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    Frequently Asked Questions

    Common questions about Revolut and their AI solutions.

    What results has Revolut reported from PRAGMA?

    According to Revolut's published material, PRAGMA-based systems improved credit scoring performance by around 130% and lifted fraud recall by about 65% versus prior approaches. A single foundation model backbone serves multiple tasks including risk, fraud, and product recommendations.

    Why did Revolut build its own AI foundation model?

    Banking events are a unique data modality that general-purpose LLMs were never trained on. By building PRAGMA on its own raw data, Revolut gets a single underlying model that can be adapted to credit scoring, fraud detection, and personalization — with reported performance gains far beyond off-the-shelf approaches.

    How does PRAGMA improve fraud detection?

    Instead of scoring transactions in isolation, PRAGMA interprets each event in the context of a user's full behavioral history. A midnight payment reads very differently if it follows a burst of rapid transactions on an unfamiliar device in a new location — contextual patterns that sequence models capture and rule-based systems miss.

    How was PRAGMA trained while protecting user privacy?

    Revolut states PRAGMA was trained on anonymized records from 26 million users across 111 countries, covering 24 billion events. The training data consists of behavioral event sequences rather than personally identifying content, in line with financial data protection requirements.

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