Treasury and cash management
run by new jobs created as a result of the larger and wealthier economy made possible by these new technologies,” claimed the authors of a PwC AI study, aptly titled ‘Will robots really steal all our jobs?’. Meanwhile, professionals across every sector are finding that AI technologies are actually changing their jobs for the better. By automating the mundane tasks they never loved in the first place, AI has freed them up to focus on more creative or strategic pursuits – areas where the human brain has an edge over any machine.
Nowhere is this clearer than in the treasury function – where many repetitive manual tasks are already being performed by AI. You might, of course, think that treasury jobs would be at risk, given the similarities with accountancy. Yet despite the ways automation is changing the sector, serious job losses have yet to materialise.
“The threat does seem to be overstated in the treasury function,” explains Sarah Boyce, associate director, policy and technical, at the Association of Corporate Treasurers (ACT). “Treasury remains an area requiring a combination of deep technical expertise and the ability to communicate complex issues with internal and external stakeholder – areas which automation are unlikely to replace.” In fact, the Oxford/Deloitte study noted that jobs requiring a high degree of social intelligence and negotiating skills were considerably less at risk from machines. The same applies to roles demanding original thinking, or forcing employees to think on their feet.
Boyce adds that treasury teams are generally small with little slack – meaning there simply isn’t scope for slashing headcounts. Rather, the introduction of AI helps teams shift their focus away from routine matters and towards more strategic tasks.
“This enables them to add greater value to the business as, for example, they spend less time on identifying cash requirements and more time on effective funding of the business,” Boyce continues. “It also improves the controls environment, as there is less opportunity for human error.”
Changing functions
This is not to say the shift towards AI hasn’t been profound. The introduction of Robot Process Automation (RPA), which began to hit the financial mainstream around 2015, has enabled treasurers to automate many of the tasks at the lower end of the value chain.
According to Gartner, 80% of finance leaders have implemented, or are planning to implement, RPA. This trend has become firmly entrenched over the past few years, as the role of the corporate treasurer has become more challenging. Faced with a slew of regulatory changes, and the pressures of the
Finance Director Europe /
www.ns-businesshub.com
pandemic, overworked treasurers have been doing all they can to improve efficiency. Simply put, RPA refers to a form of automation software whereby the user defines a set of instructions for a bot to perform. You give the robot some logical rules, and the rest is down to the machine – it will perform the given task unflaggingly, inexpensively, and without making mistakes. To date, RPA’s main applications in treasury have been in the cash management and payment space: cash pooling, data consolidation, invoicing, auditing and so forth. The upshot is that these processes are becoming far more efficient and accurate. Research from EY suggests that RPA can help teams regain up to 70% of the time they would have spent on manual data processes. Increasingly, RPA may be used across more complex functions too. It isn’t quite the same as AI – unlike a deep neural network, it is confined to a particular function and doesn’t learn as it goes. As soon as you want to do something outside of its strict parameters, a human will need to get involved. Even so, this distinction may soon be elided as
RPA tools start to be used in conjunction with AI. Over time, we may see these technologies being used to automate entire workflows.
“Some of the roadblocks include lack of understanding of the benefits; lack of understanding to ask the right questions; identifying exactly how AI can add value to the process; and lack of resources or budget to implement new solutions and processes.”
According to a 2021 report by the research and advisory company Forrester: “Customers want to scale existing bot environments and extend the scope of their automation projects beyond classic desktop- based tasks to more complex processes.” Once machine learning is used more broadly, it could pave the way for a wholesale transformation of the treasury function. Possibilities include better fraud detection, improved pattern recognition, and automated financial risk assessments. A machine learning powered system can also make sophisticated forecasts and draw from huge data sources – including ‘unstructured data’ like social media – to make unassisted decisions. However, the real draw for many treasurers will be having more real-time information at their disposal, so as to improve their own decision-making. In this scenario, the treasurer is performing a strategic, insightful role, albeit assisted by automation.
73 45% IDC
The percentage of repetitive tasks now automated or augmented with AI, robotics and RPA.
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