From transaction sequences to pricing decisions at 139M-customer scale

LLMs proved that scale and compute can turn raw sequences into general-purpose intelligence. At Nu, we've been pointing that same playbook at a very different sequence, a person's financial history, and asking what it takes to build the production systems around it that responsibly shape the decisions that Nu makes across its 139 million customers in Latin America. This talk is a look at what it takes to build a modeling and decisioning engine that Nu can depend on. It spans our data research to represent transactions as a language, our foundation model, nuFormer, that learns rich customer representations from these trillions of transactions, and ends with the constrained optimization that turns those representations into decisions that determine how we offer and price products for our customers.