AI gives Gulf banks the edge in managing liquidity with confidence

Integrated platforms and data-driven agility will allow IFIs to meet rising expectations and shape global standards

By Matthew Nassau, Business Architect, Treasury & Capital Markets at Finastra

Markets move in cycles. Each generation experiences most of the things that previous generations have endured (bull or bear markets, natural disasters, geopolitics, …) punctuated by turning points from which the future takes a distinct path (powered flight, the transistor, The Beatles, …). These highlights are often recognized early on as important in their day and seem to appear ‘overnight’, and yet have taken years of development and formation to appear in our consciousness, while the lasting extent of their transformative power is not fully appreciated.

Generative AI (GenAI) fits the model described above, poised as it is to revolutionize treasury and capital markets by markedly altering decision-making processes for market professionals. From conversational finance to predictive analytics, AI is evolving from a mere assistant to becoming a crucial decision-making tool. In Gulf Cooperation Council (GCC) countries, GenAI could add between USD 21 billion and 35 billion each year, on top of roughly USD 150 billion that existing AI technologies are expected to contribute. That represents about 1.7 to 2.8% of the region’s current non-oil GDP.

To deliver on this potential, it is essential that financial institutions have access to high-quality data, upon which GenAI can infer connections, deliver insights and enable actions.

Data has never looked so good

Data has long been treated as one of the most important assets in financial services. Vendors have built major businesses supplying real-time market feeds, and institutions invest heavily to safeguard customer information in every form. The value is clear. What is changing is how much more that value can grow as GenAI gains access to richer and more precise datasets. Large language models can spot relationships and trends that were previously buried, turning raw information into forecasts, alerts and actions that support commercial and risk decisions.

Unlocking that potential requires broader access to the information that treasury teams already rely on. Data lakes and warehouses form part of the picture, but they rarely capture everything. Treasury management systems are a prime example. Their reporting evolves constantly and plays a central role in liquidity decisions, yet much of it remains confined within the system. By making these reporting histories available to GenAI, banks can reveal patterns over time, flag emerging opportunities or risks and prompt timely intervention.

Timing is everything

To show how quickly things have shifted, consider a discussion I had with a major European bank a few years ago. The team was exploring how to treat treasury and capital markets data as a strategic asset without forcing everything into one central system. Their vision was a unified data layer where information could stay within existing applications yet still be accessed, combined and analyzed by staff using low code tools. The goal was to shift toward more data-driven decision making across the business and to uncover new sources of commercial value.

The concept was sound, but the technology required to deliver it at scale was simply too expensive and complex at the time. The bank had to narrow its ambitions and proceed with smaller, tactical initiatives. Artificial intelligence was not even part of the conversation. It felt experimental and far removed from daily operations.

Looking back, the idea wasn’t premature in strategy, only in timing. GenAI now makes this kind of agile, distributed data insight far more realistic.

‘Go big or go home’ – not any more

Expectations have moved on as technology has matured and become easier to access. The old way of classifying data projects as either short-term tactical fixes or long-term strategic overhauls no longer applies. GenAI changes the conversation. It shifts focus from where data lives to how much value it can generate. Deploying AI in specific functions like operations, the front office or reconciliation isn’t a stopgap. It’s a practical way to unlock intelligence quickly.

What will determine success is an institution’s ability to surface a wide range of data, ensure its accuracy and let AI learn from it. This doesn’t require a massive transformation program from day one. Starting with focused use cases can improve efficiency, reduce manual work and reveal valuable insights straight away. As more processes become AI-enabled, those individual wins begin to connect, creating a stronger and more intelligent foundation across the entire organization.

Outcomes lead to incomes

When a technology is still emerging, no one can predict with certainty how far its influence will reach. The best indicators often come from those willing to adopt early and test ideas in the real world. Many concepts compete for relevance, and only a few will ultimately reshape how people work.

The organizations that benefit most are the ones comfortable experimenting, moving quickly and learning as they go. GenAI encourages exactly that mindset. It allows teams to explore and refine new approaches by tapping into the data they already hold. The results show up in lower costs, stronger client value and healthier margins.

This shift is not about replacing existing business models but enhancing them. Each step forward can deliver outsized returns for firms confident enough to start now.