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Innovative Risk Early Warning Model under Data Mining Approach in Risk Assessment of Internet Credit Finance

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Abstract

The financial risks of commercial banks are classified and evaluated through the Internet of Things (IoT) technology and big data technology to reduce the financial risk loss of commercial banks in the context of Internet finance. Firstly, based on the analysis of financial risks in the context of IoT technology, an IoT tail loss mathematical model of financial operation risk is constructed to classify the operation risks of commercial banks. Secondly, the BP neural network algorithm is applied to determine the number of nodes, activation function, learning rate, and other parameters of each BP neural network layer. 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 early warning accuracy. The results show that introducing Internet technology can reduce the risk loss of commercial banks. In addition, based on 450 data samples of 90 companies in 5 years and the risk interval divided by the "3σ" rule, the Internet credit risk level was initially determined. Then, the neural network is trained and tested. The prediction accuracy of the neural network reaches 85%. In order to avoid the defects of BP neural network falling into local extreme values, GA is used to optimize the neural network. The warning is more accurate and the error is smaller, and 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.

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References

  • Ai Q. Research on systemic risk of banking industry from the perspective of internet. (2018).

  • Benaim, M. (2018). From symbolic values to symbolic innovation: Internet-memes and innovation. Research Policy, 47(5), 901–910.

    Article  Google Scholar 

  • Beque, A., & Lessmann, S. (2017). Extreme learning machines for credit scoring: An empirical evaluation. Expert Systems with Applications, 86, 42–53. https://doi.org/10.1016/j.eswa.2017.05.050

    Article  Google Scholar 

  • Chen, J. (2019). Big data boosts commercial bank credit business and risk management innovation. Investment and Financing, 12, 62–64.

    Google Scholar 

  • Chi, G., Ding, S., & Peng, X. (2019). Data-driven robust credit portfolio optimization for investment decisions in P2P lending. Mathematical Problems in Engineering, 2019(1), 1–10. https://doi.org/10.1155/2019/1902970

    Article  Google Scholar 

  • Crisosto, C. (2019). Autoregressive neural network for cloud concentration forecast from hemispheric sky images. International Journal of Photoenergy, 2019, 1–8. https://doi.org/10.1007/s11277-020-07173-w

    Article  Google Scholar 

  • Cui, D. (2015). Financial credit risk warning based on big data analysis. Metallurgical & Mining Industry, 7(6), 133–142.

    Google Scholar 

  • David, J. A., & Robert, R. (2016). Supply chain shared risk self-financing for incremental sales. The Engineering Economist, 1, 250–253.

    Google Scholar 

  • Dendramis, Y., Tzavalis, E., & Adraktas, G. (2017). Credit risk modelling under recessionary and financially distressed conditions. Journal of Banking Finance, 91, 23–25. https://doi.org/10.1016/j.jbankfin.2017.03.020

    Article  Google Scholar 

  • Dhillon, S., Madhu, C., Kaur, D., et al. (2020). A solar energy forecast model using neural networks: Application for prediction of power for wireless sensor networks in precision agriculture. Wireless Personal Communications, 112(4), 2741–2760.

    Article  Google Scholar 

  • Gagniuc, P. A., Ionescu-Tirgoviste, C., Gagniuc, E., et al. (2020). Spectral forecast: A general purpose prediction model as an alternative to classical neural networks. Chaos, 30(3), 033119. https://doi.org/10.1063/1.5120818

    Article  Google Scholar 

  • Hu J, “Brief Analysis on the Application of Big Data in Internet Financial Risk Control.” 2018.

  • Huang, X., Li, X., Yu, Y., et al. (2019). Integration of bricolage and institutional entrepreneurship for internet finance: Alibaba’s Yu’e Bao. Journal of Global Information Management, 27(2), 1–23. https://doi.org/10.4018/JGIM.2019040101

    Article  Google Scholar 

  • Imakura, A., Inoue, Y., Sakurai, T., et al. (2018). Parallel implementation of the nonlinear semi-NMF based alternating optimization method for deep neural networks. Neural Processing Letters, 47(3), 815–827.

    Article  Google Scholar 

  • Jiang S, (2016) Big data technology application and development trend. VE 33, 166–168

  • Kang, Q. (2019). Financial risk assessment model based on big data. International Journal of Modeling SIAM Journal of Science Computer, 10(04), 106–113. https://doi.org/10.1142/S179396231950020X

    Article  Google Scholar 

  • Kwon, H., Do, T. N., & Kim, J. (2020). Comprehensive decision framework combining price prediction and production-planning models for strategic operation of a petrochemical industry. Industrial and Engineering Chemistry Research, 59(25), 11610–11620.

    Article  Google Scholar 

  • Li, Y., & Zhang, C. Y. (2019). Research on internet credit interest rate risk under asymmetric information. Journal of Baoji University of Arts and Sciences, 39(1), 16–20.

    Google Scholar 

  • Lv, Z., Kong, W., Zhang, X., et al. (2019a). Intelligent security planning for regional distributed energy internet. IEEE Transactions on Industrial Informatics, 99, 3540–3547. https://doi.org/10.1109/TII.2019.2914339

    Article  Google Scholar 

  • Lv, Z., Li, X., Lv, H., et al. (2019b). BIM big data storage in WebVRGIS. IEEE Transactions on Industrial Informatics, 99, 1–1. https://doi.org/10.1109/TII.2019.2916689

    Article  Google Scholar 

  • Masmoudi, K., Abid, L., & Masmoudi, A. (2019). Credit risk modeling using Bayesian Network with a latent variable. Expert Systems with Applications, 127, 157–166. https://doi.org/10.1016/j.eswa.2019.03.014

    Article  Google Scholar 

  • Mohammadi, S., & Nazemi, A. (2020). On portfolio management with value at risk and uncertain returns via an artificial neural network scheme. Cognitive Systems Research, 59, 247–263. https://doi.org/10.1016/j.cogsys.2019.09.024

    Article  Google Scholar 

  • Sharma, D., Prinja, S., Aggarwal, A. K., et al. (2017). Out-of-pocket expenditure for hospitalization in Haryana State of India: Extent, determinants & financial risk protection. Indian Journal of Medical Research, 146(6), 759. https://doi.org/10.4103/ijmr.IJMR_2003_15

    Article  Google Scholar 

  • Soler-Dominguez, A., Juan, A. A., & Kizys, R. (2017). A survey on financial applications of metaheuristics. ACM Computing Surveys, 50(1), 1–23. https://doi.org/10.1145/3054133

    Article  Google Scholar 

  • Stehlík, M., Helperstorfer, C., Hermann, P., et al. (2017). Financial and risk modelling with semicontinuous covariances. Inform Sciences, 394–395, 246–272.

    Article  Google Scholar 

  • Tao, N. R. (2019). Analysis of Credit Risk of Internet Credit. Time Finance, 15, 145–149.

    Google Scholar 

  • Tu, C. S., Chang, C. H., Chang, S. C., et al. (2018). A decision for predicting successful extubation of patients in intensive care unit. BioMed Research International, 2018, 1–11. https://doi.org/10.1155/2018/6820975

    Article  Google Scholar 

  • Wang, L. (2016). On the construction of Internet financial risk warning system under the background of big data. Economic and Trade Practice, 004, 38–40.

    Google Scholar 

  • Wang, F. (2018). Research on application of big data in internet financial credit investigation based on improved GA-BP Neural Network. Complexity, 1, 1–16. https://doi.org/10.1155/2018/7616537

    Article  Google Scholar 

  • Wu, L., Yang, Y., Maheshwari, M., et al. (2019). Parameter optimization for FPSO design using an improved FOA and IFOA-BP neural network. Ocean Engineering, 175(1), 50–61. https://doi.org/10.1016/j.oceaneng.2019.02.018

    Article  Google Scholar 

  • Xu, D., Tang, S. A., & Dan, G. (2019). China’s campaign-style Internet finance governance: Causes, effects, and lessons learned for new information-based approaches to governance. Computer Law and Security Review, 35(1), 3–14. https://doi.org/10.1016/j.clsr.2018.11.002

    Article  Google Scholar 

  • Yang, L. (2018). Research on the present situation and countermeasures of credit risk management of rural small and medium financial institutions. Financial and Economic, 31, 210.

    Google Scholar 

  • Zabala, C. A., & Josse, J. M. (2018). Shadow credit in the middle market: The decade after the financial collapse. The Journal of Risk Finance, 19, 120–123. https://doi.org/10.1108/JRF-02-2017-0033

    Article  Google Scholar 

  • Zhang, Y. (2018). Research on credit risk prevention of large and medium-sized enterprises in commercial banks. Market Modernization, 20, 122.

    Google Scholar 

  • Zhang, Y. (2019a). Application of improved BP neural network based on e-commerce supply chain network data in the forecast of aquatic product export volume. Cognitive System Research, 57, 228–235. https://doi.org/10.1016/j.cogsys.2018.10.025

    Article  Google Scholar 

  • Zhang, Y. (2019b). Research on the risk management of internet credit business of commercial banks. Int. Rev. Econ. Finance, 20, 24–25.

    Google Scholar 

  • Zhang, S., Mao, D., & Wang, B. (2016). Application of big data processing technology in fault diagnosis and early warning of wind turbine gearbox. Automation of Electric Power Systems, 40(14), 129–145.

    Google Scholar 

  • Zhang Q, Y. Luo, S. Fu, et al. (2015) Research on internet-finance risk warning based on big data. In: International Conference on Social Science, Education Management and Sports Education. Atlantis Press. Doi: https://doi.org/10.2991/ssemse-15.2015.566.

  • Zhao, Z. (2017). Neural network algorithm based on combined classifier. Electronic Technology, 12, 43–46.

    Google Scholar 

  • Zhu, X. (2018). Research on credit risk management of china’s commercial banks under the background of interest rate marketization. Financial Community, 9, 9.

    Google Scholar 

Download references

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  1. College of Economics and Management, Nan**g University of Aeronautics and Astronautics, Nan**g, 211106, China

    Min Lin

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Lin, M. Innovative Risk Early Warning Model under Data Mining Approach in Risk Assessment of Internet Credit Finance. Comput Econ 59, 1443–1464 (2022). https://doi.org/10.1007/s10614-021-10180-z

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