Computational Intelligence for Autonomous Finance Challenges and Future Directions

Chapter 1 The Role of Autonomous Finance in the Era of Automatic Civilization
	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
Chapter 2 Analyzing the Latest Tools and Techniques for Stock Market Analysis
	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
Chapter 3 Challenges and Security Issues in Autonomous Finance
	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
Chapter 4 Involvement of Artificial Intelligence in Emerging Fintech Industry 4.0: A TCCM Framework
	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
Chapter 5 Robotic Process Automation in the Financial Sector
	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
Chapter 6 Integration of Fintech with Data Science (DS) and Artificial Intelligence (AI): A Challenging Footstep
	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
Chapter 7 Evaluation of Fintech: The Techno-Functional Application in Digital Banking
	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
Chapter 8 Real-Time Data Visualization and Autonomous Finance: Uses of Emerging Technologies
	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
Chapter 9 AI and ML Modeling and Autonomous Finance in Microfinance: An Overview
	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
Chapter 10 Application of Machine Learning Models in the Field of Autonomous Finance
	10.1 Overview
	10.2 Introduction
	10.3 Reinforcement Learning
		10.3.3 Reinforcement Learning and Deep Reinforcement Learning
			10.3.3.1 Deep Reinforcement Learning
		10.3.1 Demerits of Reinforcement Learning Techniques
		10.3.2 Markov Decision Process (MDP)
			10.3.2.1 Transition Function
	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
Chapter 11 Machine Learning Algorithm in Indian Stock Market for Revising and Refining the Equity Valuation Models
	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
Chapter 12 Hyper Automation and its Applicability in Automation Finance
	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
Chapter 13 Pre- and Post-COVID Autonomous Finance: Global Perspective
	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
Chapter 14 Emerging Trends and Future Directions in Artificial Intelligence for Next-Generation Computing
	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