In recent years, many corporates have retained significant excess cash as insurance against volatile economic times. Where previously treasury teams may have focused on running lean-as-possible cash balances, they are looking for techniques to optimise returns in this new environment.
Fluctuating externalities such as the pandemic, war, political instability, and jolts in interest rates, have placed a premium on agility. Tactics and strategies that worked yesterday may not work today, and tomorrow is an unknown future. Corporations naturally adapt and evolve over time, and their cash management teams are looking for the same abilities.
New banking technologies, particularly when combined with analytics and artificial intelligence (AI), are creating opportunities for banks to provide innovative cash management services to meet the changing environment.
While larger corporates have reasonably good views of cash flow – and can combine and analyse data from multiple accounts, currencies, and sources – the smaller corporations often struggle to match these capabilities. Companies without internal treasury teams are actively seeking cash management support from their banks. Banks can access macroeconomic information sources, internal market analysts, and aggregate data from thousands of accounts to provide forecasts that are beyond individual clients’ reach.
With historically high cash reserves and a fully informed view of cash flow, many teams are acutely aware of how their investment decisions should also be informed by their corporate environmental, social and governance (ESG) policies. This often includes a commitment to achieving net zero and developing strategies that meet both cash and ESG objectives.
Corporations are familiar with ESG questions related to sustainable supply chains, manufacturing processes, and end-of-life disposal. Shareholders and activists are becoming more interested in how companies manage not only their direct operations but also their indirect operations – such as how excess cash is invested. ESG reporting is now a commonplace in financial statements, with carbon footprint performance, for example. As ESG continues to be emphasized by politicians and consumers, every aspect will be unearthed and highlighted.
Smaller corporates may not have the knowledge or research capabilities to balance these competing demands. Bank’s treasury teams, on the other hand, should know which money market fund providers offer suitable, environmentally appropriate vehicles. The question remains about how to bridge the gap between the knowledge possessed by banks and the desire expressed by corporate clients.
Of course, in many cases banks are also the providers of money market funds and are superbly placed to help their corporate clients make conscious investing decisions, combined with cash flow forecasting. Being able to manage well during periods of cash stress, and to understand and to some degree predict future cash flows, will remain central, but ESG considerations will continue to grow.
This decidedly non-financial influence of ESG will create new demands for cash-flow agility. For example, if the forecast shows a likely cash-flow pinch and cash will be needed in three weeks’ time, banks can recommend the best solution: a short-term loan, a currency conversion from another account, or exit from an investment. Banks – through Open Banking – can collect and consolidate views from multiple accounts and provide information and recommendations that would probably be beyond their clients’ reach.
Historically, customers would log on to the bank portal to review balances, extract data to spreadsheets, summarize global positions, and take a view on next steps. The process generally did not include standardized or automated analysis.
The new message is that layered information and the synthesis of multiple sources offers value and opportunity. Throughout industry and commerce, knowing about the product is becoming as important, if not more important, than the product. In banking, the equivalent might be less emphasis on raw transaction data and more emphasis on the analysis.
Naturally, legacy banking systems were not designed with this kind of heavy metadata in mind. On the other hand, modernisation projects and, in some cases, greenfield implementations, are intended to solve the problem.
The introduction and gathering pace of solutions based on AI and machine learning (ML) are accelerating the pace of change. Again, corporations may be unable or reluctant to invest in AI and ML capabilities, particularly at the early stages where the outcome is not commercially clear-cut. However, banks can develop AI and ML solutions at comparatively low risk, with the prospect of multiple clients to support the central investment.
Without the burden of legacy systems, specialist FinTechs are already invading this space. Using cloud-based software, FinTechs can scale up rapidly and offer AI and ML solutions somewhat out-of-the-box. Their principal weakness is that the solutions work best only when trained against very large data sets, a core resource for traditional banks. For those banks able to modernise rapidly, add new components to their current systems, or vault to the latest technologies, solutions with embedded analytics and AI and ML capabilities will offer large commercial opportunities.
In this emerging landscape, the technology is ready. Finacle – and of course, many other platforms like it – offers all the key features, including embedded analytics, and AI and ML capabilities. Finacle components can be implemented as integrated accessories to legacy systems, or delivered as a complete solution, available as cloud-based software-as-a-service.
To return to the opening theme, corporates will always seek to respond to current conditions as rapidly as possible. From pandemics to politics, ESG to energy threats, the financial market can pivot in just hours. Corporate clients may find their cash position swings from surplus to scarcity, and analytical support from banks could be critical.
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