Author(s)

Qiyu Liang ¹

Affiliation(s)

¹ New York University, New York, United States

Corresponding Author

Qiyu Liang

ABSTRACT

With the macro-economy entering the new normal, many new problems have been exposed in all walks of life, and credit risks in the financial field are also being exposed at an accelerated pace. In China, every year, on average, there are more than ten trillion yuan of new financing and hundreds of trillions of yuan of stock loans issued after maturity, at present, the financial industry will bring great pressure on the subsequent credit hazard management, prevention and control of large-scale credit expiration hazards. he rapid development of modern information technology has had a profound impact on various industries, and in this context, big data technology has become increasingly widely used in the financial industry, playing an important role in financial risk prediction and prevention. The study explored financial risk prediction methods based on big data technology from aspects such as credit risk analysis, and then explored financial risk prevention strategies supported by big data technology from aspects such as risk prevention awareness.

KEYWORDS

Financial risks; Big data; Prediction; Keep away

CITE THIS PAPER

Qiyu Liang, Financial risk prediction and prevention based on big data technology. Financial Engineering and Risk Management (2023) Vol. 6: 28-32. DOI: http://dx.doi.org/10.23977/ferm.2023.061104.

REFERENCES

[1] Chen Zhenlong, Jin Shang. Bilateral Burr distribution and its application in financial market risk prediction and optimization [J]. Systems Science and Mathematics, 2023,43 (2): 505-530
[2] Luo Peng, Chen Yiguo, Xu Chuanhua. Baidu Search, Risk Perception, and Financial Risk Prediction - Based on the Perspective of Behavioral Finance [J]. Financial Forum, 2018,23 (1): 39-51
[3] Li Yanwen, Jiang Wenhua, Wang Chunjie. Can online big data credit risk scoring effectively predict credit default risk? [J]. Economic Issues, 2021 (7): 70-77
[4] Liu Min, Ren Zhongyuan, Zhou Decai, et al. Research on Internet Financial Risk Measurement and Prediction Based on Mixed Frequency Data [J]. Journal of Management, 2022, 19 (12): 1847-1854
[5] Zhou Lei, Qiu Xun, Zhu Yi, etc. Empirical Study on Credit Risk Assessment of Supply Chain Finance Based on Big Data - Taking the Whole Vehicle Manufacturing Industry as an Example [J]. Financial Development Research, 2022 (5): 64-70
[6] Dang Zhang, Miao Ziqing, Zhang Tao, etc. Progress in the application of big data methods in systematic financial risk monitoring and warning [J]. Financial Development Research, 2022 (2): 3-12
[7] Yang Kaisen, Fu Fei. Anaconda A Financial Risk Early Warning System Based on Big Data [J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 2021,44 (1): 135-142
[8] Zhou Lei, Lai Shumou, Fu Yuqi. Research on Credit Risk Assessment and Application of Big Data in Supply Chain Finance [J]. Financial Education Research, 2023,36 (1): 64-73
[9] Xing Sheng, Wang Shanshan. Research on Financial Risk Prediction and Prevention Measures Based on Big Data [J]. Technological Innovation and Productivity, 2018 (3): 1-4
[10] Cao Min, Qiu Gelei, Lin Xing. Research on Predicting Big Data Financial Risk Behavior Based on Deep Learning [J]. Journal of Xinyang Agricultural and Forestry University, 2021, 31 (3): 119-122, 127

Sponsors, Associates, and Links

---

• Information Systems and Economics
  
  
• Accounting, Auditing and Finance
  
  
• Industrial Engineering and Innovation Management
  
  
• Tourism Management and Technology Economy
  
  
• Journal of Computational and Financial Econometrics
  
  
• Accounting and Corporate Management
  
  
• Social Security and Administration Management
  
  
• Population, Resources & Environmental Economics
  
  
• Statistics & Quantitative Economics
  
  
• Agricultural & Forestry Economics and Management
  
  
• Social Medicine and Health Management
  
  
• Land Resource Management
  
  
• Information, Library and Archival Science
  
  
• Journal of Human Resource Development
  
  
• Manufacturing and Service Operations Management
  
  
• Operational Research and Cybernetics