Tomorrow’s Treasury Today: AI in Financial Management

Artificial Intelligence has dominated the conversation this year, splitting opinions with some people embracing the new technology and others sceptical about the prospect of technological unemployment and AI’s influence on industries including treasury.

A recent publication by Insider delved into the jobs that could be most affected by AI technology. Software engineers, coders, journalists and traders which are among those most at risk of being replaced in the coming years.

While it might seem like a shiny and seemingly novel concept, machine learning research dates back to the mid-20th century. The concept was first discussed in the 1930s. Despite its 93-year history, awareness and concerns about its impact on job roles have risen with the emergence of tools like ChatGPT hitting the mainstream, raising questions about job security.

BT Group CEO, Philip Jansen, recently cited AI as an influencing factor in the cutting of 55,000 jobs by 2030 replacing at least 10,000 of them with AI technology, helping to reduce business costs considerably.

The growing integration of new AI into current software reflects society’s growing demand for instant gratification. As we increasingly depend on technology for daily tasks, AI will advance. This prompts the question: how will AI’s ascent affect the treasury market?

Treasury Technology

One of the most significant concerns surrounding the growth of AI pertains to how it’s going to affect our jobs. Every profession has undergone a transformation as they adjust to advancements in technology, and treasury is no different. AI is transforming treasury functions by enhancing efficiency, precision, and strategic planning capabilities. However, successful implementation requires careful management of data, governance, and a step-by-step approach to avoid potential pitfalls.

Since its inception, the treasury function and its technology have evolved significantly, with tools developed independently to accommodate and streamline necessary services. However, changes in inflation, international exchange regulations and the wider global economy at the back end of the 20th century forced treasury professionals to come up with new techniques and strategies to maintain stability and further business development. Globalisation has also influenced companies to seek more comprehensive treasury solutions across business units globally. 

From the introduction of spreadsheet software to the development of financial systems such as Bloomberg, the treasury technology market has seen continual development and significant improvements in functionality, allowing for better connectivity between banks, vendors, and companies as it strives to keep up with increasing business requirements.

How is AI being used in treasury today?

Treasury management, an integral component of a business’s digital ecosystem, is increasingly leveraging AI to elevate its operations. AI technologies are already being employed to enhance cash flow forecasting, mitigate fraud, manage foreign exchange risk, and optimise liquidity management.

By integrating machine learning-driven solutions into treasury operations, AI aids in refining these processes further with even greater precision and efficiency. The treasury landscape already includes a diverse array of software such as Treasury Management Systems (TMS), SEPA, Bloomberg, and SWIFT, to name but a few. The COVID-19 pandemic only further accelerated the digital transformation within treasury departments, pushing the adoption of AI and other advanced technologies to new heights.

A recent study by Deloitte revealed that the majority of treasurers surveyed are prioritising digital transformation and technology advancements as critical areas for improvement. The study highlighted that inadequate systems infrastructure is causing delays and disruptions in key treasury functions such as liquidity management, foreign exchange, and forecasting. Many treasurers believe that embracing digital transformation will significantly enhance their operational capabilities.

The survey indicated that 78% of treasurers are already using technology to assist with accounting practices, while 64% are using it for cash management functions. Furthermore, 50% of respondents are employing technology for cash flow forecasting.

However, the implementation of new AI-powered technologies or the upgrade of legacy systems represents a significant challenge for many organisations. This transition has the potential to restructure entire teams, with many roles evolving into primarily supervisory or reporting functions.

A study by investment giant, Goldman Sachs, estimated a 7% rise in global GDP as a result of AI, predicting a potential global AI market value of $150 billion. However, this is not without consequences, as the report cited that potentially 300 million full-time jobs could be replaced by varying degrees of automation in the future.

The integration of AI technology in treasury management is evident in two primary areas:

• Treasury Management Systems (TMS): AI is increasingly being embedded in TMS to provide deeper, data-driven insights. These systems centralize the management of cash, liquidity, risk, and reporting. AI has the ability to enhance these functions by identifying hidden patterns, optimising cash management, assessing risks, detecting fraud, facilitating trading, and ensuring regulatory compliance. By leveraging AI, TMS can transform vast amounts of data into actionable insights, enabling treasurers to make informed decisions swiftly and accurately.
• Robotic Process Automation (RPA): RPA is transforming treasury through the automation of routine tasks and processes. It allows treasurers to create algorithms that streamline operations, boost efficiency, and execute essential business functions. RPA can handle repetitive tasks such as data consolidation, risk analysis, and predictive modelling, freeing up human resources for more strategic activities.

These advanced technologies automate tasks, consolidate data, analyse risks, and perform predictive analyses. As digital technologies and software become more interconnected, they generate data that enables treasury professionals to make quicker and more accurate decisions. AI integration through APIs is revolutionising treasury management by enabling real-time data exchange, predictive analytics, fraud detection, and automation. This not only boosts efficiency and accuracy but also equips treasury departments to navigate the complexities of the modern financial landscape more effectively.

The role of AI in treasury operations is becoming increasingly pronounced. The role of AI in treasury operations is becoming increasingly significant. The benefits of AI and machine learning are evident in the streamlined efficiency of key tasks, cost savings, and business growth. However, the impact of AI on the treasury sector mirrors broader industry changes. It’s crucial to consider the potential pitfalls of AI and its implications for the future treasury workforce and its hiring practices. As AI continues to evolve, thoughtful consideration of its impact on job roles and market dynamics is essential.

In part 2 of this blog, we look at the potential negative impacts AI could have on the treasury market.