How fintech and AI is revolutionising finance and treasury

As we count down to Sibos 2026 in Miami, it's worth reflecting on just how quickly the finance and treasury world has changed. 

Ten years ago, the finance role was seen largely as operational: generate financial reports and keep things tight. Today, the focus has shifted toward strategic insight: help us understand the money. Fintech, and more specifically, artificial intelligence (AI), has played a central role in transforming finance and treasury.

As an MBA student, I was a founding member of an AI club, where students and businesses would come together to discuss AI’s practical applications across different industries. That experience shaped my conviction that AI is fundamentally a problem-solver, not just a technology trend. 

Treasury systems are particularly ripe for AI transformation. Earlier in my career, I did a piece of work looking at the cost and time required to implement traditional treasury systems. It often took about 15 months and substantial investment to build the required capabilities.

I realised there’s a gap in the market to genuinely revolutionise treasury management through AI. Mid-market firms were particularly underserved; they’re too large for basic tools, but not large enough for the expensive, complex legacy systems.

Their treasury teams are drowning in spreadsheets and week-old data, downloading bank statements weekly and reconciling transactions by hand. For a junior team member, that could account for a large portion of their working week.

From spreadsheets to strategic decision-making

Today, rising interest rates, currency fluctuations, and inflation mean that cash management has become critical. This environment has elevated the role of the treasurer and put modern treasurers and CFOs at the centre of the strategy and decision-making process.

Delayed data is a business and compliance risk. Businesses can no longer rely on old systems that only give a snapshot of data once a week and spend hours, if not days, sifting through Excel documents. The companies that thrive are those using dynamic data to make faster, more informed decisions about liquidity, hedging, and investment.

AI removes the time-consuming operational burden from tasks like matching transactions or compiling reports, so teams can concentrate on future cash flows, funding models, and scenario planning. This shift also allows finance to respond in real-time to changes in the market. In short, automation liberates people from process, so they can become true strategic partners to the business.

Scenario modelling, risk management, and horizon scanning are areas where the benefits will become more apparent with AI. I don’t have to spend hours or days doing manual work to try to model the effect. If I can tell the AI what’s happening, I can get an answer in almost real-time and no key business decisions are delayed.

That could mean shipping a product earlier to mitigate against taxes or higher shipping costs or locking in a currency exchange at a more favourable rate in anticipation of an interest rate rise. It’s a painless experience that saves time but has the potential to substantially improve risk management and business operations.

If you’re starting in finance today, you’re not just inputting data and creating reports generated by Excel. You expect to have two-way conversations with your finance system. Agentic AI tools will want to make sure you want to do that and offer better alternatives where possible. This is the future of finance.

Maintaining regulatory and compliance standards 

As PSD3 reshapes data-sharing rules, open banking reaches maturity, and real-time payments and intelligence become the norm, the gaps exposed in that Thursday-afternoon scramble – delays, blind spots, and manual workarounds – will only widen.

The shift from compliance to capability is accelerating. Instant payments are now table stakes; what differentiates leading finance teams is how clearly and automatically they understand their cash position in real time. PSD3 enhances API standards, improves fraud data-sharing, and strengthens liability frameworks, all of which drive a need for greater transparency and control.

Every sector thinks its treasury problem is unique, and they’re half right. While the challenges vary, the common need is precision and real-time clarity. 

Finance teams don’t just need to move money fast; they need to understand and steer their cash position in real-time. That means no delays in reconciliation, no black boxes, and no legacy drag.

Delay modernisation and risk falling behind

Connecting data in real-time and using AI to automate repetitive work is a gamechanger. Instead of looking backwards, finance teams can now see their positions instantly and make strategic decisions proactively.

Over the next few years, this will evolve even further with deeper ERP integrations, near-instant reconciliations, and predictive forecasting that make finance operations almost fully automated.

Companies must put AI front and centre, close the gaps left by legacy systems, and rethink how they solve problems. The expectation now is near-total automation and intelligent systems that don’t just execute commands but proactively recommend better decisions.


The first quarter of this century redefined what was possible – driven by the democratisation of access, the rise of automation, and the relentless removal of friction. 

The next quarter will be shaped by something deeper: data that learns, intelligence that anticipates, and systems that think alongside us. The question isn’t if you need to modernise; it’s how fast.

My advice: embrace AI and automation as strategic enablers, not threats. Finance teams want to think ahead, not wrestle with yesterday’s data, technology and processes. Don’t end the financial year with 2020s tools in a 2030s economy.

Editor's note

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