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Revolut to advance fintech architecture with research platform
The online banking app has established a dedicated division within its broader AI department. Neobank Revolut has announced plans to develop machine learning architecture across its financial services, as part of the new Revolut Research division. Revolut Research is a focused division within its broader AI department and is designed to build alongside academic and technology institutions as the unit provides a foundation for the company's AI deployments and machine learning initiatives. Revolut Research is the AI engine that drives Pragma, Revolut's foundational model which was built in partnership with global chipmaker Nvidia. Pragma aims to unify financial behaviours into one connected system, as opposed to treating each transaction as an isolated task. It also works to detect fraud and enable various customer services. The platform uses a global dataset of more than 80m consumers across 40 markets to build a pattern of financial behaviours in real time, with information developing as the dataset evolves. In the future, Revolut Research will publish the findings of its scientific research and open-source technical frameworks, with additional plans to incorporate its team into the wider scientific community by means of conferences and quarterly meet-ups. "To lead the future of intelligent banking, you cannot rely on third-party blueprints," said Pavel Nesterov, the head of AI at Revolut. Nesterov added, "We have launched Revolut Research to institutionalise our 'build, don't bolt on' philosophy. "By training native foundation models on our global operational data, we are giving our engineering teams an unprecedented engine to deploy smarter features faster, eliminate systemic friction, and give our customers a safer, radically better financial experience." Anton Repushko added, "Revolut Research has been established to responsibly build financial intelligence at its deepest layer, rather than patching together narrow, specialised models. "In PRAGMA, we are developing a single, unified foundation model capable of understanding the true nuance of financial behaviour in real time. Technology is in Revolut's DNA, and by collaborating with global tech leaders, this division is engineering proprietary capabilities that set us apart from traditional banks." In August, Revolut was issued a full banking licence for the French market, giving the fintech a second European Union hub. Revolut Bank SA was granted the licence following a joint assessment by the Autorité de Contrôle Prudentiel et de Résolution, which is a part of France's central bank. Don't miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic's digest of need-to-know sci-tech news.
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Revolut launches dedicated AI research lab built on its own foundation model
This content has been selected, created and edited by the Finextra editorial team based upon its relevance and interest to our community. The Research unit will work with NVIDIA to keep pushing Pragma, the model it trained on its own customers' transaction histories to power real-time risk assessment, platform operations, and tailored product recommendations. Early deployments of Pragma on historical data have indicated major performance gains over legacy baselines, including a 2.3x uplift in identifying credit default risk, an increase of 65% on fraud cases caught and 41% more relevant product recommendations across retail and business accounts. Serving 80 million customers across more than 40 markets, Revolut processes billions of cross-border transactions and diverse financial behaviours in real time. This data feeds into Revolut Research's models. As the dataset grows, the models become exponentially smarter at detecting fraud, evaluating risk, and predicting user needs, says Pavel Nesterov, head of AI at Revolut. "To lead the future of intelligent banking, you cannot rely on third-party blueprints," he says. "We have launched Revolut Research to institutionalise our 'build, don't bolt on' philosophy. By training native foundation models on our global operational data, we are giving our engineering teams an unprecedented engine to deploy smarter features faster, eliminate systemic friction, and give our customers a safer, radically better financial experience." He says Revolut will open-source the technical frameworks and engage with the broader scientific and tech community to further refine the intelligence layer underpinning Pragma.
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Revolut has established Revolut Research, a dedicated AI research division within its AI department, to develop Pragma—a foundation model trained on data from 80 million customers across 40 markets. Built in partnership with Nvidia, Pragma has demonstrated a 2.3x uplift in identifying credit default risk and 65% improvement in fraud detection compared to legacy systems.

Revolut has launched Revolut Research, a specialized division within its broader AI department focused on advancing machine learning architecture across its fintech platform
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. The neobank's strategic move signals a decisive shift toward building native AI capabilities rather than relying on third-party solutions, as the company aims to reshape intelligent banking through proprietary technology.The Research unit serves as the AI engine driving Pragma, Revolut's foundation model developed in partnership with Nvidia
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. Unlike traditional approaches that treat each transaction as an isolated task, Pragma unifies financial behaviors into one connected system, processing data from more than 80 million consumers across 40 markets to build patterns of financial behaviors in real time1
.Early deployments of Pragma on historical data have revealed substantial performance improvements over legacy systems. The foundation model achieved a 2.3x uplift in identifying credit default risk, caught 65% more fraud cases, and delivered 41% more relevant product recommendations across retail and business accounts
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. These metrics demonstrate how customer transaction data, when processed through sophisticated machine learning architecture, can significantly enhance real-time risk assessment capabilities.Pavel Nesterov, head of AI at Revolut, emphasized the strategic importance of this approach: "To lead the future of intelligent banking, you cannot rely on third-party blueprints. We have launched Revolut Research to institutionalise our 'build, don't bolt on' philosophy"
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. By training native foundation models on global operational data, Revolut aims to give engineering teams an unprecedented engine to deploy smarter features faster and eliminate systemic friction2
.Revolut Research plans to publish findings from its scientific research and open-source technical frameworks, integrating its team into the wider scientific community through conferences and quarterly meet-ups
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. This commitment to transparency positions Revolut to refine the intelligence layer underpinning Pragma while contributing to broader advancements in AI research2
.Anton Repushko stated that Revolut Research was established to "responsibly build financial intelligence at its deepest layer, rather than patching together narrow, specialised models"
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. The division's work with Nvidia focuses on developing a single, unified foundation model capable of understanding the true nuance of financial behaviors in real time, setting Revolut apart from traditional banks.Related Stories
As Revolut processes billions of cross-border transactions from its 80 million customers, the dataset feeding into Revolut Research's models grows continuously. This creates a compounding advantage: as the dataset evolves, the models become exponentially smarter at fraud detection, evaluating risk, and predicting user needs
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. The platform's ability to detect fraud while enabling various customer services through one unified system represents a fundamental shift in how fintech architecture can be designed.Watch for how Revolut's open-source contributions influence the broader AI department strategies across financial services, and whether the performance gains demonstrated by Pragma push other neobanks to invest in proprietary foundation models rather than off-the-shelf solutions. The 2.3x improvement in credit default risk identification alone could reshape lending practices, while the 65% boost in fraud cases caught may set new industry benchmarks for real-time risk assessment.
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