Application of Large Models in the Financial Field

Jiayi Liang ()
Additional contact information
Jiayi Liang: Shen Zhen University, Institute of Financial Technology

A chapter in Proceedings of the 2025 10th International Conference on Financial Innovation and Economic Development (ICFIED 2025), 2025, pp 505-513 from Springer

Abstract: Abstract This article discusses the application status, challenges and future prospects of large models in financial field. Large model, as a deep learning model with many parameters, has already achieved remarkable results in many areas like speech recognition, image processing and natural language processing and starts to be applied in many scenarios such as investment, risk control and insurance in the financial industry. This article first introduces the current application status of financial large models, including model application, model direction innovation, cooperation and coexistence. Then, taking Ping An Bank as an example, the specific application of large models in financial scenarios is analyzed in detail, including model introduction, structure and application cases. Additionally, the article also discusses the SWOT analysis of large models in the financial field, analyzing its strengths, weaknesses, opportunities and threats. In the case of Ping An Bank, this article introduces the structural framework of the Ping An Bank GPT large model to explain how the large model can be used in scenarios such as intelligent early warning, analysis and monitoring, and its role in improving the efficiency of risk control personnel, risk early warning capabilities and response potential for fraud capabilities. However, large model also have to face the challenges in the financial field, such as lacking of professional data, high training costs and the black box effect [1] of the model. The outlook part of the article points out that the application of large models and artificial intelligence in the financial industry is a general trend, and the growth in market size also proves this. Financial institutions and technology service providers need to ensure the credibility of the data used for algorithm training and build a reasonable data processing environment. At the same time, financial institutions can prioritize lower-risk projects for trial and error to improve the data and algorithms of large models. Last, the article provides resources for further reading through references, covering multiple applications and challenges of large models in finance, and how these challenges can be addressed through technical and regulatory measures.

Keywords: Large Model; Artificial Intelligent; Financial Industry; Landing Scene (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-702-1_53

Ordering information: This item can be ordered from
http://www.springer.com/9789464637021

DOI: 10.2991/978-94-6463-702-1_53

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().