Driving Global Economic Transformation Through AI and Machine Learning

In an era defined by economic turbulence, global pandemics, climate shocks, geopolitical instability, and recurring financial crises, the world economy stands at a critical turning point. Traditional recovery strategies centered on fiscal policy adjustments, stimulus measures, and market interventions are no longer sufficient on their own to address the scale, speed, and complexity of contemporary disruptions. As these challenges intensify, there is a growing need to rethink how economic resilience and recovery are conceptualized and implemented. This underscores the importance of interdisciplinary research that critically examines the rapid transformations shaping the global macroeconomic landscape. Driving Global Economic Transformation Through AI and Machine Learning examines the transformative potential of intelligent systems and their capacity to predict risks, model crisis scenarios, manage disruptions, and optimize responses across industries and governments. From forecasting financial downturns to addressing supply chain vulnerabilities, artificial intelligence and machine learning are positioned as critical enablers of economic resilience, stability, and innovation. Covering topics such as complex dynamics in financial markets, stock price prediction, and time series forecasting, this book is an excellent academic resource for graduate and doctoral students, economists, data scientists, international development professionals, technology developers, policymakers, and more.