Internet Financial Risk Monitoring and Evaluation Based on GABP Algorithm
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 monit...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/4807428 |
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| _version_ | 1849691820041175040 |
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| author | Yaqin Guang Shunyong Li Quanping Li |
| author_facet | Yaqin Guang Shunyong Li Quanping Li |
| author_sort | Yaqin Guang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b5a30ac1c6bb40b8acd3bcd1e1ddf1cc |
| institution | DOAJ |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-b5a30ac1c6bb40b8acd3bcd1e1ddf1cc2025-08-20T03:20:55ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/4807428Internet Financial Risk Monitoring and Evaluation Based on GABP AlgorithmYaqin Guang0Shunyong Li1Quanping Li2Shanxi Institute of Socialism (Shanxi Institute of Chinese Culture)School of Mathematical SciencesSchool of History and CultureDue 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.http://dx.doi.org/10.1155/2022/4807428 |
| spellingShingle | Yaqin Guang Shunyong Li Quanping Li Internet Financial Risk Monitoring and Evaluation Based on GABP Algorithm Journal of Mathematics |
| title | Internet Financial Risk Monitoring and Evaluation Based on GABP Algorithm |
| title_full | Internet Financial Risk Monitoring and Evaluation Based on GABP Algorithm |
| title_fullStr | Internet Financial Risk Monitoring and Evaluation Based on GABP Algorithm |
| title_full_unstemmed | Internet Financial Risk Monitoring and Evaluation Based on GABP Algorithm |
| title_short | Internet Financial Risk Monitoring and Evaluation Based on GABP Algorithm |
| title_sort | internet financial risk monitoring and evaluation based on gabp algorithm |
| url | http://dx.doi.org/10.1155/2022/4807428 |
| work_keys_str_mv | AT yaqinguang internetfinancialriskmonitoringandevaluationbasedongabpalgorithm AT shunyongli internetfinancialriskmonitoringandevaluationbasedongabpalgorithm AT quanpingli internetfinancialriskmonitoringandevaluationbasedongabpalgorithm |