transaction-data

This paper presents a deployed pipeline for semantic user profiling that uses per-transaction-pattern LLM inference to reduce costs by three orders of magnitude at a major Japanese bank.

MINT is a framework that connects pretrained transaction sequence encoders to decoder-only LLMs for zero-shot predictive tasks on financial transaction data, achieving state-of-the-art performance with reduced resources.