2024, Vol. 7, Issue 2

Modern financial markets are becoming increasingly complex because of volatile conditions, high-frequency trading, and data overload in portfolio risk management. Conventional approaches, including Value at risk (VaR) and mean-variance optimization, tend to fail to observe non-linear relationship and dynamic interactions between assets. An alternative approach that promises to be accurate in the problem of predicting risk better is machine learning (ML) that uses complex patterns. Nevertheless, the fact that many ML models are opaque can be a serious problem: stakeholders are not always able to know why predictions are made, which concerns their trust, accountability, and compliance with regulations. In this paper, the researcher explores how explainable machine learning (XML) such as SHAP, LIME, and attention-based neural networks can be used to manage portfolio risks. It compares the predictive power, interpretability and practical value of the XML to the standards of traditional ML and statistical models on multi-asset financial data. The results reveal the explanatory usefulness of models in terms of delivering actionable information on risk drivers and still being competitive in the predictive ability, allowing portfolio managers to make more informed decisions and align better with the regulatory needs. This study helps bridge the research gap in confirming the theoretical knowledge as well as practical implementation of explainable ML in financial risk management by closing the gap between model performance and transparency.