HSBC Corporate Guidebook for Treasurers - Evolving Treasury Trends

56 REPRINTED FROMTMI | TREASURY-MANAGEMENT.COM LIQUIDITYMANAGEMENT: AWHOLE NEWWORLD support, as well as providing the functionality to facilitate this. One example of this might be the development of treasury's own internal thought leadership on the current financial situation of the organisation, along with the potential challenges and benefits of various possible future strategic options. Coupling this with its liquidity management functionality, means that LMP can help treasury to become more effective and efficient in executing both its traditional and new responsibilities. Speed and flexibility The data associated with treasury activities has changed significantly in recent years in the context of time. Bank and clearing systems that once operated on an end of day batch basis, now function in (or close to) real time. This opens the door to faster, better decisionmaking, but only if the available analytical tools can take advantage of streaming as well as historical data. In this respect, LMP's database and user interface technology do not suffer from the limitations of the previous generation of treasury analytics. By taking advantage of modern cloud technology that also embeds extensive big data capabilities, the processing andmining of huge volumes of data in real time could be possible. As a result, treasury's analysis can be far more proactive and timely - as can any resulting actions. Nevertheless, some clients may prefer to have access to the comprehensive data sets underpinning LMP, but use their own tools for analysis (which also corresponds with the move to open banking in Europe as represented by the Payment Services Directive (PSD2)). As a result, HSBC is looking at ways in which the same data accessible via LMPmight be delivered via API into clients' own systems. Data, data, data Liquidity is only manageable if it is visible. Even if that visibility is achievable, for many treasuries it remains a resource hungry process. Traversing multiple bank platforms to download data, emailing subsidiaries for spreadsheets of cash positions, then aggregating and normalising all the resulting information, before trying to analyse, forecast, decide and act. By automatically aggregating the necessary information across investments, cash, liquidity structures and providers, LMP radically improves this situation - but instant data management is only part of the picture. LMP also delivers the tools to give users the best possible insight into their liquidity data. From that solid basis, they canmove on to take the best possible liquidity decisions and actions for their business. New look treasury: intelligence and self-service The coupling of holistic data aggregation and user- configurable reporting tools in LMP results in a digital solution of considerable flexibility. As a result, there is little practical limitation on how treasurers might choose to slice, manipulate and analyse their data across multiple criteria, including regions, countries, banks (or other providers) and currencies, among many others. This means that treasurers armed with LMP's actionable analytics are empowered tomake faster and better decisions. Furthermore, because LMP integrates connectivity with other HSBC liquidity solutions, such as cash pooling and automated investment solutions, those decisions can be immediately translated into action. A key point here is the high level of self service implicit in LMP: many tasks can be directly undertaken from within the environment, without having to contact bank staff to request changes. This autonomy fits well with the way that the role of treasury has changed to a more consultative one that the business looks to for strategic advice. LMP helps treasurers make faster and better liquidity decisions, but in doing so it also frees them up to devote more time to business Treasurers armedwith LMP’s actionable analytics are empowered tomake better and better decisions. Conclusion: future possibilities As it stands today, LMP represents a major step forward in liquidity management and strategic decision making. However, evenmore advanced capabilities may become possible over the next few years. The high quality data management implicit in LMP lends itself well to the use of artificial intelligence and machine learning techniques, where access to robust, comprehensive data is a key enabler for tasks such as automated cash forecasting. A possible further extension of this information-rich environment is that in due course it may also be feasible to build anonymous benchmarking data sets for individual industries or geographies. Therefore, in this and many other respects LMP is not just an exceptional step forward for liquidity management today. It also opens the door to the treasury of tomorrow.