Dr. Oumaima Abouzaid is a Doctor in Economics and Management Sciences with expertise in finance, economic analysis, and emerging technologies. He holds a PhD from Chouaib Doukkali University, with research focused on foreign exchange markets, blockchain, cryptocurrencies, and digital finance. He also holds a Master’s degree in Finance and Actuarial Science from the National School of Business and Management (ENCG) Casablanca. Currently serving as a Procurement & Contract Manager at OCP Group, Dr. Oumaima Abouzaid has extensive professional experience in procurement, financial analysis, contract management, and cost optimization. He has also contributed to academic activities as an adjunct lecturer, scientific reviewer for international journals, conference program committee member, and academic mentor. Passionate about teaching, research, and academic innovation, he is committed to advancing knowledge and supporting the development of students and researchers.

AI-Driven Financial Analytics: Machine Learning, Market Volatility and Systemic Risk                                                                                                                                                                                             Abstract: The increasing availability of high-frequency and alternative financial data has created new opportunities for Artificial Intelligence (AI) and machine learning applications in financial analytics. This presentation explores how AI-driven approaches can improve the measurement and prediction of financial market volatility and systemic risk. By exploiting complex dependencies and nonlinear patterns that may remain undetected by conventional econometric models, machine learning techniques provide powerful tools for understanding financial interconnectedness and anticipating periods of market stress. The presentation reviews the main applications of AI in financial risk forecasting, including volatility prediction, contagion analysis, risk connectedness, and early-warning systems. It also examines the challenges related to data quality, model transparency, overfitting, and the interpretability of AI-based financial models. The presentation concludes by discussing how the integration of economic theory, econometric methods, and AI can contribute to more robust financial decision-making and greater financial resilience.