Affiliations: [a] Department of Management Engineering, Hebei Petroleum University of Technology, Chengde, Hebei, China
| [b] Health and Rehabilitation College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
Correspondence:
[*]
Corresponding author. Rui Ma, Health and Rehabilitation College,Chengdu University of Traditional Chinese Medicine, Chengdu, 6111137, Sichuan, China. E-mail: [email protected].
Abstract: With the deepening development of the financial market, the role of regulatory systems in ensuring green and safe financial environment is becoming increasingly prominent. The traditional intelligent financial regulatory systems on the market lack precise and effective real-time monitoring and recognition capabilities, making it difficult to effectively process and analyze large-scale financial data. In order to improve the real-time recognition of abnormal situations or potential risks, achieve automation and intelligence of supervision, this article combines deep learning technology to study the deep practice of IoT image recognition technology in intelligent financial supervision systems. In response to the “data silos” and cross regional linkage issues faced by financial industry regulation, this article designs and implements an intelligent regulatory system based on IoT image recognition technology through deep learning. Using Convolutional Neural Network (CNN) algorithm to classify and analyze system images for regulatory and risk control purposes. The research results indicate that the intelligent financial regulatory system constructed in this article has high stability and responsiveness, which can effectively meet the real-time regulatory needs of finance and help promote the healthy development of the financial market.
Keywords: Financial supervision system, internet of things, image recognition
technology, deep learning, artificial intelligence
