From prediction to action – how AI is changing treasury

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rtificial Intelligence (AI) has two rather different applications in the treasury world: machine learning to improve the prediction of future liquidity, and optimisation of the actions arising. There has been – quite rightly – a lot of focus on the former, but in our view the latter is where the long-term benefits of AI for treasurers will really lie.

“Prediction” means the ability of a treasury team to forecast future cashflows over time with the help of statistical machine-learning algorithms. That timeframe can be short term (less than one month) or long term (typically one to three years ahead). Such prediction is important because successful forecasting enables treasurers to minimise their excess liquidity and then make best use of that surplus in a variety of ways.

However, currently prediction beyond the short term is possible for only a tiny minority of companies. According to research by consultancy IDC, less than 5% of corporates can forecast cash reliably beyond three months, and less than 20% can forecast liquidity beyond one month. That is a particular problem when CFOs are under pressure to increase access to liquidity but have reached the limits of manual cash forecasting and intuitive, human decision making.

Furthermore, those current, limited forecasts are often inaccurate. According to the Association of Chartered Certified Accountants, 90% of Excel spreadsheets contain errors, but over 90% of users are convinced that their spreadsheets are error-free! What’s more, when the owner of the spreadsheet leaves his or her job it is often hard (even impossible) to maintain that spreadsheet. In November 2021, a record 4.5 million workers left their jobs in the US, according to the US Labor Department’s latest Job Openings and Labor summary.

The global economy has also entered a period of price inflation. The all-items index rose 7% for the 12 months ending December 2021, the largest 12-month increase since the period ending June 1982, according to US Bureau of Labor statistics. This situation will inevitably force central banks to restrict access to liquidity, making this resource even more strategic for companies. In this context the ability of CFOs to make the most of their data to optimise liquidity is becoming a major competitive advantage.

The only solution to scale accurate performance is to rely on assistance from AI and to leverage the exponential volume of data accessible to compagnies. IDC forecasts that by 2025 the global datasphere will grow to 163 zettabytes (that is a trillion gigabytes). That’s ten times the 16.1ZB of data generated in 2016. To understand the contribution that AI can bring to prediction there is a useful comparison with weather forecasting. Meteorology has developed from saying whether it is going to rain (or not) on a given day to forecasting the probability of rain at a given time of the day. In a similar way, thanks to AI, treasurers are increasingly able to forecast their group’s liquidity at a particular moment in time based on the probability of the various cashflows throughout the business. Using output from the company’s TMS and ERP systems, AI can analyse historic cashflows, train the algorithm and measure the confidence level of the output predictions.

So far so good. But prediction is only half the story. Once a treasurer has more confidence in the team’s forecasting capability, she can predict how solvent the business will be, then decide how to invest any excess liquidity, whether in traditional Money Market funds or in alternative products such as dynamic discounting (etc). She can also accurately decide the best facility drawdown to finance a cash shortage and optimize payment runs. These decisions have huge potential impact on the company’s P&L as well as its ability to manage risk efficiently, plan its debt issuance programme and allocate to short-term investments

Our internal studies show that with a Liquidity Optimisation tool, a CFO can save up to 50bp of financial cost without compromising access to liquidity. That saving comes from a lower loss-of-opportunity cost on cash deposits; a higher return on financial investments; and a reduction of fees and financial costs on debt facilities. Obviously, the actual gain will vary customer by customer, but there is also a more general benefit. By using AI to solve problems like liquidity forecasting, the treasury team can free up much more time to spend on the business. As AI takes over a lot of routine tasks, treasury professionals can focus on higher value-add and frankly more rewarding tasks!