Treasury and cash management
RPA can help teams regain up to 70% of the time they would have spent on manual data processes.
To date, AI-powered treasury bots have been used in areas like liquidity planning and FX exposure planning. They are also proving useful in cash forecasting, and are particularly good at uncovering discrepancies that might otherwise go undetected. For instance, PNC Treasury Management recently announced a new cash management application that uses AI and machine learning to produce cash forecasts based on historical data.
“Removing complexities and obstacles for ongoing tasks is central to our work, and we know PINACLE Cash Forecasting will be a critical resource for our clients as they continue to operate in disrupted working environments” said Chris Ward, executive vice president for PNC Treasury Management, in a press release.
Roadblocks ahead 50% 74
The percentage of workers who will require some upskilling or reskilling in the next five years.
World Economic Forum’s Future of Jobs Report, 2020
Even so, fully embracing AI is not yet at the top of the priority list for many companies – while many applications remain at a pilot stage. Machine- learning technologies are generally more complex to implement than RPA. They’re also less well-tested, meaning treasurers – a risk-averse bunch by profession – may be reluctant to take the leap. “Investment in Machine Learning remains comparatively low,” says Boyce. “The most popular focus for ML is on cash forecasting and its application to better understand and predict customer sales – especially in sectors such as retail and hospitality.” She adds that AI-based tools are merely enablers to address particular challenges – they do not
automatically suit everyone and many companies will have barriers to implementation. “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.” Another stumbling block might be regulatory concerns. The frameworks around AI are still evolving, and there are questions around accountability and bias that aren’t necessarily easy to resolve. So is now the time to invest in AI? According to many commentators of a futurist bent, that is indeed the case – failing to take the leap will ensure you’re left behind. Yet in practical terms, a certain level of caginess is to be expected, as we are dealing with many unknowns. What seems clear, at any rate, is that treasury teams will spend less time than ever on back-office tasks and more on the meaningful, problem-solving aspects of their role. Corporate treasurers may not lose their jobs – but they will need to become more flexible as their technological ecosystem changes. In the meantime, Boyce advises taking a cautious and measured approach to automation. “It’s important to get the basics right and to standardise existing processes before embarking on any automation programme,” she says. “There is still a lot of development going on in this space and it’s best to wait and see how these settle down into a mature and reliable solution for what remains a business critical function.” ●
Finance Director Europe /
www.ns-businesshub.com
Fit Ztudio/
Shutterstock.com
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