The sheer quantity of data now available means that it is too much for the human brain to analyse. At the same time, the need for such analysis to be done at high speed has never been greater. Artificial intelligence (AI) can improve the ability to forecast future liquidity, but a more important development will be the ability of AI to support confident decision-making using the output from those predictions.
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 is the latter where the long-term benefits of AI for treasurers will really lie?
‘Prediction’ means the ability of a treasury team to forecast future cash flows over time with the help of statistical machine-learning algorithms. That time frame 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. Research by the consultancy IDC found that 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 their job, it is often hard to maintain that spreadsheet.
Inflation, rising interest rates, supply chain disruptions and FX volatility are continuing to plague the global economy. This situation inevitably forces 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.
A solution to scale accurate performance is to rely on assistance from AI and to leverage the exponential volume of data accessible to companies. IDC forecasts that by 2025 the global datasphere will grow to 163 zettabytes (ZB) – that’s a trillion gigabytes, and 10 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 cash flows throughout the business. Using output from the company’s treasury management systems (TMS) and enterprise resource planning (ERP) systems, AI can analyse historic cash flows, train the algorithm and measure the confidence level of the output predictions.
But prediction is only half the story. Once treasurers have more confidence in the team’s forecasting capability, it can predict how solvent the business will be and then decide how to invest any excess liquidity, whether in traditional money market funds or in alternative products such as dynamic discounting.
They can also accurately decide the best facility drawdown to finance a cash shortage and optimise payment runs. These decisions have huge potential impact on the company’s profit and loss, as well as its ability to manage risk efficiently, plan its debt-issuance programme and allocate to short-term investments.
Kyriba’s 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.
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 more rewarding tasks.
Jean-Baptiste Gaudemet is SVP Data & Analytics at Kyriba