Identification and prevention of financial securities fraud based on deep learning

Article type: Research Article

Authors: Guo, Debing

Affiliations: School of Business, Guilin Tourism University, Guilin, China | E-mail: debing_guo@outlook.com

Correspondence: [*] Corresponding author: School of Business, Guilin Tourism University, Guilin, China. E-mail: debing_guo@outlook.com.

Abstract: Financial securities fraud is one of the serious problems facing the global financial market at present, which not only destroys the fairness of the market, but also has a serious negative impact on investors and the economic system. The aim of this research is to develop and implement a deep learning-based approach to the identification and prevention of financial securities fraud. Firstly, the definition, types and characteristics of financial securities fraud are deeply discussed, and a financial securities fraud detection model is constructed with the help of deep learning technology. The model is trained, tested and optimized by collecting and preprocessing large amounts of securities trading data and corporate financial reporting data. The empirical results show that our model has high accuracy and precision in the task of financial securities fraud detection. However, this study also reveals some challenges and limitations, such as problems with the model’s interpretability and adaptability to novel fraud strategies. Nevertheless, we believe that as deep learning technology is further developed and improved, its application in financial securities fraud identification and prevention will become more widespread and effective.

Keywords: Financial securities fraud, deep learning, fraud recognition model, data preprocessing

DOI: 10.3233/JCM-247497

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 4-5, pp. 2673-2688, 2024