Data Science and Multiple Criteria Decision Making Approaches in Finance: Applications and Methods

This book discusses the application of data science and fuzzy multiple criteria decision-making methods to assess financial issues and provide suggestions for improving the efficiency and sustainability of the financial system.

Format: Paperback / softback
Length: 173 pages
Publication date: 31 May 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into the realm of finance, employing data science and fuzzy multiple criteria decision-making (MCDM) methodologies to examine and evaluate critical financial issues. It serves as an invaluable guide for readers, introducing them to a diverse array of data science techniques and showcasing their practical applications across various domains, including business, health, economics, finance, and engineering. Moreover, the book offers insightful recommendations based on its assessments, aimed at improving the efficiency and sustainability of the financial system. With its broad scope, this book empowers readers to gain a deeper understanding of the assessment and evaluation of financial challenges through the lens of data science and MCDM approaches.

Introduction:
The field of finance has witnessed a remarkable transformation in recent years, driven by the advent of data science and advanced analytical techniques. This book aims to harness the power of these tools to address complex financial challenges and provide valuable insights into the world of finance. By integrating data science and MCDM methodologies, this book offers a comprehensive approach to evaluating financial issues and making informed decisions.

Chapter 1: Data Science Methods:
In this chapter, the authors introduce readers to a range of data science methods, including machine learning, natural language processing, and big data analytics. They discuss the principles and techniques behind these methods and their applications in the fields of finance. The chapter also highlights the importance of data quality, data preprocessing, and model selection in achieving accurate and reliable results.

Chapter 2: Application in Finance:
The second chapter explores the practical applications of data science methods in the realm of finance. It discusses how data science can be used to analyze financial data, identify patterns, and make predictions. The chapter also highlights the role of data visualization in communicating financial insights to stakeholders. Examples of real-world applications in finance, such as credit risk assessment, portfolio optimization, and fraud detection, are provided to illustrate the practical benefits of data science.

Chapter 3: Application in Health:
In the third chapter, the authors extend the application of data science methods to the health domain. They discuss how data science can be used to analyze medical records, identify risk factors, and develop personalized healthcare strategies. The chapter also highlights the importance of ethical considerations and privacy protection in the healthcare industry. Examples of real-world applications in health, such as disease prediction, drug discovery, and patient monitoring, are provided to illustrate the potential of data science in improving healthcare outcomes.

Chapter 4: Application in Economics:
The fourth chapter explores the application of data science methods in the field of economics. It discusses how data science can be used to analyze economic data, identify trends, and make policy recommendations. The chapter also highlights the role of economic models in forecasting economic outcomes and the challenges associated with model validation. Examples of real-world applications in economics, such as economic forecasting, market analysis, and policy evaluation, are provided to illustrate the practical benefits of data science in economic decision-making.

Chapter 5: Application in Engineering:
The fifth chapter focuses on the application of data science methods in the field of engineering. It discusses how data science can be used to analyze engineering data, optimize engineering processes, and develop innovative engineering solutions. The chapter also highlights the role of data-driven decision-making in engineering design and the importance of interdisciplinary collaboration in addressing complex engineering challenges. Examples of real-world applications in engineering, such as structural analysis, product design, and energy optimization, are provided to illustrate the potential of data science in driving technological advancements.

Conclusion:
In conclusion, this book serves as a valuable resource for professionals, researchers, and students interested in exploring the intersection of data science and finance. By providing a comprehensive overview of data science methods and their applications in various domains, it offers a roadmap for developing innovative solutions to complex financial challenges. The book's insights and recommendations will undoubtedly contribute to the advancement of the financial industry and the broader field of data science.

Weight: 302g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030741785
Edition number: 1st ed. 2021