Research on the Application of Artificial Intelligence in Risk Management —— Take the Banking Industry As An Example
- DOI
- 10.2991/978-94-6463-706-9_44How to use a DOI?
- Keywords
- artificial intelligence; risk management; banking industry; management means
- Abstract
Artificial intelligence technology can mine and analyze the data, and be applied to the field of risk management. It can accurately predict the customer risk, fraud, credit, etc., so as to help banks to better identify the potential risks of customers. This paper introduces the application of artificial intelligence in banking risk management, found that the current bank bank risk control management system management organization is not sound, the lack of intelligent management means, personnel post responsible for the system is not perfect, in view of the above problems, yes, the development trend of artificial intelligence technology in banking application research, build the bank intelligent risk control management system, upgrade intelligent management means, network personnel setting, in order to for our country banking using AI technology to better improve risk management ability to provide reference.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xiangzhen Wang PY - 2025 DA - 2025/05/07 TI - Research on the Application of Artificial Intelligence in Risk Management —— Take the Banking Industry As An Example BT - Proceedings of the 2024 2th International Conference on Economic Management, Financial Innovation and Public Service (EMFIPS 2024) PB - Atlantis Press SP - 494 EP - 507 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-706-9_44 DO - 10.2991/978-94-6463-706-9_44 ID - Wang2025 ER -