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Treasury organisations in AI-transition: where to start?

While adoption remains cautious, Artificial Intelligence (AI) has great potential to transform the treasury landscape. Examples can be found in the automation of routine tasks and strategic insights generation. We asked SEB experts Harri Rantanen and Anastasia Varava to share their views.

How can treasurers leverage Artificial Intelligence?

Explore practical strategies for treasurers to integrate AI into everyday operations:

  1. Start with strong data foundations
    Ensure your data management processes deliver clean, high-quality, and well-structured data. This is critical for effective AI input.
  2. Invest in skills and adoption
    Provide treasury staff with training on selected AI tools and allocate sufficient time for testing and implementation to build confidence and competence.
  3. Prioritize data security and control
    The company’s data must remain secure, fully under your organization’s control, and protected against any external leaks.
  4. Focus on high-impact use cases
    Once you understand the technology and its potential, identify the most impactful cases and move them into production. Avoid getting stuck in a cycle of building multiple small prototypes without delivering real business impact.

As of now, AI is being used in areas such as fraud detection and anomaly discovery in payment transactions. Other examples include reconciliation, where it can help match incoming payments with open invoices – even when the data is unstructured or inconsistent.

“On the other hand, there are concerns about data quality and leakage, and corporates prefer to operate within secure, internal environments rather than cloud-based solutions. There are just a few corporations that are relying heavily on large language models, because they are cautious on the data management issues”, says Harri Rantanen, Business Developer at SEB.

Large Language Models (LLMs) are a type of Artificial Intelligence trained on vast amounts of text. They can assist by providing summaries, translations, and analyses.

One notable example is a student intern who developed an LLM-based solution to help analyse bank guarantee texts and ensure compliance with treasury policies. It was built using freely available AI tools and saved the treasury team in average of 10 hours of manual work per week.
Harri Rantanen
Business Developer at SEB

Eventually AI can also begin supporting more strategic functions.

“In, for example, geopolitical risk analysis, there is a lot of potential in combining different types of information to augment predictive modelling. This is where the real competitive advantage from AI lies – in better decisions, not just saving time”, says Anastasia Varava, Head of Research at SEBx.

Anastasia Varava is clear that AI should not automate decision-making but rather help humans make more informed choices – less reliant on individuals’ gut feeling.

“Gut feeling is your converged experience. You make decisions based on your prior observations. That's exactly how AI is trained as well. But it has a lot more data at hand and can help us improve our decision-making, for example in scenario planning.”

What about risks, then? Are there none?

“Surely, there are risks associated with, for example, agentic AI – systems that act autonomously or make decisions on behalf of users. If you start testing this, you must ensure digital trust is in place first, through solutions that ensure security, authenticity, and reliability when giving consent for an AI Agent to act on behalf of you or your company”, says Harri Rantanen.

He then adds: “All in all, it’s still early days, but the trajectory is clear – the adoption of AI is likely to accelerate, moving us into a new era of intelligent finance. Always having in mind that new opportunities always come with risks, and I believe the advantages are far too great to ignore.”

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