Editors: Sonal Trivedi, Balamurugan Balusamy, Prasad Begde

Computational Modelling Approaches to FinTech Innovation

eBook: US $79 Special Offer (PDF + Printed Copy): US $135
Printed Copy: US $95
Library License: US $316
ISBN: 979-8-89881-082-5 (Print)
ISBN: 979-8-89881-081-8 (Online)
Year of Publication: 2025
DOI: 10.2174/97988988108181250101

Introduction

Computational Modelling Approaches to FinTech Innovation bridges the gap between technological advancement and financial systems by investigating the synergy between human intelligence and machine-driven models to drive innovation, resilience, and sustainability in finance.

The book opens by grounding readers in the principles of Industry 5.0 and its implications for human-machine collaboration and green innovation. It then transitions into the evolution of management practices and enterprise digitalization, highlighting the journey from Industry 1.0 through to the current digital transformation. Later chapters explore the disruptive effects of Industry 5.0 on supply chains, production, and marketing strategies, before addressing the shifts in human resource management and the convergence of finance and technology. The final section offers insight into emerging technologies, including AI, IoT, Big Data, and Machine Learning, mapping their trajectory within modern industry frameworks.

Key Features:

  • - Examine computational models driving FinTech transformation
  • - Explore Industry 5.0’s role in sustainable innovation and human-machine integration
  • - Analyze digital enterprise evolution and its management implications
  • - Investigate the effects of advanced technologies on supply chains and marketing
  • - Assess the fusion of finance and technology within organizational structures
  • - Uncover future trends through real-world applications of AI, IoT, and Big Data


Readership:

Students, researchers and fintech professionals.

Foreword

The Book Titled “Computational Modelling Approaches to FinTech Innovation” is for AI experts, fintech visionaries, entrepreneurs, researchers, and policymakers, which provides unique insights into the field of finance exploring the impact of AI and deep learning on the financial sector. Additionally, the book provides the consideration and impact of technologies on the lives of all stakeholders including employees, customers, government, and policymakers.

This book would be beneficial for researchers, scientists, engineers and BBA-MBA professionals (Fintech), Advanced students (MBA, PhDs, Postdocs) of Fintech, and for AI&ML applications. It will be relevant for new generation readers of any domain as it is multidisciplinary, includes sustainability, and comes with supplementary material, case studies, appendices, problems and solutions, self-testing, summaries, tables, glossary, etc.

Christo Ananth
Samarkand State University, Uzbekistan