Real-time treasury: Unlocking the future of Cash and Liquidity Management

Perspectives from Eddy Jacqmotte, Group Treasury Manager at Borealis and Floor Meeuwis, Liquidity and Account Management Products Advisory at Societe Generale

From Traditional to Real-Time: A Paradigm Shift but no One-Size-Fits-All

Before diving into the benefits of real-time treasury, it’s important to understand where most organizations stand today. Treasury digitalization is progressing at different speeds across organizations. While some corporates have adopted automated and data-driven systems, others still rely on manual processes. The relevance of real-time treasury depends on internal organization, banking relationships, and the complexity of cash flows. For instance, integrating APIs from multiple banking partners may not yield significant time savings to justify the effort.
From the corporate side, partial automation is common. The main challenge lies in data quality—without clean and reliable data, automation can lead to confusion. Real-time capabilities are valuable, but only when aligned with operational needs.

Technology Is Reshaping Strategic Priorities

Recent market volatility and changing interest rates conditions have prompted corporates to rethink their treasury setup.

Treasury is evolving more and more into a strategic function, with increased focus on visibility, control, and optimization.

Idle cash represents a missed opportunity, and treasurers are continuously looking to maximize liquidity performance.
Technology enables tighter integration between treasury systems and banks, allowing for dynamic cash management. Real-time data supports better forecasting and faster decision-making, turning treasury into a competitive advantage. 

AI: Promise and Pragmatism

Artificial Intelligence is beginning to play a role in real-time treasury, though many current applications are better described as machine learning or advanced analytics. Use cases include fraud detection, anomaly spotting in cash flows, and predictive forecasting.

Some experiments have delivered tangible benefits, such as improved accuracy in cash forecasting or faster identification of unusual transactions. Others have shown limited incremental value, often due to poor data quality or lack of integration. The success of AI depends heavily on the availability of clean, structured data and a clear understanding of the business context¹.

Opportunities and Risks of Real-Time Systems

The shift from batch to real-time payment systems is transforming liquidity management. Corporates must adapt to a more agile operating model, where continuous flows replace fixed cycles. This requires robust governance and real-time controls to manage complexity and mitigate risks.

It also means that beyond traditional payment activities, other parts of the banking may need to open up to provide 24/7/365 value-added services to the ecosystem, rethinking settlement models and liquidity buffers. This opens the door to dynamic remuneration models, where liquidity value is assessed in real time rather than at the end of the day.

Autonomous Treasury: Vision or Reality?

The concept of a fully autonomous treasury is compelling, but not yet realistic. AI can automate tasks, but strategic decisions still require human judgment. There are also ESG concerns, as AI requires significant computational power. Language barriers persist too—over half of the internet’s content is in English, limiting the reach of AI models trained on global data.
AI will become more embedded in treasury processes, but human expertise remains irreplaceable. The future lies in a hybrid model where intelligent systems handle routine tasks and provide insights, while human experts make strategic decisions. 

In conclusion, real-time treasury is reshaping how corporates manage cash and liquidity. It offers new levels of visibility, agility, and optimization, but its implementation must be tailored to each organization’s needs. AI and real-time data are powerful enablers, requiring careful integration and human oversight. The treasury of the future will be smarter, faster, and more connected—yet still reliant on the expertise of treasury professionals.

^{1:  MIT Says 95% Of Enterprise AI Fail- Here’s What The 5% Are Doing Right}