Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment
Article
Article Title | Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment |
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ERA Journal ID | 228 |
Article Category | Article |
Authors | Du, Guansan, Liu, Zixian and Lu, Haifeng |
Journal Title | Journal of Computational and Applied Mathematics |
Journal Citation | 386 |
Article Number | 113260 |
Number of Pages | 11 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0377-0427 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cam.2020.113260 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0377042720305513 |
Abstract | 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 ‘‘3σ’’ 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. |
Keywords | Big data technology; BP neural network; Internet credit; Risk early warning model |
Related Output | |
Is part of | Internet Financial Risk Management Study |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Files associated with this item cannot be displayed due to copyright restrictions. | |
Byline Affiliations | University of Southern Queensland |
Liaoning University, China | |
People’s Bank of China, China |
https://research.usq.edu.au/item/z1ww0/application-of-innovative-risk-early-warning-mode-under-big-data-technology-in-internet-credit-financial-risk-assessment
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