Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment

In the era of big data, it is aimed to use big data technology to form an effective early warning and prevention of Internet credit. The BP neural network algorithm is applied to determine the number of nodes, activation function, learning rate, and other parameters of each layer of the BP neural network. Also, many data samples are used to build an early warning model of Internet credit risk. The constructed model is trained and tested. Finally, the genetic algorithm (GA) is used to optimize the neural network to improve the accuracy of early warning. The results show that based on 450 data samples from 90 enterprises over five years and the risk interval divided by the 3 sigma rule, the Internet credit risk level is initially determined. Then, the neural network is trained and tested. The network output rate is as high as 85%. To avoid the defect of the BP neural network falling into local extreme value, GA is used to optimize the neural network. The warning is more accurate, and the error is smaller. The accuracy rate can reach 97%. Therefore, the use of BP neural network for early warning and assessment of Internet credit risk has good accuracy and computing efficiency, which 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. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

In this study, a BP neural network algorithm is used to construct an early warning model for Internet credit risk, and then optimized using genetic algorithms to achieve a 97% accuracy rate. Through training and testing with a large number of data samples, the results show that using neural networks for early warning and evaluation has great potential in the field of Internet finance.