in an already aggregated and
organised form.
Three examples are worth mentioning. First, mobility reports, which provide
aggregate commuting trends obtained through GPS from mobile phones and which
were able to support the monitoring of households’ access to recreation areas when
the Covid-19 pandemic struck in 2020 (see Bank of Japan (2020)). The second example
relates to internet searches, such as Google Trends, that can be used to assess
developments in real time – for instance, expectations on developments in the labor
market (Doerr and Gambacorta (2020a,b)) or car sales (Nymand-Andersen and
Pantelidis (2018)). A third source of unstructured information for central banks is text
in printed format, such as newspaper articles, firms’ financial statements, official press
releases, etc.
While central banks have substantial experience with large, structured data sets,
typically of a financial nature, they have only recently started to explore unstructured
data. As discussed above, the analysis of unstructured data requires the application
of specific tools. They are often the by-product of corporate or consumer activity and
before they are analysed, they must be cleaned and curated, ie organised and
integrated into existing structures.

3. How do Asian central banks use big data?

According to the 2020 IFC survey, central banks and supervisory authorities are
rapidly adopting big data and machine learning: the share of central banks
currently using big data has risen to 80% globally, up from just 30% in 2015. This
share has risen from 33% to 86% when looking specifically into Asia. Moreover,
around 60% of central banks in the region reported that they discuss big data issues
extensively, a ratio that is significantly above the one (42%) observed in the rest of
the world. Furthermore, all Asian respondents indicated a high to very-high level of
interest at the senior policy level, compared to only 58% outside the region.
Big data is used in a variety of areas, including research as well as monetary
policy and financial stability. Asian central banks (represented by the red bars in Graph
2) appear to use big data in most areas by more than their peers (blue bars), except
for research purposes. In particular, they process non-traditional data (darker bars) to
a greater extent to support monetary and financial stability policies – including for
specific supervisory and regulatory purposes (suptech and regtech).

6 Big data in Asian central banks

Hyperlink BIS
Purposes for which central banks use big data Graph 2

In per cent

1
Respondents could select multiple options. See footnote to Graph 1 for details on how institutions define big data and on the sample
composition. 2 Includes “monitoring crypto assets”, “cyber security” and “network analysis”.

Sources: IFC (2021a); authors’ calculations.

The big data projects undertaken by Asian central banks involve four main types
of applications: NLP, nowcasting exercises, applications to extract economy wide
insight from granular financial data and other non-traditional sources, and
suptech/regtech applications. A list of selected big data projects in Asian central
banks is provided in the Appendix.
A first type of application uses textual information through NLP. The goal is
generally to turn qualitative text-based intelligence into numerical format. One
example has been the computation of so-called economic policy uncertainty (EPU)
indices in India to assess the degree of uncertainty faced by economic agents
(Priyaranjan and Pratap (2020)). Such indices are basically constructed by setting up
dictionaries that allow for the definition of specific terms that refer to uncertainty, and
then searching them in the text considered (for instance in newspaper articles or on
internet sites). These selected terms are then counted and aggregated to provide a
synthetic index that reflects the degree of uncertainty displayed in the document of
interest. Sentiment indices can be computed in this way, eg in order to measure the
probability of the occurrence of financial instability episodes.
NLP is also helpful for policy evaluation. For instance, one can quantify the
monetary policy stance that is communicated to the public via the publication of
meeting minutes. Similarly, market expectations of interest rate decisions have been
assessed by analysing market commentaries ahead of policy meetings in Indonesia
(Andhika Zulen and Wibisono (2019)). Such exercises can be updated regularly, which
is a key advantage compared to more traditional surveys of market participants. The
information collected on economic agents’ expectations can be particularly useful
when future markets are not well developed, lack liquidity or are subject to
unexpected shocks (Amstad and Tuazon(2020); Armas et al (2020)). By contrast,
reported use of text data to inform financial stability policies has been relatively scarce
so far, although it appears to be developing as well. Other applications using text
analysis in Asian central banks have helped to: i) evaluate monetary policy credibility;
ii) ensure consistency in central banks’ communication of supervisory issues to

Big data in Asian central banks 7

financial institutions; iii) improve efficiency in the compilation of statistics (Chansang
(2019)); iv) assess the state of the labor market (Bailliu et al (2019)) or of trade
conditions (Amstad et al (2021)); v) extract information on tourism activities
(popularity of travel destinations and potential associated topics); and vi) capture
firms’ sentiment or evaluate employees’ feedback. 4
Second, a large and increasing number of central banks support their economic
analysis with nowcasting models drawing on big data. More than 40% of Asian
central banks (24% in the rest of the world) indicated that big data is used for this
purpose, especially to provide additional information on private consumption,
industry/retail sales, retail/housing prices, payments and unemployment conditions
(Graph 3, left panel). Matsumura et al (2021) combine GPS data with information on
geographical coordinates of commercial and public facilities (such as shops and
factories) to closely examine those sectors in which nowcasting can be applied to
estimate (with a reported high level of precision and efficiency) household
consumption and firm production. Finally, nowcasting models can help to fill
statistical gaps, eg when reference series do not exist, are available only at a low
frequency or are suddenly disrupted, as during the Covid-19 pandemic (De Beer and
Tissot (2020)). This aspect has become particularly important for central banks,
reflecting their dual role as producers as well as users of statistics.

Hyperlink BIS
For what specific purposes does your institution use big data? Graph 3

Nowcasting Suptech
In per cent of respondents In per cent of respondents

See footnote to Graph 1 for details on the sample composition.

Sources: IFC (2021a); authors’ calculations.

Usually, these nowcasting exercises are frequently updated as new data come in,
and various techniques – eg Lasso (Least Absolute Shrinkage and Selection Operator)
– are applied to select the combination of variables that maximises the forecast at a
given point in time (Richardson et al (2019)). One advantage is that this approach
does not rely on specific relationships assumed ex ante (as is the case for bridge

4
Of course, economic agents adjust to new technologies. For example, Cao et al (2020) show that firms
are aware that their filings are parsed and processed for sentiment via machine learning.
Consequently, they avoid words that computational algorithms may perceive as negative. This could
bias any analysis based on these filings.

8 Big data in Asian central banks

models used for “traditional” nowcasting exercises) and may be better suited to
identifying turning points, especially during times of economic upheaval (INSEE
(2020)).
A third category includes the various applications developed by central banks to
extract economy-wide insights from granular financial data or other non-
traditional sources of micro data. Financial big data include large proprietary and
structured data sets, such as those from trade repositories for derivatives transactions,
or from credit registries for loans or individual payments. For instance, trade
repositories’ records have helped identify networks of exposures in Thailand
(Chantharat et al (2017)). Similarly, information from credit registries have supported
the assessment of credit quality, eg by improving estimates of default probabilities or
loss-given-default (Pagano and Cappelli (1993)). And real-time gross settlement
systems’ data have helped help to show bank-firm interconnections through the
payments processed.
In addition, special attention has been given to extract information from non-
traditional data such as internet search queries like Google Trends that are supporting
the monitoring exercises conducted by the Bank of Thailand (Sawaengsuksant
(2019)). Other use cases of non-traditional sources include the analysis of: (i)
electricity consumption to monitor the residential property market or export invoices
to analyse the strength of the export sector in Malaysia (Wanitthanankun and
Dummee (2017)); (ii) the number of job searches to monitor the evolution in the labor
market in Thailand (Nuprae et al (2017)); (iii) mobile phone user traffic data to evaluate
the effects of Covid-19 on mobility and migration (Chanthaphong and
Tassanoonthornwong (2021)); (iv) patent applications by start-ups to estimate the
economic impact of venture capital innovations in Japan (Washimi (2021)); and (vi) e-
commerce sales (Yezekyan (2018)).
A fourth category comprises the wide range of suptech and regtech
applications to support micro-supervisory tasks. This can cover multiple areas, as
documented by Broeders and Prenio (2018), di Castri et al (2019), Coelho et al (2019)
and Financial Stability Board (2020). In general, many of the applications developed
among the Asian jurisdictions considered focus on micro-level risk assessment. For
instance, firm-level information gathered from financial statements or newspapers
can be used to support early warning exercises or enhance credit scoring (mentioned
by about 55% and 45% of Asian central banks, respectively; Graph 3, right-hand
panel). Another important area relates to fraud detection (almost 30% of the cases) –
for instance, by screening credit contracts for suspicious terms and conditions to
enhance consumer protection. Lastly, almost one third of surveyed Asian central
banks deploy big data algorithms for anti-money laundering/combating the
financing of terrorism (AML/CFT) purposes – for instance, when analysing payment
transactions to identify suspicious patterns.

4. What are the main challenges in the use of big data?

As noted above, central banks and supervisory authorities in Asia already use
extensively big data sources and analytics such as machine learning for research
purposes, namely to inform monetary policy decisions, facilitate their statistical
compilation work and support their regulatory and supervisory tasks. However, the
use of big data poses various challenges for them. Graph 4 shows that these issues

Big data in Asian central banks 9

are actively discussed by central banks in Asia (in red), especially in comparison to
their counterparts in the rest of the world (in blue). All the Asian central banks
considered mention that they have active discussions on a wide range of topics, such
as the availability of IT infrastructure, legal, security and privacy issues, as well as the
availability and strategic use of big data. Interestingly, cyber security and the
development of a formal strategy for the use of big data are areas that appear much
more actively discussed compared to their counterparts in the rest of the world.

Hyperlink BIS
What is the focus of the discussions on big data within your institution? Graph 4

In per cent

1 2
Respondents could select multiple options. See footnote to Graph 1 for details on the sample composition. Includes “data quality and
reliability”, “data interpretation” and “data governance”.

Sources: IFC (2021a); authors’ calculations.

More specifically, the survey has highlighted five main challenges for Asian
central banks in the use of big data. The first one is setting up a reliable and high-
powered IT infrastructure (IFC (2020)). Providing adequate computing power and
software involves high up-front costs. Many central banks have undertaken important
initiatives to develop big data platforms to facilitate the storage and processing of
large and complex data sets. One possible approach is represented by so-called data
lakes, obtained from pooling different data sets that are curated for future use. A
reliable and safe IT infrastructure is a prerequisite not only for big data analysis, but
also to prevent cyberattacks.
Second, central banks need to build up human capital to exploit big data.
Setting up and maintaining big data platforms requires a specific type of skillset,
combining statistical, IT, and analytical/mathematical aspects. Yet the supply of “data
scientist” is scarce and they are in high demand (Cœuré (2020)), in both the public
and the private sector. One solution is for central banks to train existing staff but
learning the new techniques that are needed can require significant time and effort.
In addition, experience shows that these skill adjustments should take place beyond
the operational level, eg the statisticians in charge of using advanced tools. Those
analysing the output of complex models must also have a good understanding of the
new techniques in order to ensure that big data predictions are not only accurate but
also representative and “interpretable” – so that specific explanatory causes or factors
can be identified and communicated for policy use. Another issue is attracting and
retaining talent, especially in the face of intense competition from the private sector,

10 Big data in Asian central banks

as well as from advanced economies especially for the less developed jurisdictions in
Asia. This may also call for a review of existing public compensation schemes, career
systems and internal hierarchical organisations in central banks.
A third challenge are the legal underpinning and ethical aspects for the use of
private and confidential data. Reputational aspects may hinder the use of information
sourced from the internet when little is known about its accuracy and the respect of
methodological standards that central banks have to comply with, not least in view
of the key role they play in National Statistical Systems. For instance, internet-based
indicators such as search queries and messages on social media may not be
representative of the real economy – not everybody is on Twitter, or only a subset of
the CPI basket prices can be scraped from the web. Moreover, various terms and
conditions may restrict the use of these data and certain forms of web-scraping are
illegal in some jurisdictions. In general, web crawlers cannot obtain data from sites
that require authentication.
Considering ethics and privacy aspects, citizens might feel uncomfortable with
the idea that central banks are scrutinising their search histories, social media
postings or listings on market platforms. While these concerns are not new, the
amount of data produced in a mostly unregulated environment makes them more
urgent (Jones and Tonetti (2020), Boissay et al (2020)). Certainly, when US consumers
were asked in a systematic survey whom they trust with safeguarding their personal
data, the respondents reported that they trust big techs the least (Armantier et al
(2021)). They had in fact far more trust in traditional financial institutions, followed by
government agencies and fintechs. Similar patterns are present in Asian countries
(Chen et al (2021)).5 Yet, ensuring privacy against unjustified intrusion not only by
commercial actors but also by government has the attributes of a basic right. For
these reasons, the issue of data governance has emerged as a key public policy
concern (IFC (2021b)).
A fourth challenge is “algorithmic fairness”. This consideration can be less
relevant for some tasks (eg nowcasting), but it may matter greatly for others (eg
evaluating the suitability of regtech applications), and in general any application of
machine learning that effects individuals would need to be subject to fairness
validations (MacCarthy (2019)). A main issue is that algorithms are often trained on
pre-classified data sets that can be subject to (known or unknown) biases, including
related to gender and ethnicity.6 Moreover, the relationship that seems to exist
between unstructured data and a certain phenomenon may unexpectedly deteriorate
when additional information arrives (eg the incorporation of new, “out-of-sample”
information).The failure of Google Flu Trends provides a good example of these perils,
as it was initially intended to provide estimates of influenza activity based on Google
Search queries but was discontinued in the mid-2010s (Lazer et al (2014)).
Finally, data quality issues are also significant, since much of the new big data
collected as a by-product of economic or social activities needs to be curated before

5
IIF (2020) finds that there is no “one-size-fits-all” approach to machine learning governance, and
there are interesting regional differences, many of which can be attributable to existing non-
discrimination and data protection laws.
6
For instance, data on past loan applications could reflect any discriminatory decisions on the part of
loan officers vis-à-vis minorities or women (Angwin et al (2016), Ward-Foxton (2019)). Likewise,
unrepresentative data could lead an algorithm to wrongly infer attributes about underrepresented
segments of the population or perpetuate any previous biases.

Big data in Asian central banks 11

proper statistical analysis can be conducted. This stands in contrast to traditional
sources of official statistics that are designed for a specific purpose, eg surveys and
censuses. Major challenges include data cleaning (eg for sources like newspapers,
social media or financial big data records), sampling and representativeness (eg in
the case of Google searches or employment websites) and matching new data to
existing sources, as documented by Siksamat (2021) in the case of Thailand.

5. Is there a role for policy cooperation?

Cooperation could foster central banks’ use of big data, in particular through
collecting and showcasing successful projects and facilitating the sharing of
experiences. For instance, developing technical discussions between institutions is
seen as a good way to build the necessary skillset among staff and develop relevant
IT tools and algorithms that are best suited to central banks’ needs.
Looking ahead, a promising area for collaboration among central banks in Asia
could be in global payments data. More than 85% of Asian central banks reported
an active use of high frequency payment data in their institutions, with a primary focus
on either the type of instruments, counterparties involved or both. This ratio is much
higher compared with other central banks in the rest of the world (about 65%; Graph
5). Moreover, all of Asian central banks expressed interest in contributing to a pilot
study on payment data (Graph 6, left-hand panel), especially to develop surveillance
exercises with a focus on interconnectedness in the financial system. This stands in
contrast to their counterparts in the rest of the world, where interest in using payment
data is primarily limited to nowcasting purposes (right-hand panel).

Hyperlink BIS
Which types of payments data are useful for your institution? Graph 5

In per cent

See footnote to Graph 1 for details on the sample composition.

Sources: IFC (2021a); authors’ calculations.

12 Big data in Asian central banks

Hyperlink BIS
Would your institution be willing to contribute to a pilot study on the use of
payments data? Graph 6

Asian central banks Rest of the world

Number Number

See footnote to Graph 1 for details on the sample composition (note: only 29 IFC members responded to this specific question); multiple
answers possible in each jurisdiction.

Sources: IFC (2021a); authors’ calculations.

International financial institutions can foster cooperation around big data. For
instance, they can help develop in-house big data knowledge, helping to reduce
central banks’ reliance on big data services providers, which can be expensive and
entail significant legal and operational risks. Indeed, the IFC has been actively
supporting such exchange of experience at the global level, and several
complementary initiatives are being developed in the Asian region, for instance
among EMEAP central banks.7
International bodies can also facilitate innovation by promoting technological
solutions and initiatives to enhance the global statistical infrastructure. In this
regard, the BIS Innovation Hub has identified as strategic priorities, among others,
effective supervision (including regtech/suptech) and open banking/finance that
could benefit from drawing on big data sources and tools. It is currently developing
its work program in these fields, with a view to producing proofs of concept (PoC)
that can benefit the central banking community.
Initial projects in the field of particular relevance for Asian central banks
include Ellipse, led by the Singapore Centre of the Hub, and Genesis (Hong Kong
Centre). Ellipse is a PoC that aims to demonstrate the functionalities and feasibility of
an integrated regulatory data and analytics platform that can (i) reduce compliance
burdens placed on financial institutions by moving away from template-based
regulatory reporting requests; (ii) be nearer to "real-time" and relevant to current
events to support supervisory judgments and actions, both locally and globally; (iii)
support a move towards newer digitally enabled architectures to replace traditional
concepts and processes of data collection; and (iv) enable predictive insights and early
warning by integrating big data analytics. Turning to Genesis, this project explores

7
Executives’ Meeting of East Asia-Pacific Central Banks (EMEAP) is a co-operative forum of eleven
central banks and monetary authorities in the East Asia and Pacific region. Common projects are
developed in the areas of banking supervision and resolution, financial markets, payments and
market infrastructure, and information technology.

Big data in Asian central banks 13

the “green art of the possible” through combining blockchain, smart contracts, digital
assets, and the internet-of-things. The underlying vision is that an investor can
download an app to invest into government bonds, so that the proceeds can be used
to develop a green project. Over the bond's lifetime, the investor would be able to
not just see accrued interest, but also track in real time how much clean energy is
being generated, and the consequent reduction in CO2 emissions linked to the
individual investment.

6. Conclusion

The world is changing and so is the way it is measured. This paper provides an
overview of the use of big data in the Asian central bank community. It leverages on
a survey conducted in 2020 among the members of the IFC. The specific responses
from seven Asian central banks were analysed and compared with those of other
central banks in the rest of the world. The overall picture suggests that, while central
banks in other regions see similar challenges and opportunities in the use of big data,
those located in Asia have very distinctive features.
First, Asian central banks define big data in an encompassing way that includes
not only unstructured, non-traditional data but also structured data sets to a larger
extent compared to other regions. Second, interest in big data appears higher in Asia,
including at the senior policy level. Third, a large majority of Asian central banks report
dealing with big data to support economic research, monetary and financial stability
policies as well as their statistical production tasks, a ratio that is slightly above the
situation reported in other regions. The related big data projects are developed
mainly in the areas of NLP, nowcasting, applications to extract economy wide insight,
and suptech/regtech solutions. Fourth, the advent of big data poses new challenges,
such as the reliability of IT infrastructures, legal aspects around privacy, algorithmic
fairness, and data quality. Interestingly, there is a somewhat higher interest among
Asian central banks for analysing these issues, with topics such as cyber security and
the development of a formal strategy for the use of big data being particularly high
on their agendas.
Asian (and other) central banks are willing to join forces to reap the benefits of
big data, the IFC survey shows. International financial institutions can support these
cooperative approaches.8 They can facilitate innovation by promoting technological
solutions to harmonise data standards and processes among jurisdictions, and
important projects have been already launched in Asia.

8
Specific initiatives to foster closer collaboration and accelerate innovation efforts include the ASEAN
Open Data Dictionary, ASEANstats, Asia Open data Partnership (Dataportal.Asia).

14 Big data in Asian central banks

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Big data in Asian central banks 17

Appendix: Big data projects in Asian central banks

Central Bank Project Data source Purpose Platform

Bangko Sentral Develop the big data Roadmap and big data
R, Python, Geoda,
ng Pilipinas BSP Big Data Project Government/ Academia governance framework; operationalise big data
QGIS
system Prototypes
Data Warehouse
Enterprise Data Warehouse Have a single database for the BSP
solution/service-providers
Reports from BSP supervised For financial stability, anti-money laundering,
Anomaly Detection in Data SAS, Python
and unsupervised entities and fraud detection purposes

Bank Indonesia Indicator of job demand from Produce proxy indicator/nowcasting Hadoop, Hive, Spark,
Online job vacancy portals
online job vacancy portals employment Impala

Identification of main
Identify main counterparties in forex market Hadoop, Hive, Spark,
counterparties in forex RTGS
from payment system data Impala
market

Indicator of consumption Produce indicator of consumption (household Hadoop, Hive, Spark,

Clearing system
from payment system data and government) from payment system data Impala

Indicator of property prices Produce statistics for property prices Hadoop, Hive, Spark,
Online property portals
from online property portals in secondary market Impala

Indicator of automobile
Produce proxy indicator/nowcasting automobile Hadoop, Hive, Spark,
supply from online Online automobile portals
supply Impala
automobile portals

Analysis of travelers' reviews Produce analysis of popularity of travel

Online travel portals Python
from online travel portals destinations and their main issues

Produce proxy indicator of household

Indicator of e-commerce Hadoop, Hive, Spark,
E-commerce sites consumption, retail sales, and use of payment
sales Impala
instruments

Indicator of Economic Policy Produce indicator of Economic Policy

News articles Python
Uncertainty Uncertainty for Indonesia

Indicator of monetary policy Produce indicator of public's perception of

News articles Python
credibility monetary policy credibility

Interconnectedness of banks Identify core and periphery banks in payment Hadoop, Hive, Spark,
RTGS
in payment system system Impala

Interconnectedness of
Hadoop, Hive, Spark,
corporations in payment RTGS Identify network structure of corporations
Impala
system

Bank of Japan A Network Analysis of the Collected from financial Analyse the structure of the Japanese repo
R
Repo JGB Market institutions located in Japan market

Release of “Statistics on
Collected from financial Publish data on securities financing transactions
Securities Financing
institutions located in Japan in Japan
Transactions in Japan”

Corporate behavior and Japan's patent data provided by Analyse the effects of R&D investment on
R
innovation Panasonic system solutions productivity growth

Analyse business and consumer sentiments

Analysis of business and
Economy Watchers Survey using comments from respondents of the R, Python
consumer sentiments
survey using text analysis

18 Big data in Asian central banks

Bank of Thailand Leading indicators for export Thai Customs Department Develop leading indicators Python

Manufacturing firm census Understand the structure of manufacturing

Manufacture sector structure Stata
from NSO sectors

High-rise residential property Electricity bills, Provincial

Monitor real demand for high-rise property RStudio
occupancy rate Electricity Authority

Use internet search Develop indicators to help monitor economic Google

Google Trends/Correlate
technology conditions Trends/Correlate

Use text analytics to improve Comptroller General's Use text analytics to improve efficiency of
Python
operation Department statistics compilation

Credit registry data/

SME financing behavior and Identify SMEs' viability and assess Impala/
micro data obtained
SME credit risks credit risks Tableau
for supervisory purposes

Export indicator from data Thai Customs Department and

Develop indicator for monitor Thai exports Python
analytics Bank of Thailand

Stylised facts on invoicing

Thai Customs Department and Explore invoice structure and natural hedge of
currency and natural hedge RStudio
Bank of Thailand Thai exporters
of Thai exporters

Determine self-employed labor income to

Self-employed labor income Labor Force Survey from NSO RStudio and Stata
monitor economy

Explore and understand the job switching

Job switching pattern of labor Social Security Office RStudio and Stata
behavior

Web-scraping, firm balance

Structure of retail trades Understand the structure of retail trade sectors Stata and Tableau
sheet, Labor Force Survey

Bank Negara Credit modelling for retail Predict probability of default, loss given
Internal credit registry database R programming
Malaysia and non-retail borrowers default etc.

News monitoring and Enhance surveillance of topics of interest and Python, Django,
Public news sites
sentiment analysis dashboard understand public sentiment on these topics ElasticSearch etc.

Analytical solution for Construct network models to establish Python, Django,

Data submitted by regulated
analysis of AML/CFT-related relationships between entities and provide ElasticSearch, Neo4j
entities, internal databases
data search capability etc.

Employee feedback text Internal talent management Analyse employees' key feedback from talent Python, Django,
analysis surveys management surveys HuggingFace

Python, Django,
Supervisory letter text Ensure consistency in the communication of
Internal data HuggingFace,
analysis supervisory issues to financial institutions
ElasticSearch

Reserve Bank of End-to-end Hadoop

Structured data from regulated Create single repository comprising structured
India Centralised Information eco-system,
entities and unstructured web- and unstructured big data, and use it for
Management System integrated with R /
scraped data analyses
Python

Outlook on specific economic

indicators based on media Online Portals Big data analytics, ML and related techniques Hadoop / R / Python
articles

Food inflation based on

Online Portals Big data analytics, ML and related techniques Hadoop / R / Python
online retail prices

Housing Price Index based on

online property Online Portals Big data analytics, ML and related techniques Hadoop / R / Python
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Source: IFC (2021a).

Big data in Asian central banks 19