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ISSN

2661-4014(Online)

Article Processing Charges (APCs)

US$800

Publication Frequency

Quarterly

PDF

Published

2024-10-14

Issue

Vol 6 No 3 (2024): Published

Section

Articles

Application and Challenges of Data Science in Financial Risk Management

Shenghua Huang

University of California


DOI: https://doi.org/10.59429/bam.v6i3.7356


Keywords: data science; financial risk management; big data processing; advanced analytical models


Abstract

This paper delves into the extensive application of data science in financial risk management and the challenges it faces. Data science plays an increasingly crucial role in the financial sector due to its powerful capabilities in big data processing, advanced analytical models, and real-time dynamics. The ability to handle massive datasets through advanced storage technologies and distributed computing frameworks provides a solid foundation for financial institutions by enabling rapid data loading, storage, cleansing, integration, and preprocessing. Advanced analytical models leveraging machine learning, deep learning, and other cuttingedge technologies automatically extract valuable insights and patterns from data, significantly enhancing the accuracy and efficiency of risk management. The real-time and dynamic nature of data science facilitates realtime monitoring and dynamic adjustments in risk management through techniques such as real-time data stream analysis and online learning. However, the application of data science in financial risk management also faces challenges such as data security, privacy protection, and model interpretability.


References

[1] Liu Shenglin. The Application of Data Science in the Field of Economics and Finance [J]. Time Figures, 2023(27):0115-0118.

[2] Wang Kai, Wang Na. Research on Data Management, Operation, and Information Security in Big Data Social Governance [J]. Computer Knowledge and Technology: Academic Exchange, 2022(018-001).

[3] Chen Yudong. Analysis of the Application Value of Data Science in the New Financial Era [J]. Business Story, 2022(18):49-51.

[4] Tian Fangyu, Liu Dayu, Li Tianye. Identification of Driving Mechanisms for the Transformation of China’s Economic Growth Rate: Based on the Triple Perspectives of External Uncertainty, Financial Risk, and Core Inflation [J]. Journal of Shanghai University of Finance and Economics (Philosophy and Social Sciences Edition), 2023, 25(6):28-43.

[5] Dang Cong, Lu Junxiang. Research on Financial Market Risk Spillover Based on High-frequency Data [J]. Journal of Ningxia University (Natural Science Edition), 2022, 43(3):254-260.



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