Search

Browse Subject Areas

For Authors

Submit a Proposal

Computational Intelligence for Autonomous Finance

Edited by Deepa Gupta, Mukul Gupta, Rajesh Kumar Dhanaraj, Balamurugan Balusamy and Parth Mukul Gupta
Copyright: 2025   |   Expected Pub Date:2024/12/30
ISBN: 9781394233229  |  Hardcover  |  
334 pages

One Line Description
The book serves as an essential guide and a deep dive into the intersection of AI
and finance, providing readers with a thorough understanding of the current state,
challenges, and future possibilities of autonomous financial systems.

Audience
This book is aimed at researchers, industry professionals, policymakers, and graduate students in finance, computational intelligence, and related fields.

Description
In the rapidly evolving domain of autonomous finance, the convergence of computational intelligence techniques and financial technologies has paved the way for a new era of financial services. This transformation is driven by the integration of artificial intelligence (AI), machine learning (ML), blockchain,
and big data analytics into financial systems, leading to the development of more responsive, efficient, and personalized financial products and services. Computational Intelligence for Autonomous Finance delves into the heart of this technological revolution, offering a comprehensive exploration of the theoretical
foundations, practical applications, and future prospects of computational intelligence in the financial sector.
The backbone of autonomous finance is a complex, interconnected ecosystem that leverages computational intelligence to automate decision-making processes, optimize financial operations, and enhance customer experiences. The book introduces the concept of an Intelligent Autonomous Financial Network (IAFN),
which integrates various computational intelligence techniques with cutting-edge financial technologies to create a self-organizing, adaptive, and scalable financial system. The IAFN framework facilitates seamless interactions between diverse financial entities, enabling the provision of innovative financial services such
as automated trading, real-time risk management, personalized financial planning, and fraud detection. The book meticulously analyzes the key challenges including data security and privacy concerns, algorithmic biases, regulatory compliance, and the need for interoperable standards. It also presents state-of-the-art solutions and best practices for overcoming these challenges, emphasizing the importance of ethical AI, robust data protection mechanisms, transparent algorithms, and collaborative regulatory frameworks. It discusses emerging trends such as quantum computing, edge computing, and decentralized finance (DeFi), highlighting their potential to further transform the financial landscape. The book also addresses the societal implications of autonomous finance, including its impact on employment, wealth distribution, and financial inclusion, advocating for a balanced approach that maximizes benefits while minimizing negative outcomes.

Back to Top
Author / Editor Details
Deepa Gupta, PhD, is the Dean at GL Bajaj Institute of Management, Greater Noida, India. Her expertise extends to organized development, corporate relations, and international collaborations. Dr. Gupta is an active researcher who has published 15 national/international patents and has contributed more than 45
research papers to various international and national conferences and journals.

Mukul Gupta, PhD, is a principal at GL Bajaj Institute of Management, Greater Noida, India. His research focuses on consumer behavior to help understand the human-centric aspects of autonomous finance systems. He has published 12 national/international patents, more than 40 research papers, and
authored books.

Rajesh Kumar Dhanaraj, PhD, is a professor at the School of Computing Science and Engineering at Galgotias University in India. He has authored/edited more than 25 books on various technologies, 21 patents, and 50+ articles and papers in various refereed journals and international conferences.

Balamurugan Balusamy, PhD, is an associate dean of students at Shiv Nadar University at the Delhi-NCR Campus in Noida, India. He has authored/edited more than 80 books and more than 200 contributions to international journals and conferences.

Parth Mukul Gupta, is an innovative entrepreneur and the director at Zarthcorp Tech Pvt. Ltd. and of the Shri Sai Memorial Foundation, Greater Noida, India. He has experience in brand building, organizational development, and global collaborations and spearheads advancements in autonomous finance through
technological innovation and strategic growth initiatives.

Back to Top

Table of Contents
Preface
1. The Role of Autonomous Finance in the Era of Automatic Civilization
Sanjeet Singh, Geetika Madaan and Jaskiran Kaur
1.1 Introduction
1.2 The Concept of Autonomous Finance
1.2.1 Autonomous Finance: The Technology and Factors Driving Its Widespread Deployment
1.2.2 CFO’s Function in Autonomous Finance
1.2.3 Motives to Switch to an Autonomous Finance Structure
1.2.4 What is the Process of Autonomous Finance (How Does it Work)?
1.2.5 Advantages of Autonomous Finance
1.2.6 Challenges Associated with Autonomous Finance
1.3 Autonomous Finance: Prospects and Developments
1.4 Key Considerations for Implementing Autonomous Finance
1.5 Conclusion
References
2. Analyzing the Latest Tools and Techniques for Stock Market Analysis
Ochin Sharma, Raj Gaurang Tiwari, Suvarna Sharma and Annu Priya
2.1 Introduction
2.2 Need for Trading Softwares
2.3 How Software for Technical Analysis of the Indian Stock Market Operates
2.4 Helpful Tools to Analyze Stock Market
2.4.1 Masterswift 2.0
2.4.2 RichLive Trade
2.4.2.1 Key Features of RichLive Trade Software
2.4.3 MetaTrader 4
2.4.3.1 Key Features of MetaTrader4
2.4.4 MotiveWave
2.4.4.1 Key Features of MotiveWave
2.4.5 Spider Stock Market Software
2.4.6 Investar
2.4.6.1 Key Features
2.4.7 eSignal
2.4.7.1 Main Properties of eSignal
2.4.8 Sharekhan Trade Tiger
2.4.8.1 Main Properties of Trade Tiger
2.4.9 Trader Guide
2.4.9.1 Trader Guide Features
2.4.10 NinjaTrader
2.4.11 AmiBroker India
2.4.12 VectorVest
2.4.13 Profit Source Platform
2.4.14 Algo Trader
2.4.15 Deep Learning Using Python
2.5 Conclusion
References
3. Challenges and Security Issues in Autonomous Finance
Mukul Gupta, Deepa Gupta, Nitin Agrawal and Parth Mukul Gupta
3.1 Introduction
3.2 A Review of the Literature
3.3 Concerns Regarding the Protection of Identity and Privacy in Autonomous Finance
3.3.1 The Vulnerability of Data
3.3.1.1 The Challenge
3.3.1.2 Aspects of Danger
3.3.1.3 Various Methods of Risk Reduction
3.3.2 Dangers Posed by Cybersecurity
3.3.2.1 The Challenge
3.3.2.2 Most Frequent Attacks from the Opponent
3.3.2.3 Countermeasures
3.3.3 The Protection of Personal Privacy
3.3.3.1 The Challenge
3.3.3.2 The Dangers to Privacy
3.3.3.3 Preserving Individual Cofense
3.4 Using Algorithms to Make Decisions Can be Biased
3.4.1 Understanding Bias in Algorithms
3.4.2 Repercussions of Bias in the Financial Sector
3.5 Ensuring Fairness in Autonomous Finance
3.5.1 Openness and Responsibility for One’s Actions
3.5.2 Measures of Fairness and Compliance Monitoring
3.5.3 The Pre-Processing of Data and the Engineering of Features
3.5.4 Modifications to the Model
3.5.5 Considerations of Ethical Implications and Diverse Teams
3.6 Compliance with Regulations in the Field of Autonomous Finance
3.6.1 Navigating Legal Frameworks
3.6.1.1 Being Able to Adapt to Rapid Change
3.6.1.2 Data Privacy and Security
3.6.1.3 Anti-Money Laundering (AML) and Fraud Detection
3.6.1.4 Protection of Consumers
3.6.1.5 Operations Across Borders
3.6.2 Be Open and Honest
3.6.2.1 Capacity to Explain and Interpret Information
3.6.2.2 Fairness and the Elimination of a Bias
3.6.2.3 User Consent and Control
3.7 Gaining an Understanding of the Fundamentals of Operational Risk
3.8 Risks Encountered in the Operation of Autonomous Finance
3.9 Concerns Regarding Ethical Issues in Autonomous Finance
3.10 Consumer Trust in Autonomous Finance
3.10.1 Establishing Trust in Financial Systems that are Independent
3.10.1.1 Recognising the Concept of Autonomous Finance
3.10.2 User Education: Filling in the Gaps in Knowledge
3.10.2.1 Challenges in Technology and Expectations
3.10.2.2 Educating Users
References
4. Involvement of Artificial Intelligence in Emerging Fintech Industry 4.0: A TCCM Framework
Annu and Ravindra Tripathi
4.1 Introduction
4.1.1 Literature Review
4.2 Data and Methodology
4.2.1 Data Collection
4.2.1.1 The Source of Data Collection
4.2.1.2 Keyword Selection and Refinement Process
4.3 Results and Discussion
4.3.1 Bibliometric Data Analysis (Descriptive and Network)
4.4 Finding, Conclusion, and Research Directions
4.5 Summary
References
5. Robotic Process Automation in the Financial Sector
Neha Sonik, Deepa Gupta and Parul Gupta
5.1 Introduction
5.1.1 Robotic Process Automation in Banking
5.1.2 What is Finance Automation?
5.2 How are Financial Institutions Making Use of Robotics and Automation?
5.2.1 Importance of Banking in Robotic Process Automation
5.3 Major Use Cases of Robotic Process Automation in Banking and Finance
5.4 Minding Gaps in Financial Process Automation
5.5 The Key Benefits of Finance Automation
5.6 A List of Accounting and Financial Services Companies That are Using RPA
5.7 Steps to Deploy RPA in Banking and Finance
5.8 Conclusion
References
6. Integration of Fintech with Data Science (DS) and Artificial Intelligence (AI): A Challenging Footstep
Ankur Goel, Monisha Awasthi, Anamika Rana and Sushma Malik
6.1 Introduction
6.2 Historical Background of Fintech
6.2.1 Fintech 1.0
6.2.2 Fintech 2.0
6.2.3 Fintech 3.0
6.2.4 Fintech 4.0
6.3 Advantages of Fintech
6.3.1 Block Chain and Crypto Currency
6.3.2 Insurance (InsurTech)
6.3.3 Regulatory (RegTech)
6.3.4 Lending (LendTech)
6.3.5 Payments (PayTech)
6.3.6 Mobile Payments
6.3.7 Trading (TradeTech)
6.3.8 Robo-Advising and Stock-Trading Apps
6.3.9 Personal Finance (WealthTech)
6.3.10 International Money Transfers
6.3.11 Equity Financing
6.3.12 Accounting
6.3.13 Banking for Consumers (BankTech)
6.4 Role of Data Science and AI
6.5 Data Science and AI (DSAI) Making Smart Fintech
6.5.1 Complex System Methods
6.5.2 Automatic Contact Recognition and Response Synthesis
6.5.3 Analytics, Teaching, and Learning Strategies
6.5.4 Deep Financial Modeling
6.5.5 Techniques for Augmentation and Optimization
6.5.6 Smart EcoFin Companies and Services
6.5.7 Automated Analytics and Learning
6.5.8 Whole-of-Business and Privacy-Preserving Federated Fintech
6.6 Use Cases of Data Science in Fintech
6.6.1 Fraud Prevention
6.6.2 Risk Analysis
6.6.3 Customer Behavior Analysis
6.6.4 Credit Allocation
6.6.5 Predictive Analytics
6.6.6 Product Development
6.6.7 Algorithmic Trading (AT)
6.6.8 Personalized Marketing
6.7 Conclusion
References
7. Evaluation of Fintech: The Techno-Functional Application in Digital Banking
Priyanka Verma, Rajesh Kumar Dhanaraj, Deepa Gupta and Mukul Gupta
7.1 Introduction
7.2 Overview of Fintech
7.2.1 Details of the Working Algorithm
7.2.2 Relationship Between FINTECH and Modern Financial Application in Digital Banking
7.2.3 Advantages of the Application
7.2.4 Barriers in the Implementation Process
7.2.5 Details of the Security System Present in the Application
7.3 Theoretical Overview
7.4 Measurement of the Success Factor of Fintech in Digital Banking
7.5 Summary
References
8. Real-Time Data Visualization and Autonomous Finance: Uses of Emerging Technologies
Govind Singh, Lokesh Verma and Anshika Baliyan
8.1 Introduction
8.1.1 Industry 4.0
8.1.2 Business Process
8.1.2.1 Management Process
8.1.2.2 Operating Process
8.1.2.3 Support Process
8.1.3 Finance
8.2 Thriving in the Tech Age: How Businesses Adapt to Emerging Technologies
8.2.1 Boosting Efficiency and Innovation: The Critical Role of Adapting to New Technologies
8.2.2 Navigating the Digital Age: The Current State of Technological Adoption
8.2.3 The Driving Force: Why Businesses Embrace New Technologies
8.2.4 Top Seven Emerging Technologies Businesses are Embracing
8.3 The Future of Work and Innovation: Emerging Technologies Transforming Businesses
8.3.1 Actionable Insights at Your Fingertips: The Power of Embedded BI
8.3.1.1 Application of Embedded BI
8.3.1.2 Embedded BI in Supply Chain Management and Logistic
8.3.1.3 Embedded BI in Sales and Services
8.3.2 Augmented Analytics
8.3.2.1 Importance of Augmented Analytics Prospecting the Opportunity of Big Data
8.3.2.2 Benefits and Uses of Augmented Analytics in Business
8.3.2.3 Use of Analytics in Business
8.3.3 Cloud Computing
8.3.3.1 How Cloud Management Works
8.3.3.2 Benefits of Cloud Management
8.3.4 Artificial Intelligence
8.3.4.1 Learning Processes
8.3.4.2 Reasoning Process
8.3.4.3 Self-Correction Process
8.3.5 Current Scenario of Artificial Intelligence in Businesses
8.3.6 Application of Artificial Intelligence in Businesses
8.3.6.1 Machine Learning
8.3.6.2 Cybersecurity
8.3.6.3 Customer Relationship Management
8.3.6.4 Internet and Data Research
8.4 Major Emerging Technologies in Finance
8.4.1 Robotics Process Automation (RPA)
8.4.2 Blockchain
8.4.2.1 Types of Blockchain
8.4.3 Autonomous Finance
8.4.4 Internet of Things (IoT)
8.5 Risk Associated with Emerging Technologies
8.6 Conclusion
References
9. AI and ML Modeling and Autonomous Finance in Microfinance: An Overview
D. N. Rao and Maheswaran Mahalingam
9.1 Understanding Autonomous Finance and Microfinance
9.1.1 Context
9.1.1.1 Application Areas
9.1.1.2 Scope
9.1.1.3 Significance
9.2 Readiness of MFIs for Autonomous Finance Transformation
9.2.1 Autonomous Finance and Microfinance—A Prelude
9.2.2 Diverged Microfinance Global Market
9.2.3 Autonomous Finance as a Turning Point
9.2.3.1 Key Components of Autonomous Finance
9.2.3.2 Technology Drivers of Autonomous Finance
9.3 Solution Drivers in the Life Cycle Journey of an MFI Customer
9.3.1 The Life Cycle Journey of an MFI Customer
9.3.2 Solution Drivers Across the Phases of the Life Cycle Journey
9.3.3 The Impact of Autonomous Finance in the Journey Cycle
9.4 Readiness of MFIs for Autonomous Finance Operations
9.5 Technology and AI and ML Enablers of Autonomous Finance for MFIs
9.5.1 Technology Enabled Autonomous Finance for MFIs
9.5.2 Optical Character Recognition (OCR)
9.5.3 Robotic Process Automation (RPA)
9.5.4 Big Data Driven Automated Approvals
9.6 Critical Business Needs of Autonomous Finance
9.6.1 Autonomous Receivables
9.6.2 Autonomous Treasury
9.6.3 Autonomous Accounting
9.7 AI and ML Analytical Models for MFIs
9.7.1 Logistic Regression
9.7.2 Logistic Regression with Ridge Regularization
9.7.3 An Examination of Linear Discriminants
9.7.4 K-Nearest Neighbor
9.7.5 Decision Trees
9.7.6 Support Vector Machines
9.7.7 XGBoost
9.8 Overall Deployment and Suitability
9.9 Roadmap for Autonomous Finance in MFIs
9.9.1 Transformation Operations for MFI
9.10 Stage-1: Operation Moonwalk
9.10.1 Stakeholders Vision
9.10.2 Prioritize Autonomous Finance Goals
9.10.3 Set KRIs and Its Impact
9.10.4 Straw Man Project
9.10.5 Assess Current State
9.10.6 Funding Needs
9.11 Stage 2—Operation Sun Shine
9.11.1 Setup Governance
9.11.2 Other Key Drivers
9.12 Stage 3 Operation Bloomsdale
9.13 Improvement Opportunities of Autonomous Finance for MFIs
9.13.1 Precautions in Adopting Autonomous Finance by MFIs
9.13.2 Data Privacy
9.13.3 AI and ML Governance
9.13.4 More Machine vs Less Human
9.13.5 Ethical Considerations
9.13.6 Regulatory Compliance
9.13.7 Surveillance and Discrimination
9.14 Embracing Future AI Agents and Robotics of Autonomous Finance
References
10. Application of Machine Learning Models in the Field of Autonomous Finance
Umesh Gupta, Shriyash Saxena, Sachin Kumar Yadav and Aditya Bhardwaj
10.1 Overview
10.2 Introduction
10.3 Reinforcement Learning
10.3.1 Demerits of Reinforcement Learning Techniques
10.3.2 Markov Decision Process (MDP)
10.3.2.1 Transition Function
10.3.3 Reinforcement Learning and Deep Reinforcement Learning
10.3.3.1 Deep Reinforcement Learning
10.4 Neural Network Basics
10.4.1 Fully Connected Neural Network (FNN)
10.4.2 Convolutional Neural Network (CNN)
10.4.3 Recurrent Neural Networks (RNN)
10.4.4 Deep Value-Based Methods
10.5 Management of Information for Credit Risk
10.5.1 Management of Information for Fraud Detection
10.5.2 Portfolio Optimization Driven by Big Data
10.5.3 Management of Information for Assets and Derivative Market
10.5.4 Algorithmic Trading
10.5.5 Big Data Analysis with the Usage of Text Mining
10.5.6 Essence of Convolutional Neural Network
10.6 Sentiment Analysis with Data Mining Approach
10.6.1 Case Study of Wang
10.7 Conclusion
References
11. Machine Learning Algorithm in Indian Stock Market for Revising and Refining the Equity Valuation Models
Nitha K. P., Suraj E. S. and Ranjith Karat
11.1 Introduction
11.1.1 Multiple Regression Machine Learning Algorithm
11.1.2 Classification
11.2 Objectives of the Study
11.3 Methodology
11.3.1 Softwares Used
11.4 Review of Literature
11.5 Machine Learning for Equity Valuation Models
11.6 Architecture of Refined Equity Models
11.6.1 Architecture of Refined Price to Earnings Model (P/E) Using Multiple Regression Machine Learning Approach
11.6.2 Architecture of Refined Price to Book Value Model Using Multiple Regression Machine Learning Approach
11.6.3 Architecture of Refined Capital Asset Pricing Model Using Multiple Regression Machine Learning Approach
11.7 Analysis—Checking the Valuation Accuracy of Revised and Refined Models Using Machine Learning Approach
11.8 Conclusion
References
12. Hyper Automation and its Applicability in Automation Finance
Pushpendra Pal Singh, Rakesh Kumar Dixit and Rajesh Kumar Dhanaraj
12.1 Introduction
12.2 Background
12.3 Hyper Automation: Evolution, Technologies, and Impact in the Digital Era
12.4 Automation-(2)-Hyper Automation: Gartner
12.5 Could Hyper Automation be a Name for AI Plus RPA?
12.6 Sophistication of the Automation
12.7 Hyper Automation Process Flow
12.7.1 Technologies
12.7.2 Robotics Process Automation (RPA)
12.7.3 Artificial Intelligence (AI)
12.7.4 Machine Language (ML)
12.7.5 Optical Character Recognition (OCR)
12.7.6 Language Understanding Intelligent Service (LUIS)
12.7.7 Hyper Automation Technological Ecosystem
12.8 Banking and Finance Applications
12.8.1 Marketing
12.8.2 Sales and Distribution
12.8.3 Regulatory Reporting
12.8.4 ICICI Bank
12.8.5 Softbank
12.9 Conclusions
References
13. Pre- and Post-COVID Autonomous Finance: Global Perspective
Shikha Singh, Deepa Gupta, Roshan Kumar and Balamurugan Balusamy
13.1 Introduction
13.2 Literature Review
13.2.1 Objectives
13.2.2 Research Design
13.2.3 Data Collection
13.3 Factors Behind the Digitalization of Financial Services During the COVID Pandemic
13.4 Challenges/Barriers for FinTech
13.5 Advantages and Disadvantages of Market Structure Modifications Towards the Digitalization of FinTech Services
13.5.1 Advantages
13.5.2 Disadvantages
13.6 Conclusion
References
14. Emerging Trends and Future Directions in Artificial Intelligence for Next-Generation Computing
Rafael Vargas-Bernal
14.1 Introduction
14.2 Concepts of Neuromorphic Computing, Artificial Intelligence, and Memristor
14.3 Advantages of Two-Dimensional Materials Used in Neuromorphic Computing
14.4 Devices Implemented with Two-Dimensional Materials to Evolve Artificial Intelligence
14.5 Future Research Directions
14.6 Summary
Acknowledgments
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page