Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network

Zixian Liu (), Guansan Du (), Shuai Zhou (), Haifeng Lu () and Han Ji ()
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Zixian Liu: Liaoning University
Guansan Du: Liaoning University
Shuai Zhou: Liaoning University
Haifeng Lu: Shenyang Branch of People’s Bank of China
Han Ji: Shenyang Branch of People’s Bank of China

Computational Economics, 2022, vol. 59, issue 4, No 10, 1499 pages

Abstract: Abstract The study aims to analyze and forecast Internet financial risks based on the model based on deep learning and the Back Propagation Neural Network (BPNN). First, the theory of Internet financial risks is introduced and a theoretical framework for analyzing and forecasting internet financial risks is established. Second, the theory of the BPNN and the algorithms based on deep learning are introduced. Then, the model based on the BPNN and deep learning is implemented to improve the early warning of Internet financial risks, analyze the data image of China's Gross Domestic Product (GDP), currency (M2), non-performing loan records, and the Shanghai Composite Index from 2006 to 2020, and forecast the risks in 2021. Through the model based on deep learning and BPNN, it can be found that the trends of the growth rate of China's GDP take on the shapes of V and L, and the trend of M2 is opposite to that of GDP. In the whole year, there is a low at the beginning and the end of the year, and the monthly non-performing loans and the Shanghai Composite Index decrease. The forecast made by the model is that there will be many fluctuations in 2021. At present, China’s economy just enters the era of the new normal, which helps to build a more scientific and sensitive early warning system for financial risks. The model based on the BPNN and deep learning greatly improves the timeliness of forecasts and has a positive impact on the stability of China’s financial environment.

Keywords: BP neural network; Deep learning; GDP growth rate; Financial risks; Early warning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10614-021-10229-z

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