According to the latest research from the Alan Turing Institute, Large Language Models (LLMs) have shown great potential in the financial sector, improving efficiency and security by detecting fraud, generating financial insights, and automating customer service. Due to their ability to quickly analyze large amounts of data and generate coherent text, LLMs are increasingly recognized for their potential in improving services in various fields such as healthcare, law, and education, particularly in the financial services sector including banking, insurance, and financial planning. This report is the first comprehensive exploration of the adoption of LLMs in the financial ecosystem, revealing that professionals in the financial sector have begun utilizing LLMs to support various internal processes such as regulatory review and are evaluating their potential in supporting external activities such as providing consulting and trade services. In addition to literature reviews, researchers also held a workshop with 43 professionals from major commercial banks, investment banks, regulatory agencies, insurance companies, payment service providers, government, and the legal industry. Among the workshop participants, 52% are already using LLMs to enhance performance in information-driven tasks, ranging from conference record management to cybersecurity and compliance insights. 29% utilize them to improve critical thinking skills, while 16% use LLMs to break down complex tasks. The financial industry has also established systems that utilize LLMs to rapidly analyze large amounts of text to enhance productivity, simplifying decision-making processes, risk analysis, and improving investment research and back-office operations. When discussing the future of LLMs in the financial sector, participants generally believe that LLMs will be integrated into services such as investment banking and venture capital strategy development within the next two years. They also believe that LLMs may be integrated to improve human-machine interaction, such as transcription and embedded AI assistants, and reduce the complexity of knowledge-intensive tasks like regulatory review. However, participants also acknowledge the risks associated with LLM technology, which will limit its usage. Financial institutions are constrained by strict regulatory standards and obligations, limiting their ability to use unexplainable AI systems, especially when these systems cannot generate outputs in a predictable, consistent, or error-free manner. Based on these findings, the report authors recommend that financial service professionals, regulatory agencies, and policymakers strengthen collaboration, share and develop knowledge about the implementation and use of LLMs, particularly in relation to security issues. They also suggest exploring the growing interest in open-source models and effectively using and maintaining these models, with addressing security and privacy concerns as a top priority. Professor Kesten Meepel, the lead author at the Alan Turing Institute, stated, "Banks and other financial institutions are always quick to adopt new technologies to improve operational efficiency, and LLMs are no exception. By bringing together experts from the financial ecosystem, we have successfully reached a consensus on the use cases, risks, value, and timeline for large-scale implementation of these technologies." Professor Lukasz Szpruch, the Director of the Financial and Economic Program at the Alan Turing Institute, also pointed out, "The financial industry is benefiting from the emergence of LLMs, and their implementation in this highly regulated sector may provide best practices that are positive signals for other sectors. This research demonstrates the enormous opportunities for research institutions and industry collaboration in assessing new technologies, as well as the benefits of ensuring secure implementation in terms of practical and ethical challenges."