{"id":3670652,"date":"2026-04-21T13:49:41","date_gmt":"2026-04-21T17:49:41","guid":{"rendered":"https:\/\/www.pymnts.com\/?p=3670652"},"modified":"2026-04-21T19:58:42","modified_gmt":"2026-04-21T23:58:42","slug":"revolut-stopped-paying-for-ai-and-started-winning","status":"publish","type":"post","link":"https:\/\/www.pymnts.com\/artificial-intelligence-2\/2026\/revolut-stopped-paying-for-ai-and-started-winning\/","title":{"rendered":"Revolut Skips OpenAI, Trains AI on 25 Million Customers"},"content":{"rendered":"<p style=\"font-weight: 400;\">Most banks pay OpenAI or Anthropic for access to artificial intelligence. <a href=\"https:\/\/www.revolut.com\/en-US\/\" target=\"_blank\" rel=\"noopener\">Revolut<\/a> built its own.<\/p><div\r\n\tid=\"article-paywall-overlay\"\r\n\tclass=\"d-none\"\r\n>\r\n\t<div id=\"article-paywall-content\" class=\"my-4\">\r\n\t\t\n<div class=\"wpcf7 no-js\" id=\"wpcf7-f2699550-o1\" lang=\"en-US\" dir=\"ltr\" data-wpcf7-id=\"2699550\">\n<div 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the form to unlock this article and enjoy unlimited free access to all PYMNTS content \u2014 no additional logins required.\n\t<\/p>\n\t<div id='registrationContainer' class='container px-0'>\n\t\t<div class=\"row\">\n\t\t\t<div class=\"col-12 col-md-6\">\n\t\t\t\t<p><span class=\"wpcf7-form-control-wrap\" data-name=\"firstName\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-text wpcf7-validates-as-required form-control border-secondary\" id=\"firstName\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"First Name*\" value=\"\" type=\"text\" name=\"firstName\" \/><\/span>\n\t\t\t\t<\/p>\n\t\t\t<\/div>\n\t\t\t<div class=\"col-12 col-md-6\">\n\t\t\t\t<p><span class=\"wpcf7-form-control-wrap\" data-name=\"lastName\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-text wpcf7-validates-as-required form-control border-secondary\" id=\"lastName\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"Last Name*\" value=\"\" 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\/>\n\t\t\t<input class=\"wpcf7-form-control wpcf7-hidden\" value=\"\" type=\"hidden\" name=\"email\" \/>\n\t\t\t<\/div>\n\t\t\t<div class=\"col-12\">\n\t\t\t\t<p><span class=\"wpcf7-form-control-wrap\" data-name=\"newsLetterChoice\"><span class=\"wpcf7-form-control wpcf7-checkbox me-1\" id=\"checkNewsletter\"><span class=\"wpcf7-list-item first last\"><input type=\"checkbox\" name=\"newsLetterChoice[]\" value=\"yes\" checked=\"checked\" \/><span class=\"wpcf7-list-item-label\">yes<\/span><\/span><\/span><\/span><span class=\"small\">Subscribe to our daily newsletter, PYMNTS Today.<\/span>\n\t\t\t\t<\/p>\n\t\t\t\t<p class=\"small lh-base\" style=\"font-size:.9rem;\">By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our <a class=\"fw-bold\" href=\"https:\/\/pymnts-com-develop.go-vip.net\/privacy-policy\/\">Privacy Policy<\/a> and <a class=\"fw-bold\" href=\"https:\/\/pymnts-com-develop.go-vip.net\/terms-conditions\/\">Terms and Conditions<\/a>.\n\t\t\t\t<\/p>\n\t\t\t\t<p><input id='hiddenPath' type='hidden' name='path' value='' \/><input type='hidden' name='userDeviceId' id='userDeviceId' \/><input type='hidden' name='pageTitle' id='pageTitle' \/>\n\t\t\t\t<\/p>\n\t\t\t<\/div>\n\t\t\t<div class=\"col-12\">\n\t\t\t\t<div id='formButtonRow' class='container mt-1 px-0'>\n\t\t\t\t\t<p><input class=\"wpcf7-form-control wpcf7-submit has-spinner btn btn-dark text-uppercase py-2 px-5 small\" id=\"theSubmitButton\" type=\"submit\" value=\"Submit\" \/>\n\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/div><input type=\"hidden\" name=\"post_id\" value=\"3670652\" \/><input type=\"hidden\" name=\"source\" value=\"https:\/\/www.pymnts.com\/artificial-intelligence-2\/2026\/revolut-stopped-paying-for-ai-and-started-winning\/\" \/><p style=\"display: none !important;\" class=\"akismet-fields-container\" data-prefix=\"_wpcf7_ak_\"><label>&#916;<textarea name=\"_wpcf7_ak_hp_textarea\" cols=\"45\" rows=\"8\" maxlength=\"100\"><\/textarea><\/label><input type=\"hidden\" id=\"ak_js_1\" name=\"_wpcf7_ak_js\" value=\"41\"\/><script>document.getElementById( \"ak_js_1\" ).setAttribute( \"value\", ( new Date() ).getTime() );<\/script><\/p><div class=\"wpcf7-response-output\" aria-hidden=\"true\"><\/div>\n<\/form>\n<\/div>\n\r\n\t<\/div>\r\n<\/div>\r\n<div id=\"article-paywall-hidden-content\">\r\n\t\n<p style=\"font-weight: 400;\">The London-based neobank trained a proprietary model called PRAGMA on 40 billion transactions, app interactions and financial events drawn from 25 million users across 111 countries. It doesn\u2019t answer questions or write emails. It makes decisions: who\u2019s likely to commit fraud, who\u2019s a credit risk, which customers are about to leave. According to <a href=\"https:\/\/arxiv.org\/abs\/2604.08649\">Revolut<\/a>, PRAGMA outperforms every task-specific system the company previously ran.<\/p>\n<p style=\"font-weight: 400;\">Mastercard <a href=\"https:\/\/www.mastercard.com\/us\/en\/news-and-trends\/stories\/2026\/mastercard-new-generative-ai-model.html\">announced in March<\/a> that it\u2019s building a generative AI model on hundreds of billions of anonymized payment transactions, with applications spanning fraud, loyalty and portfolio analytics. Plaid introduced a transaction model built to standardize how merchant data is read across institutions. As PYMNTS has <a href=\"https:\/\/www.pymnts.com\/artificial-intelligence-2\/2026\/fintechs-race-to-build-foundation-models-on-proprietary-data\/\">reported<\/a>, the companies that process the world\u2019s payments are now training AI on the structured transaction data they\u2019ve spent decades collecting.<\/p>\n<p style=\"font-weight: 400;\">PRAGMA sits apart from that group in two ways. Mastercard and Visa operate as networks: they see transactions, but not the full arc of a customer\u2019s financial life. Plaid sees account data, but its model targets a specific problem, merchant name standardization, rather than a broad decisioning layer.<\/p>\n<p style=\"font-weight: 400;\">Revolut, as a neobank, holds the complete picture. It sees where customers spend, what they trade, how they move money internationally and how they navigate the app before and after each transaction. PRAGMA is trained on all of it, fused into a single representation of each user.<\/p>\n<h2 style=\"font-weight: 400;\"><strong>Why General-Purpose AI Falls Short in Finance<\/strong><\/h2>\n<p style=\"font-weight: 400;\">AI models like ChatGPT learn by reading text. Feed them enough of the internet and they get good at predicting what word comes next. That makes them useful for drafting, summarizing and answering questions in plain language. It doesn\u2019t make them useful for fraud detection.<\/p><div id=\"pymnt-599953474\" class=\"pymnt-content pymnt-entity-placement\" style=\"margin-top: 50px;\"><p style=\"text-align:center\">Advertisement: Scroll to Continue<\/p><script async type=\"text\/javascript\" id=\"dianomi_context_script\" src=\"https:\/\/www.dianomi.com\/js\/contextfeed.js\"><\/script> \r\n<div class=\"dianomi_context\" data-dianomi-context-id=\"4329\"><\/div><\/div>\n<p style=\"font-weight: 400;\">The reason is that financial data isn\u2019t text. A bank\u2019s record of a customer\u2019s behavior is a sequence of structured events: a $14.99 subscription charge on a Tuesday, a $150 transfer on Christmas Day, a salary deposit the following Friday. Each event has a type, an amount, a currency, a timestamp and a relationship to every other event in the customer\u2019s history. When you feed that into a text-based AI, it reads the dollar signs and digits the same way it reads letters. A $1,000 transfer and a $10 transfer look nearly identical. The patterns that signal fraud or credit risk get lost.<\/p>\n<p style=\"font-weight: 400;\">Speed is the other problem. A fraud check has to complete in milliseconds, before a transaction clears. General-purpose AI models are built for depth, not for that kind of real-time decisioning at scale.<\/p>\n<h2 style=\"font-weight: 400;\"><strong>One Model That Does What Dozens Used To<\/strong><\/h2>\n<p style=\"font-weight: 400;\">PRAGMA is built differently. It reads each financial event as three things: what happened, the specific details and when. It learns patterns across a customer\u2019s entire history. A customer who topped up their account, made a small card payment, then sent an international transfer, then went quiet represents a pattern. PRAGMA learns to recognize what that pattern typically precedes.<\/p>\n<p style=\"font-weight: 400;\">According to Revolut, the model was trained on 207 billion data points spanning 25 months of real customer activity. It comes in three sizes, from a lean 10 million parameter version built for real-time fraud checks to a 1 billion parameter version for decisions where accuracy matters more than speed. All three run on more than 200 NVIDIA H100 GPUs, as detailed by <a href=\"https:\/\/nebius.com\/customer-stories\/revolut\">Nebius<\/a>, Revolut\u2019s cloud infrastructure partner.<\/p>\n<p style=\"font-weight: 400;\">The operational payoff is consolidation. Most financial institutions run a separate AI model for each problem: one for fraud, one for credit scoring, one for customer lifetime value, one for product recommendations. Each requires its own training data, its own maintenance, and its own engineering team. PRAGMA replaces that entire stack with a single shared foundation. Revolut adapts it to a new task by adjusting a small fraction of the model\u2019s parameters rather than building from scratch, cutting retraining cycles and accelerating deployment across the business.<\/p>\n<div>\n<p class=\"elementtoproof\"><i>For all PYMNTS AI coverage, subscribe to the daily<span class=\"apple-converted-space\">\u00a0<\/span><\/i><a title=\"https:\/\/www.pymnts.com\/subscribe\/\" href=\"https:\/\/www.pymnts.com\/subscribe\/\">AI\u00a0Newsletter<\/a><i>.<\/i><\/p>\n<\/div>\n\r\n<\/div>\r\n<script>\r\n\twindow.pymntsAllowedArticleCount = 2;\r\n\twindow.pymntsPostID = 3670652;\r\n<\/script>","protected":false},"excerpt":{"rendered":"<p>Most banks pay OpenAI or Anthropic for access to artificial intelligence. Revolut built its own. Get the Full Story Complete the form to unlock this article and enjoy unlimited free access to all PYMNTS content \u2014 no additional logins required. yesSubscribe to our daily newsletter, PYMNTS Today. By completing this form, you agree to receive [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1456050,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[65579],"tags":[9498,5559,7617,54394,9206,133069,14073],"class_list":["post-3670652","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-2","tag-ai","tag-digital-banking","tag-fintech","tag-neobanks","tag-news","tag-pymnts-news","tag-revolut"],"acf":{"suggested_titles":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.2 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Revolut Skips OpenAI, Trains AI on 25 Million Customers<\/title>\n<meta name=\"description\" content=\"Revolut trained its AI model on transactions, trades, app behaviors and international money movement from 25 million users in 111 countries.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.pymnts.com\/artificial-intelligence-2\/2026\/revolut-stopped-paying-for-ai-and-started-winning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Revolut Skips OpenAI, Trains AI on 25 Million Customers | PYMNTS.com\" \/>\n<meta property=\"og:description\" content=\"Most banks pay OpenAI or Anthropic for access to artificial intelligence. 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