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Integrating Advanced Technologies in Financial Risk Management: A Comprehensive Analysis
- 1 University of Melbourne, Melbourne, Australia
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Abstract
This paper delves into the pivotal role of advanced technologies in enhancing financial risk management across various domains, including credit risk, market risk, operational risk, and liquidity risk. It meticulously explores the application of machine learning (ML) algorithms and artificial intelligence (AI) in developing sophisticated risk assessment models, portfolio diversification strategies, and regulatory compliance mechanisms, which collectively surpass traditional methodologies in accuracy, efficiency, and predictive power. Through a detailed examination of enhanced Value at Risk (VaR) models, dynamic hedging strategies, and the impact of geopolitical events on market risk, alongside innovative approaches to operational risk mitigation and liquidity planning, this study underscores the transformative potential of technological advancements in financial risk management. It highlights how these technologies facilitate real-time analysis, predictive modeling, and strategic planning, significantly contributing to the resilience and stability of financial institutions in the face of evolving risks and regulatory requirements.
Keywords
Financial Risk Management, Machine Learning, Artificial Intelligence, Credit Risk, Market Risk
[1]. Curti, Filippo, et al. "Cyber risk definition and classification for financial risk management." Journal of Operational Risk 18.2 (2023).
[2]. Dwivedi, Dwijendra Nath, Ghanashyama Mahanty, and Yogesh Kumar Pathak. "AI Applications for Financial Risk Management." The Impact of AI Innovation on Financial Sectors in the Era of Industry 5.0. IGI Global, 2023. 17-31.
[3]. Mubanani, Edmond Mukhongo, and Purity Njeri Fadhil. "Application of the New Growth Theory to Financial Risk Management." East African Finance Journal 2.1 (2023): 32-39.
[4]. Wahyuni, Sandiani Sri, et al. "Mapping Research Topics on Risk Management in Sharia and Conventional Financial Institutions: VOSviewer Bibliometric Study and Literature Review." (2023).
[5]. Nugrahanti, Trinandari Prasetyo. "Analyzing the evolution of auditing and financial insurance: tracking developments, identifying research frontiers, and charting the future of accountability and risk management." West Science Accounting and Finance 1.02 (2023): 59-68.
[6]. Sharifani, Koosha, and Mahyar Amini. "Machine learning and deep learning: A review of methods and applications." World Information Technology and Engineering Journal 10.07 (2023): 3897-3904.
[7]. Kelly, Bryan, and Dacheng Xiu. "Financial machine learning." Foundations and Trends® in Finance 13.3-4 (2023): 205-363.
[8]. Sultonov, Sarvar. "IMPORTANCE OF PYTHON PROGRAMMING LANGUAGE IN MACHINE LEARNING." International Bulletin of Engineering and Technology 3.9 (2023): 28-30.
[9]. Yao, Zhenpeng, et al. "Machine learning for a sustainable energy future." Nature Reviews Materials 8.3 (2023): 202-215.
[10]. Bharadiya, Jasmin Praful, Reji Kurien Thomas, and Farhan Ahmed. "Rise of Artificial Intelligence in Business and Industry." Journal of Engineering Research and Reports 25.3 (2023): 85-103.
[11]. Ahmad, Hanandeh, et al. "The effects of big data, artificial intelligence, and business intelligence on e-learning and business performance: Evidence from Jordanian telecommunication firms." International Journal of Data and Network Science 7.1 (2023): 35-40.
[12]. Rane, Nitin. "Role and challenges of ChatGPT and similar generative artificial intelligence in business management." Available at SSRN 4603227 (2023).
[13]. Yathiraju, Nikitha, et al. "Research and Innovation to Market Development: Artificial Intelligence in Business." 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). IEEE, 2023.
Cite this article
Zhao,Y. (2024). Integrating Advanced Technologies in Financial Risk Management: A Comprehensive Analysis. Advances in Economics, Management and Political Sciences,108,92-97.
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Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis
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