Identifying Value for Treasury: Automation, Machine Learning & Artificial Intelligence
Underwritten by Kyriba
Corporate treasury departments rely on technology to maintain effective operations. Digital treasury tools, including robotic process automation (RPA) and machine learning (ML) are being used to facilitate treasury automation, which leads to better decision-making and frees time for skilled treasury practitioners to focus on strategic development.
The AFP Treasury in Practice Guide, underwritten by Kyriba, outlines how RPA and ML can, and is, being used, and shows how to build a business case for a new automation project. Check out some of the highlights included in the guide below.
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1. Robotic process automation, machine learning and artificial intelligence
RPA is a rules-based technology enabling users to automate repetitive tasks, while ML is a branch of artificial intelligence (AI) in which a machine learns how to identify patterns in data. Unlike RPA, which simply replicates a series of repetitive processes, ML can be used to analyze data to identify trends or patterns via the use of algorithms. Although the use cases vary, the use of RPA and ML offer similar potential advantages: improved accuracy; significantly reduced processing time; results available, globally, when needed; time management; and improved morale.
2. How emerging technologies are being used
RPA and ML are already being used to solve problems faced by individual treasury departments. This section dives into three use cases to highlight how different companies have deployed digital technologies to streamline operations.
3. Making the business case to implement
As with any technology project, it is critical to build a strong business case when seeking to adopt RPA or ML. To help make a compelling business case and ensure the objectives are well defined, there are several key considerations: understand the technology to maximize the potential benefits; optimize processes before automating; communicate and educate stakeholders on the proposed solution; identify potential returns; establish and monitor success metrics; and scale the solution.
4. Greater automation is the future
Increasing technology has further enabled the transformation and evolution of treasury departments from labor-intensive operational, tactical departments into strategic partners to the wider business. Two factors that are accelerating treasury technologies are: the evolution of the “internet of things,” and the move toward “real-time” finance. While the catalysts for the development of these trends is different, the implications for treasury and finance are closely linked.
Although RPA and AI are seen as cutting edge, many companies are benefiting from these technologies through solutions provided by their banks and technology partners. For organizations yet to implement the technology, doing so successfully requires three key steps: identify a pain point to be solved, match the technology to the task, and build a business case.