Big Data Analytics In Central Banks: Insights And Frameworks

Big Data and Central Banks

Banks around the world are making use of extensive data analytics and processes in order to maintain their positions in the market (Mayer-Schönberger, 2013). Big data comprises of sets of complex data that are difficult to process in application software due to the multifarious challenges associated with them. Some basic challenges of such big data includes in capturing, storing, data curation, analysis, sharing, visualization, transfer, updation and so on. The reliance of big data on predictive analytics rather than behavior analytics is immense as data sets grow faster than imagined. The scope of this literature review identifies certain journals that provide insights into big data usage in strategic planning and framework of Central Bank in UK. Central banks have taken active interests in big data as they support in their overall improvement of strategic planning. D. Bholat (2015) in his article, “Big data and central banks.” In the journal Big Data & Society, volume 2(1), 2053951715579469 identified the various ways and means in which big data is used in central banks (Bholat, 2015). At an event “Big Data and Central Banks” hosted by the Bank of England on July 2nd and 3^rd, 2014 various motivations were discussed regarding the likely impact of Big Data in banks going ahead. Banks are striving to expanding their data sources such that data analytics capabilities can be extended. The rise in big data research by banks have grown post financial crisis of 2007-2008 that prompted supervisory and statutory regulators (Beck, 2012). There are various functions that central banks are utilizing for as provided below in analysis of literature journals.  

                       

Figure 1: Central Bank’s Framework

• Information Analysis: Ilzetzki, E.G.Mendoza, and C.A. Végh (2013) article, “How big (small?) are fiscal multipliers?” In theJournal of monetary economics, volume 60(2), pages 239 to 254 (Ilzetzki, 2013). This journal evaluates the Strategic Planning and Framework that is accommodated and is developed by way of including Big Data. Central Banks have been found to have immense interests in studying and in analyzing of Big Data, that can be seen by ways they include institutional strategies, process technologies and are increase staff training for the same. While many banks view Big Data as unstructured external data this view is not prominent amongst other prominent school of thoughts (Chen, 2012).  

                       

Figure 2: Classification of Big Data

G.H.Kim, S.Trimi and J.H.Chung (2014) article, “Big-data applications in the government sector.” In the Communications of the ACM, volume 57(3), pages 78 to 85 evaluates the various application areas for Big Data (Kim, 2014). Banks collect large amounts of external data from market and various other research sources. Such extensive data are analyzed using data analytics and yield valuable information regarding market, industry situations as well as customer preferences. As during financial crisis period such data analytics reflected rise in interests rates of money market instruments as CPs, CDs and other papers. While data analytics also reflected that such interests rates were overvalued in accordance to current market situations and GDP growth rates. Central Banks have created their own platforms that can handle collection of data and flow of information which is a new process that is recently adopted post financial crisis. Such influences have been undertaken in order to consider financial stability and reduce impact of bank holding assets (Corsetti, 2012). Big Data remains critical and useful for research purposes for banks strategic planning and framework development, especially in the domain of policy making. However, in order to accommodate for such Big Data there needs to be support from policy makers, impending which such analytics cannot be included for strategic planning frameworks. Increased inflow from various data warehouse is led into processing for arriving at decisions related to various investment options for banks. During Brexit there was data analytics that reflected fall in interest rates for Central Banks in UK due to sentiments of rise in employment and lowering of inflation in UK.

Application Areas for Big Data

                         

Figure 3: Central Bank Strategic Planning Framework using Big Data

Managing security threats: S.Claessens, R.Herring, D.Schoenmaker and K.A.Summe (2010) article, “A safer world financial system: Improving the resolution of systemic institutions.” In the International Center for Monetary and Banking Studies (Claessens, 2010). Post analysis and application of data that is extracted senior level managers accommodate the same into their decision making as it directly hampers or impacts profitability of the Company. Though the aims of central banks remain to incorporate such high levels and complex data structures, yet they lack dedicated budget for handling such infrastructural costs. Research conducted on application reflects that more than 85% banks does not have intra-departmental or bodies that can cater to big data. Central Banks want to apply Big Data within their scopes but they have relative concerns and are mostly busy in managing data within their organizations (Nyman, 2015). There are relatively more concerns in encompassing data governance over banking infrastructures such that they can be managed dynamically without any security threats. Big Data captures various information and analyses them to recognize pertinent threats for the banks that exists in the environment in regards to hacking and other threats.

                         

Figure 4: Organization of Big Data

• Catering to competitive products: Committee on International Economic Policy and Reform (Washington, DC) and B.J.Eichengreen (2011) article,“Rethinking central banking.” In the Washington, DC: Brookings Institution (Committee on International Economic Policy and Reform (Washington, DC), & Eichengreen, B. J., 2011). There are several banks that are already using big data and informatics for yielding greater benefits as against pay. Banks are bent on increasing their profitability to give better scopes and incomes to their customers by means of arbitrage. As mortgage markets continues to crash and go below expected levels there is a high scope for other products that finds its suitability amongst customers. Markets from various corners of the world especially developing ones are attracting a great deal of opportunities are identified using strategic frameworks of big data analytics (Kitchin, 2014).

                                   

Figure 5:Challenges in Using Big Data

• Decision Making: Banks in general have shared internal platform for reporting frameworks and data warehouses. There is an increasing number of monitoring while banks source their own data, while they process the same. The best ways that central banks gain from collection and analysis of such Big Data is by incorporating them to form monetary policy though there are various debates that there might be impacts on macro-prudential policy also(Davenport, 2013). Central Banks have their own data platforms for dealing with regulatory data analytics that are used along with other options. Obstfeld, J.C.Shambaugh and A.M.Taylor (2009) article, “Financial instability, reserves, and central bank swap lines in the panic of 2008 (No. w14826).” In the National Bureau of Economic Research (Obstfeld, 2009). Central Bankers mostly make use of Basel Committee on Banking Supervision’s principles for supervisory data aggregation. Several central and other banks have adopted technology upgrades that are focused big data infrastructure that includes warehouse projects. Almost as high as five Central Banks Heads have made reference to Big Data while commenting regarding their strategic planning initiatives. Central Banks are taking broader strategic initiative to arrive at better decision making and approaches. Banks globally are increasing their data flows and information strategies such that there can be a secular rise increase in pace of data. Various unstructured data sets are used now for functioning of projects.
• Forecasting: Earlier banks collected data for their daily transactions and for storing them into databases. Data traditionally was used for purpose of keeping track of transactions or forecast needs. There has been a boom and revolution in data collected in recent times that allows click-stream data as banks can now retrieve any possible information regarding any particular bank, markets, consumers and so on. Big data has been defined by a number of banks as a way and method for doing big business. Big Data can be of two types primarily as transaction and non-transactional in nature. Banks are utilizing such diversified large data types for their performance management, data exploration, social analytics, and decision sciences and so on. They are drawing from Big Data using scopes of Business Intelligence or Online Analytical Processing for their complex infrastructure and handling multidimensional data. Such BI systems are allowing banks to take maximum advantage of their arbitrage position and extend a hedge situation to its various customers. Research and review made pertaining to Big Data that are incorporated in Central Banks have specified new strategic approach to analytics and Big Data. While banks are handling complex set of data there is a shift in analyzing such data. Though Bank’s analytical toolkit provides an improvised operational efficiency to maintain financial and monetary stability. Increasing usage of Big Data can be identified as conjuncture phenomenon that is given immense focus on due to recent financial crisis and structural shift due to technological advancements from increased capacities and communication capabilities. A large number of contemporary banks are offering tailor made suitability to customer offers such that early detection of frauds and fraudulent practices can be detected. Such processes will also allow for integrating in better strategic fits for the organization. While social media are taking integrative approach towards innovative risk models in their strategic planning framework there might be some positive impacts linked to long term financial stability. Big Data might provide boosts to overall banking systems by enhancing productivity by directing work towards appropriate directions. Further it might allow to lower costs associated with unnecessary wastages of resources on inappropriate things. Some researchers have identified scopes of big data intervening on business models as well as profitability but there are greater associated impacts of the same towards real economy, wealth distribution, in forecasting and measures(Wyplosz, 2012). Impacts of Big Data have been found to be tremendous on internal operations as well as on external economic and financial systems for the future. Thus, while there is an increased incidence of costs factor associated with big data there can be greater future benefits arising from the same.                

Reference:

Beck, T. 2012. Banking union for Europe: risks and challenges. . London: Centre for Economic Policy Research.

Bholat, D. 2015. Big data and central banks. . Big Data & Society, 2053951715579469.

Chen, Q. F. 2012. International spillovers of central bank balance sheet policies.

Claessens, S. H. 2010. A safer world financial system: Improving the resolution of systemic institutions. International Center for Monetary and Banking Studies. 

Committee on International Economic Policy and Reform (Washington, DC), & Eichengreen, B. J. 2011. Rethinking central banking. . Washington, DC: Brookings Institution. 

Corsetti, G. &. 2012. Has austerity gone too far?. VoxEU. org, 2.

Davenport, T. H. 2013. Big data in big companies. . International Institute for Analytics, 3.

Ilzetzki, E. M. 2013. How big (small?) are fiscal multipliers? Journal of monetary economics, 239-254.

Kim, G. H. 2014. Big-data applications in the government sector. Communications of the ACM, 78-85.

Kitchin, R. 2014. The real-time city? Big data and smart urbanism. . GeoJournal, 1-14.

Mayer-Schönberger, V. &. 2013. Big data: A revolution that will transform how we live, work, and think. . Houghton Mifflin Harcourt. 

Nyman, R. G. 2015. News and narratives in financial systems: exploiting big data for systemic risk assessment. BoE, mimeo.

Obstfeld, M. S. 2009. Financial instability, reserves, and central bank swap lines in the panic of 2008 (No. w14826). . National Bureau of Economic Research.

Wyplosz, C. 2012. The ECB’s trillion euro bet. VoxEU.org, 13.