Efficient cash flow forecasting is critical to effective liquidity risk management at any time, but the effects of COVID-19 have heavily underscored this point, as well as adding a further measure of urgency. However, while the economic disruption from the virus has greatly increased existing liquidity management challenges for finance functions, the right cash forecasting solution can materially assist in addressing these. Ray Suvrodeep, Global Head of Deposit and Investments Product Management and Scott Samson, Liquidity and Investments Commercialisation Director, Global Liquidity and Cash Management at HSBC examine the requirements this solution must satisfy if it is to deliver the optimal forecasting performance for finance departments under the most demanding circumstances.
Simplicity and reliability
Around the globe, companies of all sizes are striving to improve their liquidity forecasting/planning in order to support strategic objectives, such as business resilience or enhanced shareholder returns. In many companies there is still significant scope for improvement in these areas, with highly manual processes that cannot deliver a satisfactory reporting output still commonplace. Given that most finance organisations need to forecast across multiple time frames, from as little as a few days to perhaps several years, this situation is clearly less than ideal.
The still common practice of emailing spreadsheets around the company for the collection of local cash forecasts is neither simple nor reliable. At the other end of the spectrum are extremely sophisticated solutions, which while they may be more reliable than a spreadsheet, are costly and complex to implement and resource-intensive to maintain. Therefore, any new technology that is going to materially improve on these scenarios must be able to deliver simplicity and reliability. In the context of COVID-19, it also needs to be the sort of digital-ready, cloud-hosted tool that enables finance personnel to collaborate remotely, while still all working from the same consistent forecast.
One of the most important elements in achieving this is having all the right data available for analysis. This is often an area where many more sophisticated bolt-on solutions fail, because of the cost and complexity of integrating data on a continuous basis from bank balances around the globe. By the same token, the commonplace spreadsheet alternative of manually collating data is massively inefficient and slow.
By contrast, a cash forecasting solution that is available as a pre-integrated component of an existing electronic banking application helps mitigate these data issues because historical data is immediately available via the application. Historical data is not only useful for traditional use cases such as variance analysis, it also enables mining of the data for historical trends, which could be instructive for predicting future cash flows - particularly those more repetitive in nature, such payroll, rent and statutory payments. In the case of the most sophisticated banking applications, this even applies to all of a company’s bank balance and transaction data as well, which for convenience can be imported via a range of methods. A further advantage of this approach is its flexibility. If in the future the company decides to rationalise some or all of its other accounts to its primary bank, the balance data for the new accounts needed for forecasting will still be immediately available via its banking platform.
There is also a potential consolidation benefit here in relation to the move towards open banking. If a company can consolidate its balance information from all its banking relationships with a single trusted partner, then that partner’s electronic banking platform becomes the natural port of call for day-to-day balance enquiry and reconciliation, as well as cash forecasting.
This streamlines workflow by delivering scope economies.
Extensibility and flexibility
While many companies strive to centralise cash globally as much as possible, for some - such as those with entities in restricted markets - this centralisation will always be constrained. In other situations, companies may prefer as a matter of policy to grant some individual local autonomy, while still giving the global finance department overall visibility and risk management.
In both these situations, a forecasting solution that provides flexible multilevel authentication with centralised control of each user’s level of visibility clearly adds value. In this way, local/regional operations can upload and manage their data, while also giving the global finance function visibility of it. More generally, it places no constraints on how the company chooses to manage its forecasting, so this can be done only at a central level, or devolved downwards to whatever level desired.
This flexibility can be invaluable for situations such as where individual entities wish to anticipate and prepare for any forthcoming liquidity requirements, without having to rely on HQ or the regional finance organisation to give them the necessary information/analysis. Similar flexibility is also needed if the cash forecasting solution is to be extensible enough not to impede a client’s business as it grows in size and complexity. In this fashion mid-sized companies can use the same familiar solution as they evolve and expand.
The need for better cash forecasting is longstanding, but the current environment has made this need more pressing - for some organisations perhaps even an existential concern.
Machine learning and artificial intelligence
Machine learning (ML) and artificial intelligence (AI) have attracted enormous attention in recent years. Over longer time frames, cash flow forecasting is notoriously difficult and certain cash flow items are also inherently unpredictable. Nevertheless, AI/ML can still add predictive forecasting value, where cash flows display patterns of varying degrees of intensity and persistence. Furthermore, the benefits AI/ML can bring to cash forecasting vary widely depending on business type. For instance, companies with strong consistent seasonality in their cash flows may find AI/ML already offers a significant forecasting edge.
AI and ML are still evolving exceptionally quickly and seems likely that their applicability and value to cash forecasting will continue increasing, for both data processing automation and actual predictions. Therefore, as new advances in AI/ ML emerge that have the potential to improve forecasting performance, the cash forecasting solution needs to be sufficiently flexible to incorporate them quickly.
Risks, costs and urgency
As mentioned at the outset, COVID-19 has exacerbated the liquidity management challenge for finance organisations and, given the revenue impact on many businesses, increased the urgency of deploying efficient cash forecasting. Now, more than ever, companies need to have the clearest possible vision of their potential cash needs across multiple horizons.
In this situation, speed of implementation is obviously an imperative, which means that a forecasting solution already embedded within a global electronic banking system has a major advantage. Unlike an external solution, there is effectively no technology implementation per se, simply the enablement of a functionality already present. While an external solution might (eventually) be able to deliver similar functionality, it comes with the appreciable additional costs, delays and risks of an implementation project.
A cash forecasting solution that is already integrated within an electronic banking environment has additional benefits when it comes to user training. Much of the interface will already be familiar to existing online banking users, making remote training and support via secure session both more productive and immediately accessible1.1
The need for better cash forecasting is longstanding, but the current environment has made this need more pressing - for some organisations perhaps even an existential concern. Under those circumstances, a fully-integrated forecasting solution that can be made immediately available without additional implementation overhead is extremely valuable. If it also has a flexible and extensible architecture that enables multilevel deployment, while simultaneously facilitating the addition of new functionality, then it presents the opportunity of robust, future-proofed, cash forecasting. This will add considerable value - both in the current highly challenging conditions, as well as more generally - by supporting more effective risk management and facilitating growth.
For more information on how HSBC can help meet your needs please contact your local HSBC representative or visit gbm.hsbc.com
1.No need for physical presence of trainer
Published: June 2020
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