BIS published a report on findings of a survey on use of big data sources and applications at central banks. This 2020 survey from the Irving Fisher Committee on Central Bank Statistics (IFC) was conducted on members from 52 countries or jurisdictions. The survey addressed issues such as the meaning of big data for central banks, increasing use of big data at central banks, key applications of big data projects, and constraints faced by central banks in use of big data. The survey confirmed that big data use is bound to increase, despite the challenges faced by central banks; moreover, central banks have a comprehensive view of big data and use it effectively to support their policies.
The main conclusions from the survey include the following:
- Central banks have a comprehensive view of big data. This includes the large “non-traditional” (or unstructured) data that must be processed using innovative technologies. For two-third of survey respondents, big data also includes large “traditional” data sets that are often “organic,”—that is, collected as a by-product of commercial, financial, and administrative activities— and referred to as “financial big data.”
- Central banks are increasingly using big data. About 80% of the respondent central banks use big data regularly. Moreover, interest in big data at the senior policy level is rated as “very important” in more than 60% of cases.
- The range of big data sources exploited by central banks is diverse. A key source for the private sector is the “internet of things,” with for instance the applications developed by many central banks to scrape online portals for information in numerical or textual format. Yet another important source of information is text from printed materials processed using digital techniques. Central banks are increasingly using financial big data sets collected in a more “traditional” way, such as balance sheet information available in credit registries, loan-by-loan and security-by-security databases, derivatives trades reported to trade repositories, and payment transactions.
- Big data is effectively used to support central bank policies. With respect to the monetary policy and financial stability mandates, newly available databases and techniques are increasingly mobilized to support economic analyses and nowcasting/forecasting exercises, construct real-time market signals, and develop sentiment indicators derived from semi-structured data. This has proved useful in times of heightened uncertainty or economic upheaval, as observed during the COVID-19 pandemic. A majority of central banks also report using big data for micro-level supervision and regulation (suptech and regtech), with an increasing focus on consumer protection.
- Central banks face challenges in use of big data. The survey underscored the need for adequate information technology infrastructure and human capital. Many central banks have undertaken important initiatives to develop big data platforms to facilitate the storage and processing of very large and complex datasets. However, progress has varied, reflecting the high cost of such investments and the need to trade-off various factors when pursuing these initiatives. Moreover, a key challenge is to ensure that predictions based on big data are not only accurate but also “interpretable” and representative enough to formulate an evidence-based policy that central banks need. Furthermore, transparency regarding the information produced by big data providers is essential to ensure that data quality can be verified and public decisions can be made on a sound, clearly communicated basis.
- Cooperation could facilitate central banks’ use of big data. Developing technical discussions between institutions is seen as a powerful way to build the necessary skillset among staff and develop relevant information technology tools and algorithms that are best suited to central banks’ (idiosyncratic) needs. This can be done by collecting and showcasing successful project, facilitating the sharing of experience, and pooling resources together.
- International financial institutions can help foster cooperation in this area. For instance, these institutions can help develop in-house big data knowledge, reducing central bank reliance on big data services providers, which can be expensive and entail significant legal and operational risks. These institutions can also facilitate innovation by promoting technological solutions and initiatives to enhance the global statistical infrastructure. In addition, they can make their resources available internationally or develop joint cloud computing capabilities to reduce operational risk arising from dependence on specific providers in a highly concentrated market.
Related Link: Report (PDF)
Keywords: International, Banking, Big Data, Regtech, Suptech, IFC, Central Banks, BIS
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