Internet Financial Risk Management Under the development of Deep Learning - WU, ZIAI; ZHOU, QIAO (Pacific International Journal, 2023)
With the rapid development of Internet finance in both quantity and scale, various challenges have emerged. Deep learning is a promising tool to explore the optimal algorithm to analyze input variables that affect Internet financial risks, and corresponding classification to manage and minimize those risks. This study employs a questionnaire and data analysis to evaluate Internet financial risks, with a focus on psychological risk, social risk, technical risk, moral risk, and material risk. The research provides theoretical and practical value in the field of Internet financial risk management. The results of the study demonstrate that psychological risk, social risk, technical risk, material risk, and moral risk are all significant factors that contribute to Internet financial risks. Additionally, higher education is found to be a protective factor against Internet financial risks, while higher income is associated with greater risk. Furthermore, psychological risks were found to have the most significant impact on Internet financial risks.