Internet Financial Risk Management Study

Internet Financial Risk Management Study

Driven by technological change, the Internet has rapidly integrated with the traditional financial sector of the economy, and has continued to advance and develop new features. The Internet is no longer simply providing support and services to traditional finance in terms of technology and operation mode, but has continuously penetrated the traditional financial field, resulting in an emerging financial system that breaks the traditional model of financial intermediation, eliminates information asymmetry, reduces transaction costs and improves payment efficiency. This new financial model enables information sharing, resources sharing, economic activities facilitation and scale effect formation. Internet finance can play an important role in areas that traditional finance can not, and thus can better serve the economy and promote the development of human society. However, Internet finance as a new financial model in the process of rapid development will certainly be accompanied by hidden risks of various kinds, and has a huge impact on the traditional financial industry, financial markets and financial regulators. Therefore, this thesis raises the important issue of risk management research in Internet finance. The development of the financial industry under the Internet technology revolution is studied. Based on the characteristics of Internet finance, the types of risks that are different from those of traditional finance are identified. Based on the qualitative analysis of the identification of the types of Internet financial risks, the quantitative analysis method is used to establish the evaluation index system and do quantitative evaluation analysis of Internet financial risks. The use of BP (Back Propagation) neural network expands the application of BP neural network in the field of Internet finance, and provides a new development direction for the early warning and assessment of Internet credit risk. Comparative analysis of ML (Machine Learning) and DL (Deep Learning) algorithms in Internet credit risk assessment is used and explored to improve the accuracy of financial prediction. The exploration of edge computing and blockchain technology intends to solve the great security risks in the financial network transaction process and the certain restrictions on the current use of blockchain technology in mobile terminal equipment. The research object of this thesis is Internet financial risk management. Under the current social, economic and financial environment, it explores the theoretical basis of Internet financial risk management, and analyses the risk management framework and methods of Internet financial risk assessment, risk warning and risk control. The aim is to achieve healthy and stable development of the Internet finance industry, thus contributing to the real economy and the promotion of human social progress.

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