Abstract
Due to the generality and particularity of Internet financial risks, it is imperative for the institutions involved to establish a sound risk prevention, control, monitoring, and management system and timely identify and alert potential risks. Firstly, the importance of Internet financial risk monitoring and evaluation is expounded. Secondly, the basic principles of backpropagation (BP) neural network, genetic algorithm (GA), and GABP algorithms are discussed. Thirdly, the weight and threshold of the BP algorithm are optimized by using the GA, and the GABP model is established. The financial risks are monitored and evaluated by the Internet financial system as the research object. Finally, GABP is further optimized by the simulated annealing (SA) algorithm. The results show that, compared with the calculation results of the BP model, the GABP algorithm can reduce the number of BP training, has high prediction accuracy, and realizes the complementary advantages of GA and BP neural network. The GABP network optimized by simulated annealing method has better global convergence, higher learning efficiency, and prediction accuracy than the traditional BP and GABP neural network, achieves better prediction effect, effectively solves the problem that the enterprise financial risk cannot be quantitatively evaluated, more accurately assesses the size of Internet financial risk, and has certain popularization value in the application of Internet financial risk prediction.
Highlights
Internet finance refers to using Internet technology and mobile communication to implement financing, a new financial model of information intermediary, payment, and other businesses, and has the characteristics of universality, low cost, and information; at present, the development model mainly includes the raise, P2P network platform, the Internet money market funds, loan third-party payment, and digital currency [1]
One of the reasons for the high judgment rate is that the neural network has good knowledge discovery and feature extraction capabilities, another reason may be that all data are interpolated data, and the neural network model has a slightly poor effect on extrapolating data. e global search ability of genetic algorithm (GA) is used to optimize the structural parameters of the BP network. erefore, the Internet financial risk
From the calculation process. e optimized model trained for 2,000 times can achieve better results than the simple BP model trained for 5,000 times. e combination is more conducive to the application of the Artificial Neural Network (ANN) model in Internet financial risk prediction
Summary
Internet finance refers to using Internet technology and mobile communication to implement financing, a new financial model of information intermediary, payment, and other businesses, and has the characteristics of universality, low cost, and information; at present, the development model mainly includes the raise, P2P network platform, the Internet money market funds, loan third-party payment, and digital currency [1]. As the main product of “Internet + finance,” has the same risk with traditional financial risk in principle. In addition to traditional financial risks, Internet finance has the characteristics of fast product innovation, continuous expansion of user groups, and rapid expansion of capital scale. 2. Important Role of GABP Neural Network in the Field of Internet Financial Risk Supervision. Quantification of risk rating can effectively transform the behavioral process in the field of Internet finance into quantitative indicators, making risk avoidance work clearer and more grounded [5]. Most Internet financial enterprises have not established neural network risk control monitoring and evaluation system. Due to the generality and particularity of Internet financial risks, it is urgent for the institutions involved to establish a set of sound risk prevention, control, and monitoring and management system. By extracting the profitability, operating capacity, and debt paying capacity of the loan enterprise as the network input, the network output results are normal and alarm
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