AI-Driven Predictive Analytics for Risk Management in Financial Markets
Keywords:
Artificial Intelligence, Predictive Analytics, Financial Markets, Risk Management, Machine Learning, Data Analysis, Real-Time MonitoringAbstract
This paper examines how AI-driven predictive analytics transforms financial market risk management by improving prediction accuracy, real-time monitoring, and decision-making. We identify essential AI deployment strategies and assess their advantages and drawbacks in financial institutions by reviewing secondary data, including academic literature and industry reports. The results show that AI analyzes complicated information and finds patterns that conventional approaches miss, improving risk evaluations. Real-time monitoring helps firms react quickly to emerging threats, improving operational resilience. The report also reveals algorithmic bias and model interpretability issues that might damage stakeholder confidence. Thus, policy implications imply regulators should promote openness and fairness in AI applications and encourage financial institutions to use explainable AI. This paper emphasizes the need for ethical AI to maximize predictive analytics advantages while addressing dangers by promoting cooperation among regulators, industry stakeholders, and technology developers. Financial institutions must use AI-driven methods to manage current markets and develop sustainably.
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