Payments giant Mastercard says it has built its own proprietary generative artificial intelligence model to help thousands of banks in its network detect and root out fraudulent transactions. The company told CNBC exclusively that its new advanced AI model, Decision Intelligence Pro, will allow banks to better assess suspicious transactions on its network in real-time and determine whether they’re legitimate or not. Ajay Bhalla, Mastercard’s president of cyber and intelligence business unit, told CNBC that the new AI solution is a proprietary recurrent neural network — a core part of generative AI — from Mastercard built from scratch by the company’s cybersecurity and anti-fraud teams. “We are using the transformer models which basically help get the power of generative AI,” Bhalla told CNBC in an exclusive interview earlier this week. “It’s all built in house we’ve got all kinds of data from the ecosystem. Because of the very nature of the business we are in, we see all the transaction data which comes to us from the ecosystem.” In some cases, Mastercard is relying on open source “whenever needed,” but the “majority” of the technology is created in house, Bhalla added. Mastercard’s proprietary algorithm is trained on data from the roughly 125 billion transactions that go through the company’s card network annually. The data helps the AI understand relationships between merchants — rather than words, as is the focus with large language models such as OpenAI’s GPT-4 and Google’s Gemini — and predict where fraudulent transactions are taking place, Mastercard said. Instead of textual inputs, Mastercard’s algorithm uses the history of a cardholder’s merchant visit as the prompt to determine whether the business involved in a transaction is a place the customer would likely go. The algorithm then generates pathways through Mastercard’s network — kind of a like heat-sensing radar — to find the answer in the form of a score. A higher score would be one that follows the pattern of what’s the usual kind of behavior expected from the cardholder, and a lower score is out of that pattern.