Call for Chapters: Data Analytics and AI for Quantitative Risk Assessment and Financial Computation

Editors

Mohammad Galety, Samarkand International University of Technology, Uzbekistan
Jimbo Claver, Samarkand Interntional University of Technology, Uzbekistan
Sriharsha AV, Mohan Babu University, India
Narasimha Rao Vajjhala, University of New York Tirana, Albania
Arul Kumar Natarajan, Samarkand Interntional University of Technology, Uzbekistan

Call for Chapters

Proposals Submission Deadline: April 28, 2024
Full Chapters Due: June 30, 2024
Submission Date: June 30, 2024

Introduction

In the rapidly evolving finance landscape, combining data analytics and artificial intelligence (AI) with quantitative risk assessment and financial computation represents a transformative shift. Data Analytics and AI for Quantitative Risk Assessment and Financial Computation is designed to serve as a comprehensive guide and reference for finance professionals, risk managers, data scientists, and students at the intersection of these dynamic fields. This book will encapsulate the theoretical underpinnings, practical applications, challenges, and future directions of integrating cutting-edge AI and data analytics methodologies into financial practices. Data increasingly drives the financial industry. The volume, velocity, and variety of data generated by digital transactions, market exchanges, and social media platforms offer unprecedented financial analysis and decision-making opportunities. Simultaneously, the advent and advancement of AI technologies, including machine learning, natural language processing, and deep learning, have provided powerful tools to harness this data for predictive modeling, risk assessment, and algorithmic trading, among other applications. This synergy between data analytics and AI has enhanced the accuracy and efficiency of financial computations and risk assessments and redefined traditional financial models and strategies. This book is structured to navigate through the breadth and depth of this synergy. This book will begin with foundational topics in quantitative risk assessment, data analytics, and AI, laying the groundwork for understanding complex mechanics. Subsequent chapters will explore specific applications and methodologies, including machine learning algorithms for financial modeling, time series analysis for market prediction, and AI-driven strategies for risk management across credit, market, and operational domains. Furthermore, this book will address the regulatory landscape, ethical considerations, and the challenges of bias and fairness in AI applications in finance.

Objective

This book explores the intersection of mathematics, statistics, computer science, finance, AI, and information science. The objective of "Data Analytics and AI for Quantitative Risk Assessment and Financial Computation" is to furnish readers with a thorough understanding of how data analytics and artificial intelligence (AI) are revolutionizing the fields of quantitative risk assessment and financial computation. This book aims to bridge the gap between theoretical knowledge and practical application, offering a deep dive into the most current methodologies, technologies, and strategies driving innovation in finance. Through a carefully curated selection of topics, case studies, and expert insights, it seeks to empower finance professionals, risk managers, data scientists, and students with the tools and knowledge needed to navigate the complexities of today's financial landscape. By the end of this book, readers should be well-equipped to leverage AI and data analytics for sophisticated financial analysis, risk management, and decision-making, setting the stage for future advancements and leadership in the finance sector.

Target Audience

The target audience for "Data Analytics and AI for Quantitative Risk Assessment and Financial Computation" is multifaceted, aiming to cater to a broad spectrum of professionals and learners who are keen on exploring the confluence of data analytics, artificial intelligence (AI), and finance. This includes:

  1. Finance Professionals: Including bankers, financial analysts, portfolio managers, and risk managers who are looking to enhance their analytical capabilities, understand the latest AI technologies, and apply these innovations to risk assessment, financial modeling, and decision-making processes.
  2. Data Scientists and Analysts: Especially those with a focus or interest in the financial sector, seeking to deepen their understanding of financial principles and how their expertise in AI and data analytics can be applied to solve complex financial problems and innovate within the industry.
  3. Risk Management Specialists: Professionals focused on identifying, assessing, and mitigating financial risks, who are interested in leveraging AI and advanced data analytics for more accurate and comprehensive risk assessments.
  4. Regulatory and Compliance Officers: Those who navigate the complex regulatory landscape of the financial industry and are interested in how AI and data analytics impact compliance, regulatory reporting, and risk management practices.
  5. Academics and Researchers: Individuals in academia researching or teaching in the areas of finance, risk management, data science, and artificial intelligence, looking for comprehensive material that bridges these domains.
  6. Students: Undergraduate, graduate, and postgraduate students pursuing degrees in finance, economics, data science, computer science, or related fields who aim to gain a cutting-edge understanding of how AI and data analytics are applied in finance.
  7. Technology Entrepreneurs and Innovators: Those at the forefront of creating new financial technologies and startups, looking for insights into how data analytics and AI can be harnessed to disrupt traditional financial services and create new opportunities.



Recommended Topics

  1. Introduction to Quantitative Risk Assessment in Finance
  2. Fundamentals of Data Analytics and AI
  3. Probability Theory and Statistical Analysis for Risk Assessment
  4. Machine Learning Algorithms for Financial Modeling
  5. Time Series Analysis and Forecasting
  6. Portfolio Optimization and Asset Allocation
  7. Credit Risk Modeling and Assessment
  8. Market Risk Analysis and Value at Risk (VaR) Models
  9. Operational Risk Management
  10. Liquidity Risk Measurement and Management
  11. Regulatory Frameworks and Compliance
  12. Algorithmic Trading and High-Frequency Trading (HFT)
  13. Natural Language Processing (NLP) in Financial Analysis
  14. Blockchain and Cryptocurrencies Risk Assessment
  15. AI Ethics and Bias in Financial Models
  16. Big Data Technologies in Finance
  17. Risk Management in the Era of AI
  18. Practical Challenges and Considerations
  19. Quantitative Risk Management
  20. Portfolio Optimization
  21. Derivative Pricing
  22. Incomplete vs Complete Financial Markets
  23. Investment Problems Related to Innovative Growth Centers
  24. Artificial Intelligence and Beyond
  25. Case Studies in AI and Data Analytics for Finance
  26. Future Trends in AI and Quantitative Finance



Submission Procedure

Researchers and practitioners are invited to submit on or before April 28, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by May 12, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by June 30, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Data Analytics and AI for Quantitative Risk Assessment and Financial Computation. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2025.



Important Dates

April 28, 2024: Proposal Submission Deadline
May 12, 2024: Notification of Acceptance
June 30, 2024: Full Chapter Submission
August 4, 2024: Review Results Returned
September 1, 2024: Final Acceptance Notification
September 8, 2024: Final Chapter Submission



Inquiries

Mohammad Galety
Samarkand International University of Technology
galety.143@gmail.com

Jimbo Claver
Samarkand Interntional University of Technology
jimbo.maths@gmail.com

Sriharsha AV
Mohan Babu University
avsreeharsha@gmail.com

Narasimha Rao Vajjhala
University of New York Tirana
narasimharaonarasimha@gmail.com

Arul Kumar Natarajan
Samarkand Interntional University of Technology
itsprofarul@gmail.com



Classifications


Business and Management; Computer Science and Information Technology
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