Over the span of a generation, treasury organizations in many large companies have migrated from a reliance on spreadsheets toward the use of treasury management systems. These solutions provide measurable increases in cash visibility, productivity, and risk controls. Now new technologies like data analytics, robotic process automation (RPA), and machine learning are rapidly building on these capabilities, fundamentally transforming the day-to-day activities of leading corporate treasury functions.

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

Expectations are that within five years, most treasury management systems will include embedded RPA—which automates repetitive processing tasks using rules-based logic. In fact, some vendors have already started to integrate these technologies. Robots automatically access, assess, and process huge data volumes involving cash positions, interest rates, payables, receivables, and foreign exchange rates, performing these tasks faster and more accurately than humans could.