Topic Taxonomy Generation with LLMs for Enriched Transaction Tagging

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Abstract
This work presents an unsupervised method for tagging banking consumers’ transactions using automatically constructed and expanded topic taxonomies. Initially, we enrich the bank transactions via web scraping to collect relevant descriptions, which are then preprocessed using NLP techniques to generate candidate terms. Topic taxonomies are created using instruction-based fine-tuned LLMs (Large Language Models). To expand existing taxonomies with new terms, we use zero-shot prompting to determine where to add new nodes. The resulting taxonomies are used to assign descriptive tags that characterize the transactions in the retail bank dataset. For evaluation, 12 volunteers completed a two-part form assessing the quality of the taxonomies and the tags assigned to merchants. The evaluation revealed a coherence rate exceeding 90% for the chosen taxonomies. Additionally, taxonomy expansion using LLMs demonstrated promising results for parent node prediction, with F1-scores of 89% and 70% for Food and Shopping taxonomies, respectively.

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