Since the dawn of sci-fi thrillers and the realization of computing power, people have been excited about the capabilities of technology. In this series, we have discussed technology’s power in meeting the basic needs of treasury management and have explored the technology-powered layers your organization can add to strengthen, reinforce, and spotlight its potential cash-management capabilities. The last layer in reaching your company’s full potential is achieving self-actualization through machine learning.

Results from the Association for Finance Professionals’ Strategic Treasury Survey showed that “80% of treasury and finance professionals believe that treasury is currently playing a more strategic role at their organizations than in the past three years, [and] a large majority (80%) agree that the strategic role of treasury will grow further at their companies, and that the function will be playing a greater strategic role three years from now.”

Artificial Intelligence and Robotics

Before diving into machine learning, we need to define two commonly used terms in this space: robotics and artificial intelligence (A.I.).

Robotics refers to programable machines that are used to carry out tasks in an automated fashion. This is as simple as a VBA-based macro in Excel or as complex as an assembly line. There can be various levels of human intervention and means by which the “robot” interacts with the physical world.

Artificial intelligence (AI), on the other hand, takes robotics to a new level. When applied to a robotic process, this programming allows a system to learn from the data it consumes or tasks it completes. The key difference boils down to one word: “intelligence.”  With AI, the “how” of completing a task is up to the AI to develop, while with robotics, the “how” is preprogrammed.

Today, we interact with these technologies without giving it much thought. Robotics can be found in your home, such as when you set your coffee maker to start brewing at a certain time. It also is found in personal banking when setting up automated payments or other notifications. AI is the basis for your personalized Apple Music or Spotify playlists made “just for you,” or for smart tolling systems on the highway.

Treasury Applications

What about your day-to-day business activities?

Examples of robotics are easier to identify. Over the last decade, companies have pushed for greater task automation with the goal of achieving efficiencies, reducing errors, and gaining cost savings. Use cases for robotics often have very similar characteristics: repetitive, manual, and time-consuming. In the world of treasury management, organizations are either using Excel or relying on treasury management systems (TMSs) to address many of these pain points, including determining cash positions, performing reconciliations, and calculating metrics.

The most common applications of AI are with machine learning. This is when you take those robotic examples and apply AI to create systems that are learning as they go.

A common example is the application of AI in augmenting the data-consolidation process to create forecasts. According to the Association for Finance Professionals (AFP), “to create the forecast, treasury needs to consolidate the data correctly to make sure it is getting the right data sources from the TMS and ERP systems.” By creating a connection between a specific forecast input and the actual historic data for that time, treasury teams can select more appropriate forecast methodologies. For example, again from the AFP, “attempting to forecast based on due dates for payments is pointless, as customers rarely pay precisely when you require them to. Hence why it is so important to look at the historical trends and patterns, if available.”

Most treasury teams have not yet made the leap to utilizing A.I., but the use cases are prolific and offer great opportunities.

At Kyriba, “use cases such as detection of payment anomalies, smart forecasting and cash reconciliation, and potentially hedge optimization are currently in testing with corporate customers that are eager to harness the new technology.”  

Don’t Go It Alone

Whether you are just starting out on the journey or already are ahead of the curve, 2020 will be an important year to get your hands dirty. If you don’t know where to start, speak with your TMS provider today. If you don’t currently utilize a TMS, or are unsure if your current provider is right for you, contact MorganFranklin today to take the first step in your bank-account rationalization journey.

By Ken Fick

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