LLMs in Banking: Applications, Challenges, and Approaches

This study systematically explores the applications, challenges, and strategic approaches of Large Language Models (LLMs) in the banking industry. The paper first constructs an analytical framework that integrates the business value chain with technical dimensions, identifying and evaluating extensive application scenarios of LLMs in banking. The paper then delves into key challenges, such as data privacy and security, model interpretability, algorithmic bias, and system integration, and proposes corresponding strategies. Additionally, it provides strategic recommendations for banks implementing LLMs, emphasizing system planning, data ecosystem construction, and compliance management. This paper aims to offer both theoretical insights and practical guidance for banks seeking to leverage LLMs for technological transformation and competitive advantage.