Published: April 23, 2018
Liquidity management is undergoing a period of major change. Liquidity is (re)circulating more quickly, major opportunities are emerging in ‘big data’ analysis and the global economy looks set to push liquidity levels higher. Nick Powell, Global Head of Commercialisation and Ray Suvrodeep, Global Head of Deposit and Investments Product Management, Global Liquidity and Cash Management at HSBC examine some of the opportunities arising from this situation and how treasuries might best maximise them.
Liquidity is moving faster. Innovations such as instant payment systems are seeing clearing cycles shrinking to the point of being almost instantaneous. At the same time, the advent of mobile payments is triggering 24x7 cash flows at far higher frequencies. Furthermore, this acceleration doesn’t just apply locally: various SWIFT initiatives mean that it also pertains to cross-border and global flows as well. The net result is that the velocity of liquidity is increasing and is likely to continue doing so.
In general terms this may be beneficial to corporations, as their cash conversion cycles diminish, along with latency in their payment execution and the amount of contingency cash buffer needing to be held in their bank accounts. This could result in less pressure on cash flow planning and forecasting, as well as assisting working capital efficiency.
On the other hand, this new faster liquidity environment also throws up some challenges for treasuries. For treasurers, getting cash to the right place, in the right currency, at the right time, is a fundamental task. However, as payments become faster, outflows also accelerate, so the response times for fulfilling this obligation become more demanding.
A major obstacle here is liquidity fragmentation. An efficient method for viewing and mobilising liquidity is an absolute must for addressing this. Fortunately, such a method now exists in the form of the next generation virtual account (ngVA).
In addition to the traditional advantage of virtual accounts – the ability to improve reconciliation rates by giving customers their own dedicated account details to pay to – ngVAs also include a self-service element. This enables clients to open/close virtual accounts quickly to suit their immediate needs, such as to track balances and transactions for respective customers/ entities. This is faster and more efficient than opening physical accounts individually and managing/reconciling them on an ongoing basis.
When using ngVAs, all the liquidity is automatically concentrated in the single physical account that heads each group of virtual accounts. This makes rapid liquidity mobilisation far easier to achieve than if multiple physical accounts were involved. Essentially, ngVAs address some of the most fundamental liquidity fragmentation challenges by supporting speed of execution, as well as providing an architecture that more generally facilitates high speed liquidity management.
Higher velocity liquidity opens the door to the challenge/ opportunity of intraday cash forecasting and liquidity management. On the one hand, this is concerned with making sure that the most effective use is made of internal sources of liquidity throughout the day. On the other, it involves managing any external sources of credit on a cross-bank basis. Given that cash is recirculating faster, this involves balancing a situation where there may be relatively few liquidity banks versus payment banks involved.
Dealing with this effectively in a real (or near real) time environment makes access to similarly timely data essential. Some treasuries already poll their banks for intraday statements. Some banks already offer an even better real time alternative to this, whereby data is streamed to the client continually. While this offers a great opportunity to improve real time liquidity forecasting and management, it comes with the caveat that the client’s technology infrastructure must obviously be capable of capturing and processing the resulting data stream.
The application of specialised liquidity management techniques and functions (partly in response to higher liquidity velocity) is contributing to the generation of valuable data that can be analysed to enhance treasury-specific processes, as well as developing more general business intelligence. In view of the progress made by many corporate treasuries in recent years, this is a particularly timely development. For some time now the primary goal for many treasurers has to been to achieve visibility and control of all corporate liquidity. While this may not be technically possible in some cases, a growing number have nevertheless already achieved this, or made major steps forward. As a result, they are now able to take advantage of the next stage in optimising liquidity management: using that visibility and control to deliver insight and effectiveness. Armed with the right data and tools, this is now achievable.
Recent years have seen rapid developments in the science of big data analysis. These make it possible for treasuries to analyse their own data for tasks such as cash forecasting (see sidebar “Revolutionising forecasting”), as well as to gain more general insight into potential liquidity risks and opportunities. Innovations in artificial intelligence (AI), such as deep neural networks, mean that treasuries will in future be able to detect these risks and opportunities quickly and efficiently within huge volumes of data – a task that would be impossible for a human analyst.
However, analysing just the corporation’s own transaction and liquidity data is just the first step towards all that is actually possible. As a result of their day to day role, major transaction banks have access to vast quantities of external data, such as cash flowing through the banking system. The most innovative of these banks are looking to make this available to clients so that they can analyse and benchmark their performance in areas such as working capital and liquidity management.
In this respect, it is important to emphasise the distinctive nature of this data: it is real transaction data, not just survey or sample data. While this makes it possible to draw far more robust inferences, a key point is the degree of depth and scope of the data that is available for analysis. A major transaction bank with a global network will be able to provide clean banking system data from around the globe in considerable depth and granularity, which will help to support statistically significant analytical output.
Nevertheless, attractive as this opportunity is, corporate treasuries are highly unlikely to have the budget or inclination to build their own platform for warehousing and analysing this data. They need a suitable interface through which they can access it that also provides the necessary analytical tools. For instance, HSBC has plans to introduce much of this functionality through a new liquidity management portal, which is currently in development.
The availability of better liquidity insight and forecasting is also a timely development in a world where liquidity levels are likely to be rising. There are currently signs that a period of global macro growth is getting underway. The US and China are two major engines here, with current GDP growth of ~3% [1] and 6.8% [2] respectively. The Eurozone also appears to be recovering to the extent that the European Central Bank is talking of ceasing its quantitative easing programme [3. Many emerging markets are also performing well, with Malaysia seeing GDP growth of 5.8% [4], Vietnam 6.4% [5] and the Philippines 6.5% [6]. Therefore, 2018 could see robust growth coupled with low inflation.
From a treasury perspective, this is likely to result in higher liquidity flows. However, in a strong economic environment there is an incentive to invest in businesses, possibly across multiple markets. Therefore, there is a need to be able to cover funding demands that could arise in multiple parts of the world in multiple currencies. In addition, in a stronger macroeconomic environment, existing businesses may be generating higher levels of surplus cash. So treasury may have to manage substantial irregular multiple global cash deployments, alongside a continual supply of revenue generation.
Managing this situation requires an efficient deployment model that is well diversified. In addition, currency risk needs to be minimised and the tenor profile of investments must be able to accommodate sudden calls to cover investments/acquisitions.
At present, many on balance sheet bank deposit products tend to fall broadly into instant-access/overnight or term categories. In an environment where treasurers are expected to maximise return, while still retaining sufficient liquidity agility to cover possibly numerous acquisitions, something that fits between these two existing poles is clearly desirable. For example, a suitable notice account product available across multiple markets would give treasuries an additional yield versus liquidity option, when compared to a typical vanilla term deposit.
For some treasuries, rising liquidity levels may result in issues related to counterparty limits specified in their treasury investment policy. They may simply run out of available appetite for bank balance sheet. While this is less likely to be an issue with counterparty banks with stronger credit ratings, it nevertheless creates demand for solutions that efficiently integrate on and off balance sheet investment opportunities. HSBC’s Liquidity Investment Solutions (LIS) deliver this by providing automated investment of cash above a client-specified trigger level with a range of approved third party asset managers. Automated redemption based on a minimum balance trigger level or risk limits on third party managers is also supported.
While various products and solutions are available to help treasuries cope in the current shifting liquidity environment, these in isolation are insufficient. If global treasuries are to maximise their liquidity opportunities, the overriding need is for a bank service proposition that binds products and solutions into a single consistent experience, regardless of technology, location, currency or investment tenor. Anything less than this is palpably inefficient in the eyes of lightly-resourced treasuries that are perpetually expected to do more with less.
The current environment is a good example of this high level of expectation. However, while higher liquidity velocity, big data and stronger macroeconomic growth may seem a daunting prospect, for the most innovative treasuries they individually and collectively represent an important opportunity. More efficient use of working capital, better business intelligence/forecasting and enhanced yield on surplus cash are just some of the potential benefits available.
Big data analytics create opportunities for treasurers to re- engineer their cash forecasting processes completely. Common practice today is to send out spreadsheets to business units asking them to fill in their projected cash flows. These are then aggregated at a central treasury level to produce overall forecasts. This is labour-intensive, slow and for practical reasons can only be done periodically. It is also heavily dependent upon the varying skill and experience levels of those making the individual projections.
Given the right big data and analytics, this bottom-up cash forecasting process can now be replaced by something far more efficient. Recent advances in artificial intelligence (AI) mean there is no reason why techniques such as deep neural networks cannot be applied to tasks such as cash forecasting, where human judgment and intuition based on previous experience are currently key factors in making forecasts. If AI can replace all or most of this manual process, then clearly cash flow forecasting as we know it today could change fundamentally. The value created could be substantial, not just in operational cost/effort saving, but also in automating and optimising downstream treasury decision making and processes.
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