28 May 2019

BIS: The use of big data analytics and artificial intelligence in central banking

Information and internet technology has fostered new web-based services that affect every facet of today’s economic and financial activity. This creates enormous quantities of “big data” – defined as “the massive volume of data that is generated by the increasing use of digital tools and information systems” (FSB (2017)). Such data are produced in real time, in differing formats, and by a wide range of institutions and individuals. For their part, central banks face a surge in “financial big data sets”, reflecting the combination of new, rapidly developing electronic footprints as well as large and growing financial, administrative and commercial records.

This phenomenon has the potential to strengthen analysis for decision-making, by providing more complete, immediate and granular information as a complement to “traditional” macroeconomic indicators. To this end, a number of techniques are being developed, often referred to as “big data analytics” and “artificial intelligence” (AI). These promise faster, more holistic and more connected insights, as compared with traditional statistical techniques and analyses. An increasing number of central banks have launched specific big data initiatives to explore these issues. They are also sharing their expertise in collecting, working with, and using big data, especially in the context of the BIS’s Irving Fisher Committee on Central Bank Statistics (IFC); see IFC (2017).

Getting the most out of these new developments is no trivial task for policymakers. Central banks, like other public authorities, face numerous challenges, especially in handling these new data and using them for policy purposes. In particular, significant resources are often required to handle large and complex data sets, while the benefits of such investments are not always clear-cut. For instance, to what extent should sophisticated techniques be used to deal with this type of information? What is the added value over more traditional approaches, and how should the results be interpreted? How can the associated insights be integrated into current decision-making processes and be communicated to the public? And, lastly, what are the best strategies for central banks seeking to realise the full potential of new big data information and analytical tools, considering in particular resource constraints and other priorities?

Related presentations in this IFC Bulletin covers three main aspects:

• an assessment of the main big data sources and associated analytical techniques that are relevant for central banks;
• the insights provided by big data for economic policy, with an overview of concrete central bank projects aiming at improving statistical information, macroeconomic analysis and forecasting, financial market monitoring and financial risk assessment; and
• the use of big data in crafting central bank policies, including organisational aspects and related challenges.

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