The Bank for International Settlements (BIS) released a report that details applications and tools of data science in central banking.
The concept of data science refers to the study of data and therefore includes the various techniques for extracting insights from them. Yet data science is fundamentally different from traditional data analysis, as it typically applies to large, complex and/or unstructured information sets.
A key factor supporting the development of central banks’ data science projects in recent years has been the sheer volume and complexity of financial data available in today’s societies. This requires more sophisticated techniques for data management and analysis, a trend reinforced by the new opportunities opened up by artificial intelligence (AI) and machine learning (ML).
Another factor has been the greater focus on real-time, evidence-based policymaking, which requires authorities to rely on better analytical and forecasting capacities to support their decisions. Additionally, advances in statistical computing infrastructure and enhanced training have enhanced the data skills of official sector staff.
These elements have accelerated efforts to advance data science, helping central banks to quickly adapt to the swiftly evolving financial landscape. In this endeavor, the role of data scientists lies at the intersection of three areas: information technology (IT); mathematical and statistical methods; and business, or “subject-matter” expertise.
In addition, BIS and its partners within the Eurosystem, De Nederlandsche Bank and the Deutsche Bundesbank are creating a data platform that has potential to shed light on the macroeconomic relevance of crypto asset markets and decentralized finance (DeFi).
Project Atlas combines on- and off-chain information, creating a layered approach to data vetting and tailored statistics for central banks. A first proof of concept was developed focusing on international flows of crypto assets.
The project report details how the proof of concept uses transactions between crypto exchanges in the Bitcoin network, along with the location of those exchanges, as a proxy for cross-border capital flows. Attribution data links on-chain transactions to crypto exchanges, which are then mapped to their geographical location (where possible).
The derived bilateral flows between countries are visualized on a globe that presents the data in a user-friendly and easily accessible manner. An initial analysis of preliminary data collected by the platform shows that cross-border flows are substantial economically and unevenly distributed across geographical regions.
“Working in the intersection of economics, finance and computer engineering, we are developing a new and important public good for central banks globally. The data on cross-border flows are relevant for areas like payments and macroeconomic analysis,” said Cecilia Skingsley, head of the BIS Innovation Hub, in a statement.
“Atlas enables a variety of use cases. Researchers can structurally analyze the micro data while policymakers can access tailored dashboards for insights at a glance,” said Burkhard Balz, member of the Executive Board of the Deutsche Bundesbank, in a statement.