Over the span of a generation, treasury organizations in manylarge companies have migrated from a reliance on spreadsheetstoward the use of treasury management systems. These solutions provide measurableincreases in cash visibility, productivity, and risk controls. Nownew technologies like data analytics, robotic process automation(RPA), and machine learning are rapidly building on thesecapabilities, fundamentally transforming the day-to-day activitiesof leading corporate treasury functions.

So extraordinary is the digital transformation of treasury thatmany believe treasury teams of the future will primarily be engagedin value-added activities in areas like optimizing cash flows andcurrency management, instead of routine transactional tasks.Datadriven insights that staff acquire from analytics tools willalso be increasingly valuable to other parts of the enterprise.

Expectations are that within five years, most treasurymanagement systems will include embedded RPA—which automatesrepetitive processing tasks using rules-based logic. In fact, somevendors have already started to integrate these technologies.Robots automatically access, assess, and process huge data volumesinvolving cash positions, interest rates, payables, receivables,and foreign exchange rates, performing these tasks faster and moreaccurately than humans could.

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