Application of SOM-GAN based deep learning technology in the security protection of rural bank depositors' funds information

Abstract

Since the reform and opening up, the status of China's banking industry has been significantly improved, and the traditional banking industry has gradually cross-integrated with Internet technology [1], which has become an important support for national economic development and a core component of the financial system. For example, in the original physical bank and online bank, new Internet financial models have been expanded, such as online financial management, bank electronic accounts, mobile banking, P2P models, third-party payment platforms, crowdfunding, etc. [2]. However, in the period of rapid development of the transformation and upgrading of traditional banks facilitated by information technology, many problems have emerged, such as the relaxation of supervision, the lag of policies and regulations, and the difficulty of reasonable protection of private data, etc. These problems may affect the interests of depositors and even the state. For example, in 2022, China's Henan rural bank 40 billion event, this event involved about 400,000 depositors, and platforms such as Du Xiaoman and Xiaomi Finance have related cooperation with these rural banks. Therefore, reasonable security protection of depositors' fund information and information processing and encryption under certain deep learning technology have long become the focus of experts' research in information technology. For example, Tian Gengwen has conducted an in-depth discussion on customer information security in the digital transformation of banks [3]. For example, Yang Wanrong conducted an in-depth study on the path and measures of data security governance of small and medium-sized banks [4]. For example, Zhang Wenli, Peng Xiaolei et al have made an in-depth elaboration on the bank data security solution [5]. Following hot issues closely, this paper provides a method of deep learning technology based on SOM-GAN in the security protection of rural bank depositors' funds information, aiming to combine the advantages of self-organizing mapping neural network and generative adantagonistic network unsupervised learning. At the same time, it can complement the competition mode of self-organized neural network, generate the discriminant characteristics of adversarial network and generate real data to process and review the relevant data of the fund information security of rural bank depositors, ensure the fund management security of rural bank depositors, and realize the monitoring and analysis of their funds and the surrounding environment when depositors are storing funds. Thus, the security, confidentiality, authenticity and integrity of depositors' funds information are guaranteed, and hidden dangers such as theft of depositors' funds, loss of depositors' personal information and threats to depositors' personal information security brought about by the 40bn deposit red code incident of Henan Bank in 2022 are eliminated.