Research Article
Open access
Published on 29 November 2024
Download pdf
Shen,Z. (2024).Financial Risk Management Based on Big Data Technology.Advances in Economics, Management and Political Sciences,124,101-107.
Export citation

Financial Risk Management Based on Big Data Technology

Zhihao Shen *,1,
  • 1 Shanxi University of Finance & Economics, WuCheng Street, TaiYuan, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2754-1169/2024.17735

Abstract

In the past decade, the financial industry has shown a trend of rapid development, with traditional financial institution models gradually being replaced by a coexistence of traditional and various emerging financial forms. As the operating models in the financial industry undergo corresponding changes, the types and quantity of risks that may exist within the industry continue to increase. The complexity of financial risks is gradually becoming more pronounced, with factors such as changes in laws and regulations, lack of credit assessment, and data loss or leaks in internet financial trading platforms potentially leading directly to financial risks. Employing traditional work models and methods for financial risk management often makes it difficult to achieve the desired risk control outcomes. Currently, big data technology has been widely utilized in the insurance, banking, and other sectors of the financial industry. Major financial institutions continuously innovate the traditional methods of analyzing and processing data information by leveraging big data, transforming the way financial data exists in the industry. Only by actively adapting to the trends of the times and flexibly applying big data technology to practices such as credit assessment and risk alerting can financial risk management innovation be comprehensively promoted, resulting in effective control of complex financial risks. Based on this, this paper analyzes the changes and characteristics of financial risk management in the era of big data, discusses the important role and specific applications of big data technology in the field of financial risk management and proposes relevant strategies for enhancing financial risk management in the big data environment, aiming to advance the progress of financial risk management in the era of big data.

Keywords

Big Data Technology, Financial Risk Management, Risk Assessment, Risk Prevention, Technology Application

[1]. Cerchiello, P., & Giudici, P. (2016). Big Data Analysis for Financial Risk Management. Journal of Big Data, 3(1). https://doi.org/10.1186/s40537-016-0053-4

[2]. Zhou, H., Sun, G., Fu, S., Liu, J., Zhou, X., & Zhou, J. (2019). A big data mining approach of PSO-based BP Neural Network for financial risk management with IOT. IEEE Access, 7. https://doi.org/10.1109/access.2019.2948949

[3]. Zhang, M. (2020). Research on the application of financial and Taxation Big Data in Enterprise Taxation Risk Management. E3S Web of Conferences, 218. https://doi.org/10.1051/e3sconf/202021801053

[4]. Wei, R., & Yao, S. (2021). Enterprise financial risk identification and information security management and control in Big Data Environment. Mobile Information Systems, 2021, 1–6. https://doi.org/10.1155/2021/7188327

[5]. Yue, H., Liao, H., Li, D., & Chen, L. (2021). Enterprise financial risk management using information fusion technology and Big Data Mining. Wireless Communications and Mobile Computing, 2021, 1–13. https://doi.org/10.1155/2021/3835652

[6]. Jung, K., Kim, D., & Yu, S. (2022). Next generation models for portfolio risk management: An approach using financial big data. Journal of Risk and Insurance, 89(3), 765–787. https://doi.org/10.1111/jori.12374

[7]. Liang, Q. (2023). Financial risk prediction and prevention based on Big Data Technology. Financial Engineering and Risk Management, 6(11). https://doi.org/10.23977/ferm.2023.061104

[8]. Du, Q. (2023). Financial risk prediction model in the context of big data - corporate financial risk control based on LSTM Deep Neural Networks. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns.2023.2.01422

Cite this article

Shen,Z. (2024).Financial Risk Management Based on Big Data Technology.Advances in Economics, Management and Political Sciences,124,101-107.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content

About volume

Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis

Conference website: https://2024.icftba.org/
ISBN:978-1-83558-699-0(Print) / 978-1-83558-700-3(Online)
Conference date: 4 December 2024
Editor:Ursula Faura-Martínez, Javier Cifuentes-Faura
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.124
ISSN:2754-1169(Print) / 2754-1177(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).