AI-Powered Financial Engineering: Optimizing Risk Management and Investment Strategies
Keywords:
AI-Powered Financial Engineering, Risk Management, Artificial Intelligence, Algorithmic Trading, Portfolio Optimization, Quantitative FinanceAbstract
This research examines how AI optimizes financial engineering risk management and investing methods. The main goals are assessing how AI improves forecast accuracy, asset selection, and portfolio optimization and identifying its implementation issues and policy consequences. The secondary data review synthesizes research and case studies to evaluate AI's performance in various areas. AI increases risk assessment and investment decision-making with sophisticated machine learning approaches, which provide deeper insights and flexibility than conventional models. Model interpretability, data quality, and regulatory compliance remain issues. The paper recommends explainable AI models to solve transparency challenges and rules that balance innovation, data protection, and ethics. These findings enable financial institutions and regulators to use AI's promise and navigate its difficulties to create a more resilient and adaptable financial system.
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