Big Data in Financial Industry Risk Management: Applications and Challenges

DOI

10.2991/978-94-6463-548-5_29How to use a DOI?

Keywords

Big Data; Financial Industry; Risk Management

Abstract

This paper explores the applications and challenges of big data in financial industry risk management. As technology advances, big data has become a crucial tool in the financial sector, especially in risk management. Big data technologies provide financial institutions with more comprehensive and accurate risk assessment and monitoring tools by integrating data from various sources. The paper first outlines the definition, characteristics, and application framework of big data in the financial industry, followed by an in-depth analysis of its specific applications in credit risk, market risk, operational risk, fraud detection, anti-money laundering, and compliance risk management. Through machine learning and real-time monitoring, big data helps financial institutions identify and prevent potential credit defaults in credit risk management. In market risk management, big data aids financial institutions in accurately predicting market fluctuations and implementing appropriate hedging strategies through real-time market data analysis. Additionally, the paper discusses big data’s role in anomaly detection and process optimization in operational risk management, as well as in transaction monitoring and behavioral analysis for fraud detection and anti-money laundering. However, the application of big data also faces several challenges, including data quality and accuracy, data privacy and security, technology and talent shortages, compliance and legal issues, and difficulties in data integration across systems. These challenges demand higher standards for financial institutions to build effective risk management systems. To address these challenges, financial institutions need to enhance data governance, drive technological innovation, improve big data talent development, and strengthen data privacy and security protection. Finally, the paper concludes that despite these challenges, big data holds great potential in financial risk management, and further development should focus on cross-industry collaboration and innovation to fully leverage its advantages, ensuring the stability and sustainable development of the financial sector.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zishan Liu
PY  - 2024
DA  - 2024/10/21
TI  - Big Data in Financial Industry Risk Management: Applications and Challenges
BT  - Proceedings of the 2024 3rd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2024)
PB  - Atlantis Press
SP  - 266
EP  - 274
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-548-5_29
DO  - 10.2991/978-94-6463-548-5_29
ID  - Liu2024
ER  -