Mastercard keeps tabs on fraud with new foundation model

Mastercard has developed a large tabular model (an LTM as opposed to an LLM) that’s trained on transaction data rather than text or images to help it address security and authenticity issues in digital payments.

The company has trained a foundation model on billions of card transactions, with the intention of expanding to hundreds of billions in time. The datasets include payment events and associated data such as merchant location, authorisation flows, fraud incidents, chargebacks, and loyalty activity. Mastercard says personal identifiers are removed before the training began, and that the model parses behavioural patterns rather than concern itself with individual identities.

By excluding personal data, the technology reduces privacy risks that may affect other forms of AI in financial services sector. The scale and richness of the data allow the model to infer patterns that are commercially valuable – the company said in a recent blog post – despite the lack of per-user information. Although anonymisation removes signals that could be argued as being useful in the area of risk assessment, Mastercard asserts that using sufficiently large volumes of behavioural data compensates for an...