The biggest story in technology over the past three years has undoubtedly been AI, so much so that we forget what came before. Before AI was single-handedly keeping the economy from recession, the backbone of the world’s finances was the FAANG companies, including Facebook, Apple, Amazon, Netflix and Google. All of these companies shared a focus on personalisation, using data to tailor their offerings to every customer.
Artificial intelligence has quickly become embedded in the machinery of modern banking, but for many institutions, its application has been conservative, confined largely to operational efficiency, fraud detection, and customer support automation.
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These are important, but they leave much of AI’s potential untapped. If banks restrict its use to cost control, they risk missing what may prove to be its most transformative role: a direct driver of new revenue streams.
For me, efficiency is only the first step. The real opportunity lies in deploying AI to power hyper-personalisation, predictive analytics, and dynamic pricing. Capabilities that can reshape how banks generate growth. With more than two decades of experience in global payments, working with issuers, acquirers, and enterprises worldwide, I have seen first-hand how data-driven technologies are beginning to redefine the competitive landscape.
Data is the new oil
At the core of this shift is the monetisation of data. Banks sit on vast repositories of transactional insights that extend beyond individual customers to wider behavioural patterns. Historically, this data has been underused, in part because of the complexity of regulation and in part due to a lack of tools to extract actionable intelligence. Think of this like being able to pump thousands of barrels of crude oil but not being able to refine it. AI changes that.
By analysing transaction flows, spending categories, and cashflow patterns in real time, banks can identify opportunities to deepen relationships with customers. A customer consistently paying for overseas streaming services, for instance, may be more receptive to tailored foreign exchange offers.
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By GlobalDataA small business whose payments spike seasonally could benefit from dynamic credit terms. These are commercially viable applications of AI that move banks from passive service providers to proactive partners in growth.
Of course, the question is not only how much data can be harnessed but how responsibly it can be deployed. This is where compliance and innovation intersect. I would point to the growing role of self-hosted AI frameworks as a way forward.
Instead of sending data to external models, which risks violating GDPR in Europe, CCPA in California, or PSD2 rules across the EU, banks can build and train models internally. That approach keeps sensitive data within the bank’s perimeter while still allowing experimentation and innovation. The advantage is twofold: it protects customer trust while enabling banks to develop proprietary AI capabilities that become a source of competitive differentiation.
Equally critical is transparency. As regulators tighten scrutiny of automated decision-making, “black box” AI poses a reputational and compliance risk. Explainable AI – models that can articulate the rationale behind their outputs – is emerging as a cornerstone of responsible innovation. For customers, this ensures clarity when decisions affect their creditworthiness or pricing. For regulators, it provides assurance that automated processes are auditable and fair. Banks that succeed in embedding explainability into their systems are more likely to win both customer trust and regulatory goodwill, creating a stable foundation for growth.
Cultural change before technological
Some leading banks are already moving in this direction. JPMorgan Chase has invested heavily in AI not just to reduce costs but to identify new opportunities in trading and wealth management. Goldman Sachs has integrated machine learning into its consumer platform Marcus, leveraging data to refine offers and pricing strategies. These examples illustrate that AI, when placed at the centre of revenue generation strategies, can reshape competitive positioning.
The institutions that treat AI purely as a back-office utility will struggle to match the agility of those that put it to work on the front line of customer engagement.
For most banks, the challenge is cultural as much as technological. AI adoption at scale demands a mindset shift, away from defensive compliance-driven deployment and toward a more entrepreneurial approach that balances risk with innovation.
This does not mean abandoning caution; it means recognising that the responsible use of AI can generate entirely new forms of value. It also requires investment in talent. Data scientists, ethicists, and compliance specialists must work together to design systems that are not only commercially effective but socially and legally robust.
There is also the question of timing. As competition intensifies, first movers gain the advantage of shaping customer expectations. If one bank begins offering predictive, personalised services that adapt in real time, others will be forced to follow. The longer institutions wait to explore AI as a revenue enabler, the harder it will be to catch up once customer baselines have shifted.
My perspective reflects my vantage point, where issuing and acquiring services across multiple geographies provide daily exposure to how payments and data are converging.
From that position, I see the growing evidence that AI’s future in banking will not be decided solely in the back office. It will be determined in how effectively institutions can turn data into trusted, transparent, and compliant sources of growth.
