In recent years, generative artificial intelligence (AI) such as large language models represented by ChatGPT has developed rapidly and is widely used in many fields. In addition to the capabilities of traditional AI in analysis, judgment, and decision-making, generative AI has advantages in model structure, data dependency, application scenarios, and interpretability. It can give full play to its creative characteristics and promote the AI industry, promoting the decision-making and analytical type to develop leaps and bounds to the generative type. As the financial industry involves a large amount of text data and requires rapid decision-making, it naturally has a high demand for large language models. Large language models will have a significant impact on the high-quality development of the financial industry. Therefore, it is necessary to further explore how large language models can promote the digital transformation and upgrading of the financial industry by changing the business model of the financial industry. However, despite the broad application prospects of large language models in the financial industry, there is still a lack of systematic literature review to discuss in detail the specific applications, challenges, and potential solutions of large language models in the financial industry.
This paper systematically reviews the three stages of model development: the nascent stage, the consolidation stage, and the explosive stage, clarifying the evolution of large models. It elaborates on the structure of the Transformer to help readers understand the underlying principles of large language models. Furthermore, this paper summarizes the application of large language models in the financial sector and details how these models are driving a paradigm shift in the industry. This includes enhancing productivity, transforming existing human-computer interaction models, and improving the accuracy of information dissemination and retrieval. Using examples from banking, insurance, financial management, and investment, this paper explores the opportunities that large language models bring to the financial industry. Additionally, it addresses the challenges these models face in the financial sector, such as data privacy and security, accuracy and reliability, legal regulations, technical costs, and implementation. This paper proposes corresponding countermeasures to these challenges. Finally, it offers policy recommendations from the perspectives of advancing legislation, optimizing algorithms, and fostering cross-departmental cooperation to accelerate the implementation and application of large models in the financial industry.
This paper is the first to provide a comprehensive overview of the application of large language models in the financial field. Deeply analyzing specific application cases of large language models in the financial field, this article demonstrates the possibilities of large language models in various industries in the financial field. This not only provides valuable reference for practitioners in the financial field but also lays the foundation for subsequent research.
Looking ahead, the development of large language models in the financial sector remains full of infinite possibilities. Perhaps they can deeply participate in the R&D of innovative financial products, bringing more personalized and precise services to the financial market. With technological iterations, the data processing capabilities and analytical accuracy of large language models are expected to further improve, better addressing the complex and ever-changing financial environment. Additionally, it is anticipated that more comprehensive industry standards and regulations will be introduced in the future, promoting the compliant and robust application of large language models in the financial sector.