Why We Need to Get Comfortable with Treasury Automation

  • By AFP Staff
  • Published: 1/3/2023
Treasury Automation

As treasury evolves from an operational department to a strategic business partner, automation is playing an increasingly important role. With the expanded use of real-time payments, we are moving ever nearer to a world of real-time finance, an environment of constantly changing data. Without more automation, treasury practitioners will simply not be able to make decisions quickly enough.

While technology is critical to improving operational efficiency, it is only achievable if the technology is deployed to perform suitable tasks. This requires accurate expectations of what each type of technology can and cannot deliver. In making this determination, treasurers must be able to do two things: identify key inefficiencies, or “pain points,” within their operations; and match each activity to an appropriate technology with the potential to solve it.

All companies have these pain points and, if left unchecked, they only get worse. Pain points manifest themselves in different ways, whether as errors, such as missed investment opportunities or unhedged exposures, or as time-consuming manual processes, such as the preparation of the cash position. While the root causes and associated operational weaknesses are relatively simple to identify, the challenge lies in selecting appropriate solutions to solve those problems.

Robotic Process Automation

Robotic Process Automation (RPA) is a rules-based technology enabling users to automate repetitive tasks. It effectively uses a software “bot” to replicate a series of manual processes performed by a person. Unlike a standard workflow process that operates within a single system, an RPA bot can be set up to capture data from multiple systems as it mirrors a human treasury team member by sitting above existing systems. Because of this, RPA processes can often be implemented quickly and without major disruption to existing operations.

Machine Learning and Artificial Intelligence

Artificial intelligence (AI) is the use of a computer or machine to mimic certain elements of human intelligence. Machine learning (ML) is a branch of AI, in which a machine learns how to identify patterns in data.

Unlike RPA, which simply replicates a series of repetitive processes, machine learning can be used to analyze data to identify trends or patterns through the use of algorithms. This goes to the heart of many companies’ problems with data analysis: Companies generally hold or amass vast stores of data, but they do not convert it into meaningful information.

As with RPA, data analysis via machine learning is faster than human computation. Once set up, machine learning can operate at any time (constantly, overnight or according to a customized schedule), enabling decisions to be made with the optimal, latest available data.

However, implementation is more complex than typical RPA scenarios. Machines can learn or be taught by making amendments to the underlying algorithms, patterns over time, but they are reliant on access to multiple data to perform meaningful calculations. Companies need to be prepared to make the investment in technology and data cleansing or preparation before any machine learning would be effective, both from a results and a cost perspective.

The Potential Benefits

The use of RPA and machine learning offer similar potential advantages including:

  • Improved accuracy. With RPA, as long as the process is set up correctly, the bot will perform the same tasks in the same way every time. The risk of human processing errors is eliminated and, if any variance in the RPA outcome and actual outcome is identified, the RPA process can be adjusted. In the case of machine learning, accuracy will improve over time, as the machine learns, and the algorithms are adjusted.
  • Significantly reduced processing time. Bots and machines can perform typical tasks in a fraction of the time it takes a person to complete. This means activities can be performed faster, so decisions can be made based on the most recent available data.
  • Results available globally, when needed. Machine learning and RPA technology can operate at any time, so calculations can be performed overnight or on desired schedules to meet operational requirements. So, for example, in the case of cash positioning in a multinational organization, results can be available when teams in each location start their respective days.
  • Time management. Eliminating mundane processing from a treasury professional’s day means that time can be redirected to more value-added activities such as engaging with additional time-saving activities, supporting the wider business or focusing on strategic decisions.
  • Improved morale. Although some treasury staff will be concerned about the impact of RPA and machine learning on their own jobs, for most organizations the technology is an additional process that will improve team members’ experiences by eliminating the stress of calculating positions under time pressure or reducing the risk of error. Team members will also have time to spend on more interesting and personally rewarding activities.

How Technologies Are Being Used

AI and ML have incredible potential for cash management and forecasting, particularly when reconciling prior-day bank files with yesterday’s expected cash position. “This is one of the first cash management processes performed each day,” said Bob Stark, vice president of strategy for Kyriba. “And for some organizations, the volume of transactions is so big that it can take hours and multiple people to do that reconciliation.”

ML can be used to identify and resolve those discrepancies on its own. “In the simple scenario where the prior day file reports a $1 million wire and we thought it was going to be $900,000, the cash manager will know through their experience what explains that $100,000 difference and what to do about it,” Stark said. “Machine learning will learn from the user’s manual reconciliation, so next time it will reconcile those transactions without human intervention.”

But AI and ML can do more than detect anomalies — they can recognize when an exception isn’t actually a problem. For example, your company might make a regular monthly payment to a supplier of approximately $10,000. However, a recent payment made at the end of the current month is $15,000. With rules-based automation or even RPA, you likely have a payment control that flags that 50% variance from the normal monthly amount, quarantining that payment for further review. But ML can recognize that this particular payment is part of a larger pattern where the last monthly payment in each quarter is substantially higher than the average.

AI and machine learning can also be used in the actual AR process. While RPA allows AR to automate the repetitive, manual tasks, machine learning algorithms can be used for more intricate work such as identifying patterns in transactions. This can help your organization in its decision-making and really tailor the behavioral change that you want to bring about in your customers.

AI can also help treasury as it consolidates copious amounts of data from ERP systems, TMS and other bespoke sources when doing cash forecasting. To create the forecast, treasury needs to consolidate the data correctly to make sure it’s getting the right data sources from the TMS and ERP systems.

Making the Business Case to Implement

As with the adoption of any new technology, it’s critical to build a strong business case. This means getting buy-in from a project sponsor and approval from all required stakeholders. Setting and achieving key success metrics lends credibility to the project, which in turn will help treasury practitioners introduce more digital finance initiatives. To help make a compelling business case and ensure the objectives are well-defined, there are a number of key considerations.

  • Understand how the technology will solve the problem. As outlined above, different technologies are better suited to solving particular problems. The project owner needs to understand the nature of the problem and how the proposed technology will solve it.
  • Optimize processes before automating. If there is an existing process, map it and review whether it can be made more efficient. Many manual processes incorporate separate checks and approvals to protect against error and fraud. While some may need to be migrated into an automated process, it may not be necessary to migrate all of them, as long as the rules are tightly written. A machine learning project may require improvements to data management to enable automated data analysis.
  • Communicate and educate stakeholders on the proposed solution. Communication is central to the success of any project. Senior management will have to approve the project. If IT input is required, they will need to be engaged early in the planning process to secure resources. Banks, technology providers and other data suppliers should also be approached early to plan how they can support the project. Treasury team members will want to understand the implications for them.
  • Identify potential returns. One of the key benefits of automating a process, whether by RPA or ML, is to remove layers of human involvement in either mundane, standardized processing or time-consuming data collation and analysis. So, although there will be some clearly measurable costs and benefits, many will be “softer” benefits in the form of released time and reduced risk of error and fraud.
  • Establish and monitor success metrics. It can be helpful to identify some clear targets to serve as measurements of the success of the project, such as how much time an RPA project has saved. It may be possible to illustrate consequential benefits too, such as improved investment returns due to more accurate cash forecasts. If possible, use these measurements to refine the bot or machine to achieve further efficiencies.
  • Scale the solution; look for the next step. Measuring the outcome of one project will help build support and momentum for others. As technology develops, there will always be additional ways it can be adopted to improve treasury operations.

Treasury Technologies on the Horizon

The adoption of new treasury technologies continues to accelerate, driven by two factors in particular: the evolution of the “internet of things,” and the move toward real-time finance. While the catalysts for the development of these two trends are different, the implications for treasury and finance are closely linked.

The value of the internet of things comes from the way data can be shared between billions of different devices connected via the internet. It allows individuals to control their personal environment (e.g., smart lighting and heating) and companies to manage a whole range of processes from stock ordering to logistics management.

For treasury and finance, the value will come from being able to link the physical and financial supply chains and gain better insight into cash. To do so effectively, data sent out by these connected devices needs to be analyzed by artificial intelligence; the devices simply produce too much data to be analyzed any other way.

There is a clear trend toward more real-time activity in treasury and finance, with real-time payments being just one, albeit significant, step. Notably, the move toward real-time processing is also a shift to 24/7/365, always-on operations. Treasury will need to consider how to manage this change and how to manage risks that will emerge overnight, including between the Friday close and the Monday restart.

The growth of e-commerce has already provided a sense of changes to come. Consumers who pay online expect to see their order status updated in real-time and, in some sectors, the service or product made available in real-time too.

Managing payment processes is just one part of a much wider change the adoption of real-time payments will bring to finance and treasury. If payments are being made in real-time, treasury departments will need to manage their liquidity in real time too, and they will rely on a level of automation and artificial intelligence to do so. Before long, the foreign exchange and money markets will move toward real-time, profoundly affecting the treasury department’s day, which is currently structured by cut-off times.

For these reasons, it seems inevitable that both RPA and artificial intelligence, including machine learning, will play a more prominent role in the management of corporate treasury departments in the coming years.

Although RPA and AI are seen as cutting-edge, in reality, many companies are benefiting from these technologies through solutions provided by their banks and technology partners. For organizations yet to implement the technology, it’s time to get comfortable with treasury automation. The future is merely a click away.

To learn more, check out the AFP Treasury in Practice Guide, Identifying Value for Treasury: Automation, Machine Learning & Artificial Intelligence, underwritten by Kyriba.

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