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Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network

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  • Fei-Peng Wang

Abstract

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.

Suggested Citation

  • Fei-Peng Wang, 2018. "Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network," Complexity, Hindawi, vol. 2018, pages 1-16, December.
    • Handle: RePEc:hin:complx:7616537
    DOI: 10.1155/2018/7616537
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    References listed on IDEAS

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    1. Kusi, Baah Aye & Agbloyor, Elikplimi Komla & Ansah-Adu, Kwadjo & Gyeke-Dako, Agyapomaa, 2017. "Bank credit risk and credit information sharing in Africa: Does credit information sharing institutions and context matter?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1123-1136.
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    Cited by:

    1. Min Lin, 2022. "Innovative Risk Early Warning Model under Data Mining Approach in Risk Assessment of Internet Credit Finance," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1443-1464, April.
    2. Fang Liu & Hua Gong & Ligang Cai & Ke Xu, 2019. "Prediction of Ammunition Storage Reliability Based on Improved Ant Colony Algorithm and BP Neural Network," Complexity, Hindawi, vol. 2019, pages 1-13, March.

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