Making Machine Learning Work for Treasury

Machine learning is often seen as the panacea for all modern business challenges. However, while it can be a very useful ally, it won’t fix everything – treasurers will still need to make judgment calls. In the company of seasoned experts, TMI explores the reality underpinning this much-discussed technology.

The current level of conversation around machine learning (ML) might suggest that it has already become one of the most prevalent treasury solutions. The truth is that while it is making significant headway in terms of adoption in the treasury context, the journey has only just begun; many firms are still working out where it might fit, and how it can be best used. Is it really time for treasurers to engage?

“Over the next three years, we will create more new data than we have in the entirety of human history combined,” states John Pizzi, Senior Director, Enterprise Strategy, Capital Markets, FIS. He argues that artificial intelligence (AI) is “no longer a science fiction”, citing PwC figures that show AI is expected to contribute nearly $16tr. to the global economy over the next decade.

Few can fail to have noticed the use of AI by Big Tech firms such as Amazon and Google in helping or steering our decision-making. But in both organisations, the use of AI goes beyond what is obvious to consumers. Amazon is using it to optimise its logistics and warehousing operations, and Google is developing AI to aid screening evaluations for lung cancer patients.

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