Key points

  • CEO Nik Storonsky said Revolut wants to grow beyond financial services into a global technology company built in Europe.
  • The company is training proprietary AI models on transaction data and has already established a dedicated research division.
  • The strategy could widen Revolut's product reach, but data governance, security and regulatory execution remain central tests.

Revolut is positioning artificial intelligence as the bridge from digital banking to a much broader technology business. Chief executive Nik Storonsky said at the Wave by Vento conference in Turin on October 9 that the company wants to move beyond financial services and eventually reach the scale of major U.S. technology groups.

The ambition is more concrete than a generic AI pledge. Storonsky said Revolut has developed proprietary models trained on customer transaction data, drawing on a flow of roughly 30 million to 40 million transactions each day. He described that information as a distinctive dataset for building new services, while stressing that the longer-term objective is global reach rather than a narrow banking expansion.

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From banking app to technology platform

Independent reporting from Reuters and The Next Web placed the remarks in the same strategic frame: Revolut wants to add technology products connected to finance, not abandon its core business. The Next Web reported that Storonsky discussed AI agents that could shop with customers' cards, personalized guidance on spending and investments, and connections that would allow third-party agents to interact with Revolut's own systems after customer authentication. Those examples remain plans described by the chief executive, not a timetable for general release.

Revolut had already laid the technical groundwork. In an August 25 company announcement, it created Revolut Research, a dedicated unit developing machine-learning systems for financial services. The unit supports PRAGMA, a proprietary foundation model built with Nvidia and intended for risk assessment, platform operations and product recommendations. The company said the model's early benchmarks used historical data, so those results should not be treated as proof of future real-world performance.

Why the strategy matters

A bank-like platform that controls both financial data and its AI layer could design services faster, automate more decisions and reduce dependence on external software. It could also turn a payments and banking relationship into a wider technology ecosystem. For customers, that may mean more automated assistance and fraud detection. For banks and fintech rivals, it raises the competitive pressure to build or buy comparable models.

Revolut's scale gives the plan weight. Reuters reported that the company was valued at $115 billion in a 2026 secondary share sale, up from $45 billion in 2024. It has also expanded its regulated footprint, including preliminary approval for a U.S. national bank charter in September and licenses in Britain and France. Storonsky separately said Revolut would prefer a primary U.S. listing if it eventually pursues an initial public offering.

Execution and governance are the test

The same data advantage also creates obligations. Financial models trained on transaction histories must meet privacy, security, fairness and explainability expectations across multiple jurisdictions. Reuters noted that Revolut has faced regulatory scrutiny over transaction-monitoring controls and a September incident in which customer data was sent to hackers posing as government investigators; the company said its systems and customer funds were unaffected.

Revolut has not disclosed a full product roadmap, launch schedule or financial targets for the broader technology push. The October 9 remarks therefore mark a strategic direction rather than a completed transformation. The next evidence will be whether its research program produces widely deployed products, whether customers trust agent-led financial actions, and whether regulators accept the controls around them.

Sources

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