Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network
Author
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-02
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
The arrival of the era of big data has provided a new direction of development for internet financial credit collection.
First of all, the article introduced the situation of internet finance and traditional credit industry.
Based on that, the mathematical model was used to demonstrate the necessity of developing big data financial credit information.
Then, the Internet financial credit data are preprocessed, the variables suitable for modeling are selected, and the dynamic credit tracking model of BP neural network based on adaptive genetic algorithm is constructed.
It is found that both LM training algorithm and Bayesian algorithm can converge the error to 10e-6 quickly in the model training, and the overall training effect is ideal.
Finally, the rule extraction algorithm is used to simulate the test samples.
The accuracy rate of each sample method is over 90%, and some accuracy rate is even more than 90%, which indicates that the model is applicable to the credit data of big data in internet finance.
American Psychological Association (APA)
Wang, Fei-Peng. 2018. Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network. Complexity،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1135880
Modern Language Association (MLA)
Wang, Fei-Peng. Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network. Complexity No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1135880
American Medical Association (AMA)
Wang, Fei-Peng. Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network. Complexity. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1135880
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135880