Analyzing the Impact of Machine Learning on Systemic Risk Identification in Global Financial Markets
Keywords:
Machine Learning, Systemic Risk, Global Financial Markets, Predictive Analytics, Financial Crises, Algorithmic TransparencyAbstract
The advent of machine learning (ML) has revolutionized systemic risk identification in global financial markets by enhancing predictive capabilities and reducing the probability of financial crises. This paper explores the application of ML techniques in identifying and managing systemic risk, providing a detailed review of pre-2022 literature. Employing quantitative analyses and case studies, this study demonstrates the strengths and limitations of ML in capturing risk patterns. It further evaluates ML’s role in complementing traditional financial models, emphasizing areas such as sentiment analysis, credit risk evaluation, and market volatility prediction. While ML offers promising advancements, challenges persist in terms of algorithmic transparency and ethical considerations. The findings contribute to ongoing discussions on optimizing ML-driven financial risk management practices.
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Copyright (c) 2023 Salumanda Christian K (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
