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The Truth About Treasury and Artificial Intelligence Despite initial industry scepticism, treasurers are beginning to get acquainted with artificial intelligence’s (AI) potential use cases. Nikolai Diekert, BELLIN, looks beyond the hype to provide a candid view on where the technology can add value.

The Truth About Treasury and Artificial Intelligence

The Truth About Treasury and Artificial Intelligence

By Eleanor Hill, Editor


It’s been a hot topic for almost every treasurer over the past year, but – hype aside – what is the true value of artificial intelligence (AI) within treasury? Nikolai Diekert, Director Product Management at leading TMS provider BELLIN, explores the concrete use cases for AI in treasury, providing a candid view on where the technology can add value and where it still has room for improvement.

 

Eleanor Hill, Editor, TMI (EH): Before we talk about AI in treasury, it would be helpful to clarify what AI is – and what it isn’t. Would you be so kind?

Nikolai Diekert (ND): Of course. You’re right – there are many contradicting and confusing definitions of AI. A good first step is to split the definition into ‘artificial’ and ‘intelligence’. What we mean by ‘artificial’ is that it is non-human, but created by people. While we tend to think of modern computer devices in this instance, analogue machines also fall into the ‘artificial’ bracket.

As for ‘intelligence’, there are even more definitions of this than there are for AI! But there is an interesting and useful definition on Wikipedia, which states that it is the ability to ‘perceive or infer information, and to retain it as knowledge to be applied towards adaptive behaviours within an environment or context’. Taking these elements together, AI can be viewed as any device that perceives its environment and takes actions that maximise its chance of successfully achieving its goals.

You also asked what AI is not, though, and this is very important. We often hear the words ‘machine learning’ (ML) uttered in the same breath as AI – but these are not one and the same. ML is a subset in the field of AI whereby algorithms build a model based on sample data and perform tasks or make decisions on real data without being explicitly programmed. The model is ‘trained’ on the sample data in various ways, supervised, unsupervised, reinforcement, self-learning and so on.


EH: Isn’t ML the scary part of AI – the bit that makes treasurers wonder if they will be replaced by machines?

ND: Some of the results that Google’s DeepMind has shown in recent years do make us wonder if machines will take over soon. In October 2019, for example, Google announced that its AlphaGo AI had beaten a world-class player at the ancient Asian board game Go – in other words, it played better than a human. For some people, this is a frightening thought, for others, it is something they are looking forward to.

But when asking whether the treasurer will be replaced by a machine, we have to be realistic. At this point in time, the machine still needs people to programme it. And there are some parts of human nature – like gut instinct and experience – that a machine cannot replicate entirely. Of course, we can’t be sure how fast the world will change!


EH: What are the main applications for AI within treasury?

ND: As I see it, there are four main areas of application for AI in the field of treasury. First is the automation of tasks. Any repetitive task that requires only minor decisions to be made could have AI applied to it, such as reconciliation of forecast with transaction data. Even if automation is already very advanced within a particular treasury department, there could still be advantages to using AI, such as making explicit instructions ‘redundant’ by using machines that observe human behaviour.

The second area where treasury could benefit from AI is forecasting. For many treasurers, producing an accurate and real-time cash flow forecast remains an elusive task. A handful of treasury teams are already using AI to improve their liquidity planning and risk forecast, having developed their own AIs alongside in-house data scientists with significant technology investment. There are also some treasurers who have purchased forecasting systems from vendors which leverage AI, but there is some debate as to the extent of the ‘intelligence’ here.

Next, we have support in complex decision-making. This is not the same as actually making the decision. It is providing analysis and different scenarios in order to assist the final decision. A good example would be determining which instrument to use for a specific hedge of a hard-to-understand risk. AI could also help treasurers to pinpoint the optimal time to issue a bond, for instance.

Last, but by no means least, is fraud prevention, especially in the area of payments. Here, software can predict and prevent electronic payment losses before they occur. Machine learning can automatically respond to variances in data, behaviours and trends. It learns patterns of fraudulent and legitimate transactions, to simultaneously minimise fraud and false positives.

 

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