Data-Driven Modelling and Predictive Analytics in Business and Finance

Alex Khang

ISBN 10: 1032600624 / ISBN 13: 9781032600628

Published by Taylor and Francis Ltd, GB, 2024

About this Item

Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers:Data-driven modellingPredictive analyticsData analytics and visualization toolsAI-aided applicationsCybersecurity techniquesCloud computingIoT-enabled systems for developing smart financial systemsThis book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.

Seller Inventory # LU-9781032600628

Bibliographic Details

• Title: Data-Driven Modelling and Predictive Analytics in Business and Finance
• Publisher: Taylor and Francis Ltd, GB
• Publication Date: 2024
• Language: English
• ISBN 10: 1032600624
• ISBN 13: 9781032600628
• Binding: Paperback
• Condition: New
• Weight: 820 grams

About this title

Synopsis

Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent.

Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers:

• Data-driven modelling
• Predictive analytics
• Data analytics and visualization tools
• AI-aided applications
• Cybersecurity techniques
• Cloud computing
• IoT-enabled systems for developing smart financial systems

This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.

About the Author

Alex Khang is a Professor of Information Technology, D.Sc. D.Litt., and a AI and Data scientist, AI and Data Science Research Center, Global Research Institute of Technology and Engineering, North Carolina, United States.

Rashmi Gujrati is a Professor, Campus Director, and Dean of International Affairs at Kamal Gandhi Memorial Ayurvedic College, Nawanshahr, India.

Hayri Uygun holds a Ph.D. from Recep Tayyip Erdogan University, Institute of Social Sciences, Business Administration, Rize, Turkey.

R. K. Tailor is an expert in robotic process automation and robotic accounting and a Senior Associate Professor at the Department of Business Administration, Manipal University, Manipal, India.

Sanjaya Singh Gaur is a Clinical Professor of Marketing at the NYU School of Professional Studies in New York University, New York, United States.