Every swipe, transfer, and payment on a modern financial network encodes a pattern of human behavior. Transaction data is one of the richest signals an enterprise owns. Yet most production use cases for such tabular data still depend on hand-engineered features and rule sets that are brittle, expensive to maintain, and blind to the sequential structure inside a customer history.

Foundation models, pre-trained on large volumes of unlabeled transaction sequences, change this equation by producing general-purpose representations of financial behavior that transfer across a wide array of downstream tasks. A single backbone covers fraud detection, credit scoring, lifetime value prediction, segmentation, personalized recommendations, recurrent-transaction detection, and more.

The industry signal is strong and accelerating.…