BIS Examines Use of Big Data and Machine Learning at Central Banks
BIS published a paper that provides an overview on the use of big data and machine learning in the central bank community. The paper leverages on a survey conducted in 2020 with responses from 52 central banks worldwide. The paper examines how central banks define and use big data, as well as which opportunities and challenges they see. Institutions use big data and machine learning for economic research, in the areas of financial stability and monetary policy, as well as for suptech and regtech applications. Data quality, sampling, and representativeness are major challenges for central banks and so is legal uncertainty around data privacy and confidentiality. The paper notes that cooperation among public authorities could improve central banks’ ability to collect, store, and analyze big data.
Among other challenges, several institutions report constraints in setting up an adequate IT infrastructure and in developing the necessary human capital. Overall, the analysis highlights that central banks define big data in an encompassing way that includes unstructured non-traditional as well as structured data sets. Central banks' interest in big data and machine learning has markedly increased over the last years. In 2020, over 80% of central banks report that they use big data, up from just 30% five years ago. Among the institutions that currently use big data, over 70% use it for economic research, while 40% state that they use it to inform policy decisions. These numbers suggest that big data and machine learning offer many useful applications and can help central banks in fulfilling their mandate. The vast majority of central banks are now conducting projects that involve big data and the central banks are willing to join forces to reap the benefits of big data. Indeed, half of them reported an interest in collaborating in one or more specific project, with three types of cooperation envisaged:
- By sharing knowledge among those institutions that have developed specific expertise that can be reused in other jurisdictions. These expertise include general big data techniques (for example, data visualization, network analysis, machine learning tools), more general information management issues (for example, development of open-source coding, data-sharing protocols, encryption and anonymization techniques for using confidential data) as well as specific applications that are more devoted to the central bank community (for example, suptech and regtech areas).
- By using big data to work on global issues such as international spillovers, global value chains, and cross-border payments.
- By developing joint exploratory projects to benefit from economies of scale and collectively share (limited) financial and human resources.
International financial institutions can greatly support these cooperative approaches. They can facilitate innovation by promoting technological solutions to harmonize data standards and processes among jurisdictions. With this spirit, the BIS Innovation Hub has been established to identify and develop insights into critical trends in financial technology of relevance to central banks, explore the development of public goods to enhance the functioning of the global financial system, and serve as a focal point for a network of central bank experts on innovation. Such a network could undoubtedly play an important role in facilitating international cooperation to exploit big data sources and techniques.
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Keywords: International, Banking, Big Data, Regtech, Suptech, Machine Learning, BIS
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