Big Data Analytics in Central Banking

NB: We do not resell your papers. Upon ordering, we do an original paper exclusively for you.

NB: All your data is kept safe from the public.

Table of Contents

Introduction
Discussion
Recommendations
Conclusion
Reference List

Introduction

Central banking is supervisory control over a certain state or union’s monetary policies, including currency production and distribution. The institutions that perform it are privileged legally in comparison with commercial banks, which gives unique powers, in one respect, but can cause problems along with that. Thus, central banks have to operate outstandingly large arrays of information, which are not necessarily well-structured; this may interfere with their communication with the audience. Big data analytics, meanwhile, not only provides an unsophisticated solution to this but also can contribute to maintaining financial stability and transforming the entire sphere.

Discussion

Application of Big Data and Analytics to Central Banking

Sentiment Analysis

One of the types of data central banks use is discussion papers, which involve balanced perspectives on certain topical issues. Being based on original investigations and literature reviews, such documents incorporate large amounts of information (European Central Bank, n.d.). In addition, the knowledge on which they rely normally is quite various in forms and sources because it needs regular updating, which, in turn, requires multi-directional research (Bholat, 2015). These nuances, along with the need for a balanced view, can complicate identifying the sentiment of final papers as positive, negative, or neutral. Simply stated, it becomes hardly possible to decipher the authors’ attitude to the topic. Although this reduces bias and, consequently, improves the reliability of the papers, the audience may find themselves unable to understand how exactly the central bank comments on a particular situation.

Meanwhile, sentiment analysis enables assessing and classifying big data arrays according to their emotional polarity. It may utilize several techniques, but the basic principle lies in calculating the percentage of positive and negative verbal markers in the texts (Correa et al., 2017). Analyzing a sufficiently large collection of discussion papers in such a way can provide an adequate summary of how central banks in the region or around the globe regard a certain phenomenon in the economy.

Data Governance Frameworks

It would be relevant to note that the need for optimizing and governing information continues to grow together with its volumes. The never-ending increase in the latter compromises data visibility; in simple terms, finding the necessary evidence requires more and more effort. Along with this, the risk of security issues correlates directly with the intensity of information flows (Tissot, 2018). Therefore, enterprises and institutions, including central banks, have to adopt or design data governance frameworks.

For instance, the US Federal Reserve has a special board, the Office of the Chief Data Officer, of a 4-rank hierarchical structure. Data management, which occurs at the lowest level, is responsible for the quality of the information the organ operates. The stewarding team and the governance committee supervise its use and, in turn, report to the data council, whose members make major decisions (Casey, n.d.). Such a system enables repetitive and constant monitoring of reliability and security at each stage of informational performance.

Assistance with Monetary Policies and Improving Financial Stability

The ever-growing amount and fragmentary character of the data central banks utilize and provide are the reasons why their communication with the audience frequently lacks precision. Notably, according to Banco Bilbao Vizcaya Argentaria (2018), 80% of the broadly available information is “textual or unstructured” (p. 1). Extracting senses from such writings may be challenging, preventing many from understanding monetary decision drivers. Meanwhile, big data approaches, including the above sentiment analysis, enable organizing disconnected pieces into clusters, models, matrixes, and others to identify the prevalent topics and trends of a certain period.

The additional information, for which the techniques of such a kind allow, can be helpful in forecasting economic processes, for instance, inflation or unemployment. Thus, the analysis outcomes may show how the frequency of topic-specific words in the working papers of the central bank or banks changes from year to year (Banco Bilbao Vizcaya Argentaria, 2018). In combination with the positive or negative sentiment, this may simplify the timely identification of unfavorable phenomena and adapting the financial policies accordingly.

The latter allows assuming that from a longer-term perspective, analyzing the reports on monetary decisions, topical discussion papers, and other documents central banks release as big data can help maintain financial stability. Thus, in addition to the above rapid forecasting, it is possible to examine the recent trends in detail by revealing and classifying the connections among the most frequent words (Banco Bilbao Vizcaya Argentaria, 2018). This provides more information in comparison with studying the papers separately for the maximally accurate identification of the current economic needs (Tissot, 2018). Better awareness, in turn, enables quicker and more adequate responses to prevent the negative tendencies from escalating and, consequently, the economy from destabilization.

One of the potentially necessary adjustments may be lowering the policy rate in case of a downturn to avoid socially destructive processes. Among the latter is redundancy, which is a frequent and, normally, the primary consequence of underfunding businesses in crises. In addition, the rate is integral to the overall cost of living, which parameter is in a cause-and-effect relationship with inflation (Muellbauer, 2018). This drives to the assumption that adjusting the policy rate to the current state of the economy as soon as possible helps maintain both inflation and unemployment within their natural limits. Therefore, it is reasonable to state that working with big data can factor positively into sociopolitical climate as well, not solely economic.

Possible Effect of Big Data and Analytics over the Next 5-10 Years

Changing the Internal Operations of Central Banks

It is quite apparent from all of the above that the future of the monetary industry involves its further digitalization. According to Panetta (2021), “the digital revolution is transforming the role and the nature of money,” which inevitably will influence the performance of central banks (para. 2). Thus, the expert highlights that throughout the upcoming decade, the share of online operations most probably will continue to increase, providing even more broadly accessible information.

That doubtlessly would bear a serious threat to cyber security even without big data analytics. With it, the likelihood of misusing the outcomes is substantially higher due to their improved descriptiveness (Tissot, 2018). Therefore, it is reasonable to assume that central banks will take additional measures for protection, adopting strategies similar to the above framework of the US Federal Reserve or even more complex. The boards of such kinds will be in charge of gathering, producing, processing, storing, and distributing data in the most appropriate ways in terms of security.

Changing the Economic and Financial Systems

Regarding the entire sphere of economy, various techniques of big data analysis presumably will help discover more consistent patterns in it, which, in turn, will allow for new approaches to solving the issues. Thus, Muellbauer (2018) reports that forecasting core inflation in the United States revealed a stable link between it and unemployment. Its existence provides a base for innovative strategies targeting a higher level of economic as well as social stability.

Recommendations

In addition to cyber security, big data analytics requires special attention to ethical and reputational aspects. Simply stated, the increased access to the central banks’ views of certain topics bears the risk of misinterpretations and, consequently, the partial or complete loss of public trust (Tissot, 2018). The probability of data misuse, as mentioned, also will rise substantially together with their volume and availability, which can lead to an increase in the appropriation of corporate opportunities, utilizing damaging materials for blackmail, and others. Considering this, it may be reasonable for high-level financial institutions to be more transparent in communication, including discussion papers.

Conclusion

Big data analytics can play an essential role not only in the performance of central banks but also in supporting economic stability. This umbrella term comprises a set of approaches to systematizing and interpreting rich, diverse, and poorly structured information. Financial documents frequently involve such, as they rely on research outcomes, which undergo actualization on a permanent basis. Applying special techniques to analyzing such data arrays simplifies and quickens revealing the most recent trends in the economy, enabling the maximally rapid and adequate response to them to prevent or smooth crises. Although the given practice is associated with certain security as well as ethics-related issues, it provides broad prospects for improving the socioeconomic environment, for which reason the digitization of finance will most probably continue.

Reference List

Banco Bilbao Vizcaya Argentaria. (2018) A Big data approach to understanding central banks. Spain: BBVA Research.

Bholat, D. (2015) ‘Big data and central banks’, Big Data & Society, 2(1), pp. 86-93. Web.

Casey, M. (n.d.) Emerging opportunities and challenges with central bank data. USA, the Board of Governors of the Federal Reserve System, the Office of the Chief Data Officer.

Correa, R., et al. (2017) Sentiment in central banks’ financial stability reports. Web.

European Central Bank. (n.d.) Discussion papers. Web.

Muellbauer, J. (2018) ‘The future of macroeconomics’, in The future of central banking: Festschrift in honour of Vítor Constâncio. European Central Bank, pp. 6-45.

Panetta, F. (2021) The present and future of money in the digital age. Web.

Tissot, B. (2018) ‘How can big data support financial stability work?’, 2018 CIRET Conference – Workshop on Big Data for Economic Statistics: Challenges and Opportunities. Irving Fisher Committee on Central Bank Statistics, Rio de Janeiro, Brazil, 11 September.

Click Here To Order Now!