Treasury Contrarian View: AI in Treasury—Hype or Reality?

Artificial Intelligence (AI) is one of the hottest topics in corporate finance, and treasury is no exception. Vendors promise smarter forecasting, faster reconciliations, and real-time risk management. But here’s the big question: Is AI in treasury living up to the hype, or is it just another buzzword with limited real-world impact?

Where AI Is Making a Real Difference

  1. Cash Forecasting and Liquidity Management
    AI-driven models can analyze vast datasets to predict cash flows with greater accuracy than traditional methods. By identifying patterns and anomalies, AI helps treasurers anticipate liquidity needs and optimize working capital.
  2. Fraud Detection and Risk Management
    Machine learning algorithms excel at identifying suspicious activities. They can detect fraud in real-time by analyzing transaction behaviors, flagging anomalies faster than human monitoring ever could.
  3. Automating Repetitive Tasks
    AI-powered automation tools handle routine tasks like bank reconciliations, payment processing, and data validation, freeing up treasury professionals to focus on strategic decision-making.
  4. Advanced FX and Hedging Strategies
    Some treasury teams are leveraging AI to optimize FX exposures, identify hedging opportunities, and even make predictive assessments on currency movements.

Where the Hype Outpaces Reality

  1. Overpromised Capabilities
    Many AI solutions sound impressive but fall short when it comes to delivering actionable insights. Predictive models can struggle with unprecedented events (like the COVID-19 pandemic), making them less reliable in volatile markets.
  2. Data Quality Issues
    AI is only as good as the data it processes. Inconsistent, siloed, or poor-quality data—a common issue in treasury systems—can undermine the effectiveness of AI-driven insights.
  3. Complex Implementation and Costs
    AI isn’t a plug-and-play solution. Implementing AI tools often requires significant investments in technology, integration, and employee training, which can outweigh the short-term benefits for smaller organizations.
  4. Limited Human Judgment Replacement
    While AI excels at data analysis, it can’t replace the nuanced decision-making and strategic thinking that treasury professionals bring to the table. Treasury isn’t just about numbers—it’s about context, risk appetite, and business judgment.

The Balanced View: AI as an Enabler, Not a Replacement

Instead of viewing AI as a silver bullet, treasury leaders should see it as a tool to enhance existing processes:

  • Augment, Don’t Replace: Use AI to complement human expertise, not to replace it.
  • Focus on Data First: Prioritize data quality and integration before investing heavily in AI-driven solutions.
  • Iterative Adoption: Start small with specific use cases (like forecasting or fraud detection) and expand as the technology proves its value.

Let’s Discuss

  • Is AI delivering tangible benefits in your treasury operations, or does it feel more like a marketing buzzword?
  • What’s the biggest challenge your team faces in adopting AI technologies?
  • How do you balance the potential of AI with the need for human oversight in treasury decisions?

We’ll kick off the conversation with insights from our board members and treasury tech partners who are actively experimenting with AI. Share your thoughts below—we’d love to hear your experiences!

James Kelly, Co-Founder, Your Treasury, comments:

We’re in the early stages of use of generative AI in treasury and teams are getting real value from chatbots and meeting summaries, but these are quite small use cases. While machine learning has been used in some very large treasury departments and banks for forecasting, it’s use has been far from widespread and so for most of the market, there’s a new skill to learn. It’s also the case that it won’t work for all businesses or all cash flows as the ‘huge datasets’ described above aren’t available in many businesses or for certain cash flows.

Where there is genuine potential is in reducing reliance on key staff by becoming an ‘organisational memory bank’, offering how to guides instantly, as well as the ability to produce reports and guides. Over time, we should see AI infused into most treasury processes. For example, if a transaction is reclassified once manually, this is remembered for the future. Ultimately we’ll move away from modular systems where modules don’t talk to each other, to tools that can manage whole processes in one place. This may be the current chatbot style or may be controlled by voice or other means

Does that mean that treasury is going to be fully automated and without people? It’s highly unlikely for a number of reasons:

1. Our start point is pretty manual, with a lot of complex ad hoc tasks, so from where we are to full automation is a huge leap.
⁠2. It’s unlikely that this would be acceptable for many boards and auditors given the sensitivity around cash.
⁠3. To achieve that level of automation would require significant use of AI agents and generative AI, which has been repeatedly shown to be power hungry and devious. See Glass Almanac’s AI: OpenAI’s New Model (o1) Lied and Manipulated Its Way to Survival During Testing and Bank Info Security’s Models Can Strategically Lie, Finds Anthropic Study

For these reasons we always look to balance the benefits of generative AI (which are significant and unlock problems we previously couldn’t automate efficiently) with the risks and only use it to the minimum extent required. It’s almost certain that new treasury roles will emerge in managing and maintaining models, as well as roles requiring the hugely important soft skills (anyone who’s done a bank refinancing will tell you that the legal clauses are a small part of the process).

Overall, there are significant opportunities but deployment needs to be done thoughtfully

Matthew Harlan, Chief Treasury Officer, Nilus, comments:

AI in treasury is neither hype nor a magic bullet—it’s a tool, and like any tool, its value depends on how well it’s applied. While AI has made meaningful strides in areas like fraud detection and automation, its effectiveness in forecasting and risk management are dependent by the data that feeds it. The real opportunity lies in using AI to augment treasury teams, not replace them.

At Nilus, we’ve seen firsthand how AI-driven insights can enhance liquidity management and operational efficiency when paired with strong data and centralized fragmented systems. I’m excited that presently, we are living in the future and believe that now is the time for Treasury teams to engage with technology and reimagine the future.

Lorena Pérez Sandroni, Head of Treasury, PayU GPO, comments:

AI provides valuable insights, and I truly believe it can enhance the role of a treasurer by automating routine tasks, which leads to high levels of turnover in our teams when these tasks are too tedious and there is no room for further personal development while things still need to be done!

I believe the success of AI, like any other technology, will depend on proper implementation and integration into existing treasury systems, requiring investment in technology and training. Are we doing this while enhancing the use of AI in corporates?

However, AI will never replace the strategic insights that a human treasurer brings. 😉

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