By Will Towning

Use cases for financial and economic stability

Payments play a fundamental role in our financial and economic systems. From the most humble of activities, such as buying a coffee or catching the underground, to the most complex financial derivative arrangements. Despite this, payments have historically been seen as the rather uninspiring, ordinary financial plumbing that facilitates big business – they are often overlooked and undervalued. But, by its very nature, the data flowing through the pipes can reveal huge amounts about the financial and economic world around us. 

In this series of articles, I hope to share a number of use cases for how technology enables us to operationalise payment data to generate such insights and analysis. Some of the use cases are being executed by leading central banks, financial market infrastructures and commercial banks today, while others are on the horizon of innovation in supervision and liquidity management. 

In this feature, I have chosen three important use cases in action today that highlight the value of large-value payments data in maintaining financial and economic stability: payment system stress testing, bank liquidity monitoring, and supply chain analytics.

These techniques can be used alone or in combination, depending on the requirements of the payment system.

Payment System Stress Testing 

Interbank payments data provides a valuable canvas for examining both the operational and financial stability implications of participant stresses on the system. By mapping out all the transactions into a network of connections between banks and deploying simulation technology (Figure 1), leading central banks are able to analyse what impact a failed participant might have on the liquidity of other participants or of a temporary operational disruption that results in failed payments for a period. 

Interbank Liquidity Monitoring

The same interbank payments data can also be used to monitor bank liquidity and generate early warning indicators of possible liquidity issues. For example, over time, payments data helps create useful behavioural profiles or signatures for each bank in the system. In a real-time environment, authorities are then able to start predicting this ‘normal’ behaviour, and if incoming payments data does not conform, alarm bells will start to ring. 

The global financial crisis and events since have shown us that liquidity risks can proliferate rapidly into wider, more damaging issues. The ability to identify and address these potential undetected liquidity problems before they escalate is vital for maintaining a safe, functioning financial system. 

Moreover, interbank payment data can help authorities understand how liquidity is distributed throughout the system. This information is invaluable during periods of market stress, where defensive liquidity strategies aggravate already adverse conditions and cause liquidity gridlocks. For example, knowing the main conduits and sinks of liquidity provides visibility over monetary transmission and, in turn, helps monetary policymakers design and execute appropriate policy. These policy benefits can also be felt in the financial stability function of a central bank.

Supply Chain Analytics

Payments data also acts as a trail of economic activity as goods and services are traded worldwide. By mapping this trail, authorities can gain a comprehensive view of global supply chains and the interconnectedness of the economy (Figure 2). This is a powerful tool for a number of use cases.

Authorities and businesses are largely in the dark when it comes to the visibility of complex interdependencies of supply chains on a granular level. The information they do have is often not very timely, limiting their ability to react quickly to changes in the economic environment. 

With payments data, authorities can start to understand how companies and sectors of the economy are exposed to supply chains around the world and gain clarity on concentration risks. Furthermore, the information is very timely, providing authorities with an accurate view of the current state to base their decisions on. 

The use of predictive models and stress testing of these payments data supply chains also support better decision-making. Authorities can analyse the propagation of supply or demand shocks in systemically important sectors, helping design appropriate ex-ante responses or preventative policy. 

What’s to come? 

These are just three of several use cases for how payment data is being deployed by the world’s leading authorities for the purpose of financial and economic stability.  In later features, I will be sharing insight into how payments data is being used for advanced nowcasting of economic activity and financial crime analytics. I will also delve into how the introduction of central bank digital currencies will open up a whole new set of opportunities, as well as how payments data analytics and simulation are proving to be valuable tools for driving liquidity cost savings and revenue for banks and infrastructures. And finally, we will explore the opportunities and challenges of the emerging world of synthetic payments data.