Large-scale language models (LLMs) have the potential to improve efficiency and security in the financial sector by detecting fraud, generating financial insights, and automating customer service, according to research from the Alan Turing Institute. there is.
LLM's ability to quickly analyze large amounts of data and generate consistent text has the potential to improve services in a wide range of fields, including healthcare, law, education, and financial services such as banking, insurance, and financial planning. My understanding of sexuality is deepening.
This report is the first to examine the adoption of LLM across the financial ecosystem, with those working in the sector already starting to use LLM to support various internal processes such as regulatory reviews. This shows that the evaluation is being carried out. Possibility to support external activities such as the provision of advisory and trading services.
Alongside the literature review, the researchers held a workshop with 43 experts from major trunk and investment banks, regulators, insurance companies, payment service providers, government and legal experts.
The majority of workshop attendees (52%) are already using these models to improve performance on information-oriented tasks, from meeting note management to cybersecurity and compliance insights, and 29 % use it to improve critical thinking skills, and a further 16 people use these models. % employ them to break down complex tasks.
Systems have already been established in this field that increase productivity through rapid analysis of large volumes of text, simplify decision-making processes and risk profiling, and improve investment research and back-office operations.
When asked about the future of LLMs in the financial sector, participants felt that LLMs would be integrated into services such as investment banking and venture capital strategy development within two years.
They also believed that LLM has great potential to be integrated to improve interactions between humans and machines. For example, dictation and built-in AI assistants can reduce the complexity of knowledge-intensive tasks such as regulatory reviews.
However, participants also acknowledged that the technology risks limiting its use. Financial institutions are subject to extensive regulatory standards and obligations that limit their ability to use unaccountable AI systems to produce output predictably, consistently, or without risk of error.
Based on their findings, the authors recommend that financial services professionals, regulators and policy makers collaborate across sectors to share knowledge on the implementation and use of LLM, particularly as it relates to safety concerns. We recommend that you develop it. It also suggests that growing interest in open source models should be considered to ensure they can be used and maintained effectively, but mitigating security and privacy concerns will be a top priority. Masu.
Professor Kirsten Maple, lead author and Turing Fellow at the Alan Turing Institute, said: “Banks and other financial institutions have always been quick to adopt new technologies to make their operations more efficient, and the advent of the LLM is no different. By bringing together experts from across the financial ecosystem, , we were able to create a common understanding of the use cases, risks, value, and timelines for implementing these technologies at scale.”
Professor Lukasz Spruch, Director of the Finance and Economics Program at the Alan Turing Institute, said: For other fields. This study demonstrates that it is beneficial for research institutions and industry to work together to assess the vast opportunities and practical and ethical challenges of new technologies and ensure their safe introduction. ”
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