Why fragmentation of the global data supply chain poses risks to financial services

Matthew Osborne and David Bholat

Every minute of the day, Google returns over 3.5 million searches, Instagram users post nearly 50,000 photos, and Tinder matches about 7,000 times. We all produce and consume data, and financial firms are key contributors to this trend. Indeed, the global business models of many firms have amplified the data-intensity of the financial services industry. But potential fragmentation of the global data supply chain now poses a novel risk to financial services. In this blog post, we first discuss the importance of data flows for financial services, and then potential risks from blockages to these flows.

Big Data and financial services

One of the earliest known examples of a financial transaction is a clay tablet in the British Museum recording a loan of silver in ancient Babylon. Today that loan would be recorded in bits and bytes on computers.

In recent years there has been a huge increase in the use of data in financial services, reflecting underlying shifts in both supply and demand. On the supply side, the rapid growth in the amount of data available reflects the digitisation of financial transactions over the past thirty years. Growth in the stock of financial data has been enabled by technological advances that have increased storage capacity and lowered computational costs.

Meanwhile, on the demand side, more efficient and insightful use of data is seen by many financial firms as a means of increasing revenues and reducing costs in an increasingly competitive and low yield environment — demand which has been stoked by the desire to emulate the success of Silicon Valley firms in exploiting Big Data, and further stimulated by leading consultancies.

Central banking is also becoming more data intensive. Broadly, the Bank of England receives two main types of quantitative data from regulated firms. The first type is data submitted through statistical and regulatory returns. The number of data points received through these returns has grown an estimated five-fold since the financial crisis (Figure 1) largely because the Bank has since acquired micro-prudential supervisory responsibilities. Second, and separately, regulators also have access to more granular data on specific types of financial agreements and transactions. Here too there has been growth since the financial crisis. In the case of data on the UK derivatives market, where each contract is reported to trade repositories, this sums to more than one billion rows of data every month.

Figure 1: Estimated number of data points (distinct cells) collected through Bank of England statistical and regulatory returns from firms per annum 

Source: Future of Finance Report

The global flow of data

Besides the increasing mass of financial services data, another striking feature is the increasingly global span of its circulation. This has been driven by three factors.

First, global financial services firms maintain central hubs for storing and analysing data generated by their cross-country operations. They maintain data centres in these hubs in order to store and process it in a consistent and secure way, and have it analysed by specialist teams. The UK is a leading hub for data centres (eg see case study on p.15 of this report).

Second, it is common for financial services firms to outsource elements of their businesses and transfer data to and from these third parties. There is a global market for these vendors, and a wide range of functions are provided, especially back-office functions such as HR and accounting services, as well as specialised risk management and product development activities. In some cases, firms ‘insource’ these functions to intra-group entities operating in lower-cost jurisdictions. 

Third, financial services firms often need to transfer data globally to service customers. For example, international payments require the transfer of personal data between sending and receiving banks. More generally, global financial services firms need to transfer data across borders between their various subsidiaries and branches as a matter of course. They also need to service customers when they are abroad.   

Risks from barriers to cross-border flows of financial data

Cross-border data flows bring financial benefits. A McKinsey report in 2016 estimated that the flow of data across borders contributed $2.8 trillion to the global economy, compared to what would have occurred in a world without any cross-border flows. McKinsey predict that this figure could reach $11 trillion by 2025. In the financial sector, cross-border data flows can bring benefits to UK consumers. For example, cost savings from locating data management functions in jurisdictions with comparative advantages in them may be passed on to consumers in the form of lower cost financial services.

However, there are an increasing number of legal and regulatory obstacles that may slow the flow of cross-border data sharing (Figure 2). These restrictions run the gamut, from outright bans on certain kinds of data transfers with particular jurisdictions; to permitting the sharing of data, but only on condition that recipient jurisdictions meet standards established by the sending jurisdiction; to requiring that a local copy of the data always also be stored within the jurisdiction where a firm is headquartered. The rationale for these restrictions are also varied, ranging from  concerns about protecting privacy and personal data, to preventing commercial competition — so-called ‘digital mercantilism.’

Figure 2: Cumulative number of restrictions on cross-border data flows

Source: European Centre for International Political Economy

A number of studies have shown that disruption to data flows could harm economic growth and reduce the productivity of firms in affected sectors. Moreover, we think that restrictions on cross-border data flows may pose particular risks to the financial sector.

More generally, we think disruption to the free flow of data could pose at least three key risks to financial services.

The first is that these create operational frictions in the movement of information across subsidiaries and branches within financial services firms. These could lead to delays and disruptions in the timely execution of financial services for consumers, crystallising into operational and financial losses. A wide variety of activities could be affected including insurance, and bank accounts and, lending, cross-border payments, know your customer / anti-money laundering / counter-terrorist financing measures, risk management, compliance, internal fraud detection, cloud computing and international settlement of securities transactions.

The second risk from barriers to cross-border data flows is that they may impede the sharing of information between regulators. Although memorandum of understandings exist between different regulators, in future, any new restrictions on information sharing between regulators in different jurisdictions is likely to be particularly deleterious in a financial crisis, as in 2008, when access to granular information was sometimes required to facilitate mergers and acquisitions, or the recovery and resolution, of firms.

Finally, a third risk is that such restrictions inhibit innovative technologies that may improve the efficiency of financial services. For example, blockchain technology works by instantly and automatically updating ledgers across networks that might be global in scope. Such technology can reduce transaction costs. However, those gains can only be made if there are not barriers to the fast and free flow of information across distributed ledgers.


Although the enabling data technologies have changed over centuries, financial services has always relied upon systems for documenting and sharing information. Today, in the era of Big Data and global business models, the link between finance and data is especially strong. Risks to the effective sharing of data are therefore risks to the effective functioning of the financial system as well.

Matthew Osborne works in the Bank’s Sterling Markets Division and David Bholat works in the Bank’s Advanced Analytics Division.

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