Large Language Model Application for Regulatory Horizon Scanning : Case Study on Anti-Greenwashing Regulations

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Zhang, Hao and Dao, Daniel and Bowden, James and Cummins, Mark (2025) Large Language Model Application for Regulatory Horizon Scanning : Case Study on Anti-Greenwashing Regulations. University of Strathclyde, Glasgow. (https://doi.org/10.17868/strath.00092580)

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Abstract

This white paper explores the application of Generative AI, specifically Large Language Models (LLMs), to enhance regulatory horizon scanning within financial services. Using the Financial Conduct Authority’s (FCA) 2024 anti-greenwashing rule as a case study, we demonstrate how LLMs can be integrated into the strategic foresight process to detect early regulatory signals, analyse stakeholder feedback, and forecast future regulatory developments. Our framework builds upon the traditional horizon scanning model, comprising exploration, assessment, application, and continuation, and incorporates advanced text analysis techniques including semantic similarity testing with models such as BERT and RoBERTa. The study shows that LLMs can significantly improve the efficiency, accuracy, and scalability of horizon scanning by extracting meaningful insights from large, unstructured datasets. The results highlight the potential of LLM-driven foresight to enhance regulatory preparedness, guide compliance strategies, and inform policy design in an increasingly complex and dynamic regulatory environment.

ORCID iDs

Zhang, Hao ORCID logoORCID: https://orcid.org/0009-0002-1021-7476, Dao, Daniel ORCID logoORCID: https://orcid.org/0000-0001-9449-8003, Bowden, James ORCID logoORCID: https://orcid.org/0000-0002-0419-1882 and Cummins, Mark ORCID logoORCID: https://orcid.org/0000-0002-3539-8843;
CORE (COnnecting REpositories)