From Prediction to Action: AI-Driven Prescriptive Analytics for Business and Financial Decision-Making
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Beschreibung
This book begins by laying the methodological foundations of prescriptive analytics, from core machine learning and optimization techniques to the practical workflows of data collection, feature engineering, model validation, and integrating AI with optimization methods. In today's complex and competitive landscape, accurate predictions are no longer enough. Organizations need the capability to turn insight into optimized, actionable decisions. From Prediction to Action addresses this critical gap, offering readers both the theoretical grounding and the practical tools required to build, scale, and govern intelligent decision-making systems under conditions of uncertainty, risk, and regulatory scrutiny.
It then turns to the operational realities of deploying these systems at scale: designing robust architectures, implementing solutions with Python, Power BI, and Tableau, deploying on cloud platforms such as AWS and Azure, and embedding AI seamlessly into organizational workflows.
Beyond the technical, this book examines the broader forces shaping AI-driven decision-making today fiscal policy, financial inclusion, and economic growth across Central Asia, the GCC, and Southeast Asia; sustainability and governance through ESG performance, sovereign wealth funds, and circular economy practices; and the educational, legal, and institutional frameworks that must evolve alongside the technology itself, from academic integrity in the age of generative AI to the regulation of AI systems and the rise of public-private partnerships.
Bringing together systematic literature reviews, bibliometric analyses, and conceptual frameworks alongside applied case studies, from Prediction to Action offers a genuinely holistic view one that connects economics, finance, sustainability, and law to reflect the true complexity of contemporary business challenges.
A must-read for researchers, practitioners, and decision-makers seeking to move beyond prediction and into the actionable, ethical, and sustainable application of AI-powered analytics.
This book begins by laying the methodological foundations of prescriptive analytics, from core machine learning and optimization techniques to the practical workflows of data collection, feature engineering, model validation, and integrating AI with optimization methods. In today's complex and competitive landscape, accurate predictions are no longer enough. Organizations need the capability to turn insight into optimized, actionable decisions. From Prediction to Action addresses this critical gap, offering readers both the theoretical grounding and the practical tools required to build, scale, and govern intelligent decision-making systems under conditions of uncertainty, risk, and regulatory scrutiny.
It then turns to the operational realities of deploying these systems at scale: designing robust architectures, implementing solutions with Python, Power BI, and Tableau, deploying on cloud platforms such as AWS and Azure, and embedding AI seamlessly into organizational workflows.
Beyond the technical, this book examines the broader forces shaping AI-driven decision-making today fiscal policy, financial inclusion, and economic growth across Central Asia, the GCC, and Southeast Asia; sustainability and governance through ESG performance, sovereign wealth funds, and circular economy practices; and the educational, legal, and institutional frameworks that must evolve alongside the technology itself, from academic integrity in the age of generative AI to the regulation of AI systems and the rise of public-private partnerships.
Bringing together systematic literature reviews, bibliometric analyses, and conceptual frameworks alongside applied case studies, from Prediction to Action offers a genuinely holistic view one that connects economics, finance, sustainability, and law to reflect the true complexity of contemporary business challenges.
A must-read for researchers, practitioners, and decision-makers seeking to move beyond prediction and into the actionable, ethical, and sustainable application of AI-powered analytics.
Provides a comprehensive approach to building intelligent decision support systems Focuses on high-impact financial applications such as credit risk modeling, fraud detection and algorithmic trading Integrates machine learning with optimization techniques
Autor*in
Wael Abdallah
Themen in »From Prediction to Action: AI-Driven Prescriptive Analytics for Business and Financial Decision-Making«
Prescriptive analytics Business analytics Financial analytics Decision-making systems Intelligent decision support systems Machine learning Deep learning Optimization techniques Data-driven decision-making Predictive analytics Big data analytics Data mining Feature engineering Model evaluation Real-time analytics