Abstract
Purpose of the research - To examine modern methods of investment portfolio optimization with an emphasis on risk and volatility management, and to explore the integration of financial monitoring tools in ensuring regulatory compliance and operational resilience. Methodology - The study is based on the modeling and analysis of a diversified hypothetical investment portfolio containing stocks, bonds, and ETFs. It combines classical optimization theories—such as the Markowitz model and Sharpe ratio—with advanced tools including Value-at-Risk (VaR), Conditional Valueat-Risk (CVaR), stress testing, and machine learning algorithms for volatility forecasting and asset allocation. Originality/value - This research provides a synthesis of traditional financial models and modern datadriven techniques. A notable contribution is the applied use of financial monitoring systems—used by secondtier banks—to assess portfolio stability and regulatory risk under the frameworks of AML/CFT and Basel III. Findings - The results show that implementing innovative risk management and optimization strategies significantly enhances portfolio performance and resilience. Empirical analysis demonstrates that financial monitoring, when combined with CVaR-based modeling and stress scenarios, contributes to better decisionmaking, reduced exposure to extreme losses, and improved compliance.