## AI-Powered Financial Transaction Analysis Payment networks generate an impressive amount of transactional data, which contains valuable information about consumer and merchant behavior. A new study explores how Large Language Models (LLMs) can be used to improve the analysis of this data. Traditional models often rely on index-based representations for categorical merchant fields, which can lead to a loss of semantic information. LLMs, thanks to their ability to understand natural language, offer a promising alternative, although their computational intensity poses a challenge for real-time financial deployments. ## A Hybrid Framework for Efficiency The research introduces a hybrid framework that uses LLM-generated embeddings as semantic initializations for lightweight transaction models. This approach aims to balance interpretability with operational efficiency. The framework includes data fusion from various sources to enrich merchant categorical fields, a one-word constraint principle for consistent embedding generation across different LLM architectures, and noise filtering and context-aware enrichment techniques to ensure data quality. Experiments conducted on large-scale transactional datasets have demonstrated significant performance improvements across multiple transaction understanding tasks.