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资料全名：Computational Intelligence for Autonomous Finance Challenges and Future Directions
In an era marked by unprecedented technological advancements, the financial sector stands at the cusp of a transformative revolution, propelled by the burgeoning field of computational intelligence. This book serves as a seminal exploration into this dynamic domain, offering a comprehensive examination of how artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) are redefining the landscapes of finance and banking. This book is designed to navigate the intricate interplay between emerging technologies and financial services, addressing both the profound opportunities and the intricate challenges that lie ahead. The genesis of this book lies in the recognition of a new age of automatic civilization, where autonomous finance emerges not merely as a possibility but as an imperative for survival and prosperity. Chapter 1 sets the stage by delving into the role of autonomous finance in the era of automatic civilization. This section lays the groundwork for understanding the transformative potential of computational intelligence in finance. As we progress through the book, each chapter systematically unfolds the multifaceted aspects of this revolution. Chapter 2 provides a deep dive into the cutting-edge technologies shaping stock market strategies today. This is followed by an insightful chapter on the challenges and security issues that are present in autonomous finance, highlighting the paramount importance of cybersecurity in a digitally dependent financial world. In subsequent chapters, the book expands its purview to explore the symbiotic relationship between AI and the Fintech industry, the pivotal role of RPA in financial operations, and the intricate integration of fintech with data science and AI. Notably, Chapter 4 introduces a TCCM (Theory, Context, Characteristics, and Methodology) framework for understanding AI’s role in Fintech Industry 4.0, offering a structured approach to navigating the complex landscape. Chapters 5 through 8 delve into specific applications and innovations in the field, from robotic process automation in finance to the techno-functional applications in digital banking, and the uses of real-time data xvii Downloaded from https://onlinelibrary.wiley.com/doi/ by ibrahim ragab - Oregon Health & Science Univer , Wiley Online Library on [20/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License xviii Preface visualization in autonomous finance. These discussions underscore the practical implementations of computational intelligence and highlight the ongoing evolution of financial technologies. Next, our focus shifts toward more specialized applications of AI and ML in finance, with chapters dedicated to autonomous finance in microfinance, the application of ML models in autonomous finances, and the use of machine learning algorithms in the Indian stock market. Each chapter provides a unique lens through which to view the potential for computational intelligence to revolutionize financial models and valuation methods. As the book nears its conclusion, it addresses the broader implications of these technologies in the context of hyper-automation and its applicability in finance, as well as the impact of the COVID-19 pandemic on autonomous finance from a global perspective. The final chapter offers a forward-looking perspective on the future trajectories of AI in finance, inviting readers to contemplate the vast horizons yet to be explored. More than just an academic treatise, this volume is a call to action for innovators, policymakers, and practitioners to embrace the challenges and opportunities presented by autonomous finance. It will inspire a new generation of financial technologies that are secure, efficient, and, above all, equitable. As we stand on the brink of this new era, this book serves as a beacon, guiding the way toward a future where finance is not just autonomous but also inherently more human.
1 The Role of Autonomous Finance in the Era of Automatic
Civilization 1
Sanjeet Singh, Geetika Madaan and Jaskiran Kaur
1.1 Introduction 2
1.2 The Concept of Autonomous Finance 2
1.2.1 Autonomous Finance: The Technology and Factors
Driving Its Widespread Deployment 3
1.2.2 CFO’s Function in Autonomous Finance 3
1.2.3 Motives to Switch to an Autonomous Finance Structure 4
1.2.4 What is the Process of Autonomous Finance
(How Does it Work)? 6
1.2.5 Advantages of Autonomous Finance 6
1.2.6 Challenges Associated with Autonomous Finance 7
1.3 Autonomous Finance: Prospects and Developments 9
1.4 Key Considerations for Implementing Autonomous Finance 14
1.5 Conclusion 15
References 15
2 Analyzing the Latest Tools and Techniques for Stock Market
Analysis 21
Ochin Sharma, Raj Gaurang Tiwari, Suvarna Sharma
and Annu Priya
2.1 Introduction 21
2.2 Need for Trading Softwares 23
2.3 How Software for Technical Analysis of the Indian Stock
Market Operates 23
2.4 Helpful Tools to Analyze Stock Market 24
2.4.1 Masterswift 2.0 24
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vi Contents
2.4.2 RichLive Trade 25
2.4.2.1 Key Features of RichLive Trade Software 25
2.4.3 MetaTrader 4 25
2.4.3.1 Key Features of MetaTrader4 26
2.4.4 MotiveWave 27
2.4.4.1 Key Features of MotiveWave 27
2.4.5 Spider Stock Market Software 28
2.4.6 Investar 29
2.4.6.1 Key Features 30
2.4.7 eSignal 30
2.4.7.1 Main Properties of eSignal 31
2.4.8 Sharekhan Trade Tiger 31
2.4.8.1 Main Properties of Trade Tiger 32
2.4.9 Trader Guide 33
2.4.9.1 Trader Guide Features 33
2.4.10 NinjaTrader 33
2.4.11 AmiBroker India 34
2.4.12 VectorVest 35
2.4.13 Profit Source Platform 36
2.4.14 Algo Trader 37
2.4.15 Deep Learning Using Python 38
2.5 Conclusion 39
References 40
3 Challenges and Security Issues in Autonomous Finance 43
Mukul Gupta, Deepa Gupta, Nitin Agrawal
and Parth Mukul Gupta
3.1 Introduction 44
3.2 A Review of the Literature 45
3.3 Concerns Regarding the Protection of Identity and Privacy
in Autonomous Finance 46
3.3.1 The Vulnerability of Data 46
3.3.1.1 The Challenge 46
3.3.1.2 Aspects of Danger 47
3.3.1.3 Various Methods of Risk Reduction 47
3.3.2 Dangers Posed by Cybersecurity 47
3.3.2.1 The Challenge 47
3.3.2.2 Most Frequent Attacks from the Opponent 47
3.3.2.3 Countermeasures 48
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Contents vii
3.3.3 The Protection of Personal Privacy 48
3.3.3.1 The Challenge 48
3.3.3.2 The Dangers to Privacy 48
3.3.3.3 Preserving Individual Cofense 48
3.4 Using Algorithms to Make Decisions Can be Biased 49
3.4.1 Understanding Bias in Algorithms 49
3.4.2 Repercussions of Bias in the Financial Sector 49
3.5 Ensuring Fairness in Autonomous Finance 50
3.5.1 Openness and Responsibility for One’s Actions 50
3.5.2 Measures of Fairness and Compliance Monitoring 50
3.5.3 The Pre-Processing of Data and the Engineering
of Features 50
3.5.4 Modifications to the Model 50
3.5.5 Considerations of Ethical Implications
and Diverse Teams 51
3.6 Compliance with Regulations in the Field of Autonomous
Finance 51
3.6.1 Navigating Legal Frameworks 51
3.6.1.1 Being Able to Adapt to Rapid Change 51
3.6.1.2 Data Privacy and Security 52
3.6.1.3 Anti-Money Laundering (AML) and Fraud
Detection 52
3.6.1.4 Protection of Consumers 52
3.6.1.5 Operations Across Borders 52
3.6.2 Be Open and Honest 53
3.6.2.1 Capacity to Explain and Interpret
Information 53
3.6.2.2 Fairness and the Elimination of a Bias 53
3.6.2.3 User Consent and Control 53
3.7 Gaining an Understanding of the Fundamentals
of Operational Risk 54
3.8 Risks Encountered in the Operation of Autonomous Finance 55
3.9 Concerns Regarding Ethical Issues in Autonomous Finance 57
3.10 Consumer Trust in Autonomous Finance 59
3.10.1 Establishing Trust in Financial Systems
that are Independent 59
3.10.1.1 Recognising the Concept of Autonomous
Finance 59
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viii Contents
3.10.2 User Education: Filling in the Gaps in Knowledge 60
3.10.2.1 Challenges in Technology and Expectations 60
3.10.2.2 Educating Users 60
References 61
4 Involvement of Artificial Intelligence in Emerging Fintech
Industry 4.0: A TCCM Framework 65
Annu and Ravindra Tripathi
4.1 Introduction 66
4.1.1 Literature Review 68
4.2 Data and Methodology 69
4.2.1 Data Collection 69
4.2.1.1 The Source of Data Collection 69
4.2.1.2 Keyword Selection and Refinement Process 70
4.3 Results and Discussion 70
4.3.1 Bibliometric Data Analysis (Descriptive and Network) 70
4.4 Finding, Conclusion, and Research Directions 76
4.5 Summary 77
References 77
5 Robotic Process Automation in the Financial Sector 81
Neha Sonik, Deepa Gupta and Parul Gupta
5.1 Introduction 81
5.1.1 Robotic Process Automation in Banking 82
5.1.2 What is Finance Automation? 82
5.2 How are Financial Institutions Making Use of Robotics
and Automation? 83
5.2.1 Importance of Banking in Robotic Process
Automation 84
5.3 Major Use Cases of Robotic Process Automation
in Banking and Finance 84
5.4 Minding Gaps in Financial Process Automation 91
5.5 The Key Benefits of Finance Automation 92
5.6 A List of Accounting and Financial Services Companies
That are Using RPA 95
5.7 Steps to Deploy RPA in Banking and Finance 97
5.8 Conclusion 99
References 99
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Contents ix
6 Integration of Fintech with Data Science (DS) and Artificial
Intelligence (AI): A Challenging Footstep 101
Ankur Goel, Monisha Awasthi, Anamika Rana
and Sushma Malik
6.1 Introduction 102
6.2 Historical Background of Fintech 102
6.2.1 Fintech 1.0 102
6.2.2 Fintech 2.0 103
6.2.3 Fintech 3.0 104
6.2.4 Fintech 4.0 104
6.3 Advantages of Fintech 104
6.3.1 Block Chain and Crypto Currency 105
6.3.2 Insurance (InsurTech) 105
6.3.3 Regulatory (RegTech) 105
6.3.4 Lending (LendTech) 106
6.3.5 Payments (PayTech) 106
6.3.6 Mobile Payments 106
6.3.7 Trading (TradeTech) 106
6.3.8 Robo-Advising and Stock-Trading Apps 107
6.3.9 Personal Finance (WealthTech) 107
6.3.10 International Money Transfers 107
6.3.11 Equity Financing 107
6.3.12 Accounting 108
6.3.13 Banking for Consumers (BankTech) 108
6.4 Role of Data Science and AI 108
6.5 Data Science and AI (DSAI) Making Smart Fintech 111
6.5.1 Complex System Methods 112
6.5.2 Automatic Contact Recognition and Response
Synthesis 113
6.5.3 Analytics, Teaching, and Learning Strategies 113
6.5.4 Deep Financial Modeling 114
6.5.5 Techniques for Augmentation and Optimization 114
6.5.6 Smart EcoFin Companies and Services 115
6.5.7 Automated Analytics and Learning 115
6.5.8 Whole-of-Business and Privacy-Preserving
Federated Fintech 115
6.6 Use Cases of Data Science in Fintech 116
6.6.1 Fraud Prevention 116
6.6.2 Risk Analysis 117
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x Contents
6.6.3 Customer Behavior Analysis 117
6.6.4 Credit Allocation 118
6.6.5 Predictive Analytics 119
6.6.6 Product Development 119
6.6.7 Algorithmic Trading (AT) 119
6.6.8 Personalized Marketing 119
6.7 Conclusion 119
References 120
7 Evaluation of Fintech: The Techno-Functional Application
in Digital Banking 123
Priyanka Verma, Rajesh Kumar Dhanaraj, Deepa Gupta
and Mukul Gupta
7.1 Introduction 123
7.2 Overview of Fintech 124
7.2.1 Details of the Working Algorithm 125
7.2.2 Relationship Between FINTECH and Modern
Financial Application in Digital Banking 128
7.2.3 Advantages of the Application 131
7.2.4 Barriers in the Implementation Process 133
7.2.5 Details of the Security System Present
in the Application 135
7.3 Theoretical Overview 136
7.4 Measurement of the Success Factor of Fintech
in Digital Banking 137
7.5 Summary 139
References 139
8 Real-Time Data Visualization and Autonomous Finance:
Uses of Emerging Technologies 143
Govind Singh, Lokesh Verma and Anshika Baliyan
8.1 Introduction 144
8.1.1 Industry 4.0 144
8.1.2 Business Process 145
8.1.2.1 Management Process 147
8.1.2.2 Operating Process 147
8.1.2.3 Support Process 147
8.1.3 Finance 147
8.2 Thriving in the Tech Age: How Businesses Adapt
to Emerging Technologies 148
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Contents xi
8.2.1 Boosting Efficiency and Innovation: The Critical
Role of Adapting to New Technologies 149
8.2.2 Navigating the Digital Age: The Current State
of Technological Adoption 149
8.2.3 The Driving Force: Why Businesses Embrace
New Technologies 150
8.2.4 Top Seven Emerging Technologies Businesses
are Embracing 150
8.3 The Future of Work and Innovation: Emerging Technologies
Transforming Businesses 151
8.3.1 Actionable Insights at Your Fingertips:
The Power of Embedded BI 151
8.3.1.1 Application of Embedded BI 152
8.3.1.2 Embedded BI in Supply Chain Management
and Logistic 152
8.3.1.3 Embedded BI in Sales and Services 153
8.3.2 Augmented Analytics 153
8.3.2.1 Importance of Augmented Analytics
Prospecting the Opportunity of Big Data 153
8.3.2.2 Benefits and Uses of Augmented Analytics
in Business 154
8.3.2.3 Use of Analytics in Business 155
8.3.3 Cloud Computing 155
8.3.3.1 How Cloud Management Works 156
8.3.3.2 Benefits of Cloud Management 156
8.3.4 Artificial Intelligence 157
8.3.4.1 Learning Processes 157
8.3.4.2 Reasoning Process 157
8.3.4.3 Self-Correction Process 157
8.3.5 Current Scenario of Artificial Intelligence
in Businesses 158
8.3.6 Application of Artificial Intelligence in Businesses 158
8.3.6.1 Machine Learning 158
8.3.6.2 Cybersecurity 158
8.3.6.3 Customer Relationship Management 158
8.3.6.4 Internet and Data Research 159
8.4 Major Emerging Technologies in Finance 159
8.4.1 Robotics Process Automation (RPA) 159
8.4.2 Blockchain 161
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xii Contents
8.4.2.1 Types of Blockchain 161
8.4.3 Autonomous Finance 162
8.4.4 Internet of Things (IoT) 163
8.5 Risk Associated with Emerging Technologies 164
8.6 Conclusion 165
References 166
9 AI and ML Modeling and Autonomous Finance
in Microfinance: An Overview 167
D. N. Rao and Maheswaran Mahalingam
9.1 Understanding Autonomous Finance and Microfinance 168
9.1.1 Context 168
9.1.1.1 Application Areas 168
9.1.1.2 Scope 169
9.1.1.3 Significance 170
9.2 Readiness of MFIs for Autonomous Finance Transformation 170
9.2.1 Autonomous Finance and Microfinance—A Prelude 170
9.2.2 Diverged Microfinance Global Market 171
9.2.3 Autonomous Finance as a Turning Point 172
9.2.3.1 Key Components of Autonomous Finance 172
9.2.3.2 Technology Drivers of Autonomous Finance 172
9.3 Solution Drivers in the Life Cycle Journey of an MFI
Customer 173
9.3.1 The Life Cycle Journey of an MFI Customer 173
9.3.2 Solution Drivers Across the Phases of the
Life Cycle Journey 175
9.3.3 The Impact of Autonomous Finance in the Journey
Cycle 177
9.4 Readiness of MFIs for Autonomous Finance Operations 177
9.5 Technology and AI and ML Enablers of Autonomous
Finance for MFIs 180
9.5.1 Technology Enabled Autonomous Finance for MFIs 180
9.5.2 Optical Character Recognition (OCR) 180
9.5.3 Robotic Process Automation (RPA) 180
9.5.4 Big Data Driven Automated Approvals 181
9.6 Critical Business Needs of Autonomous Finance 182
9.6.1 Autonomous Receivables 182
9.6.2 Autonomous Treasury 182
9.6.3 Autonomous Accounting 182
9.7 AI and ML Analytical Models for MFIs 182
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Contents xiii
9.7.1 Logistic Regression 184
9.7.2 Logistic Regression with Ridge Regularization 185
9.7.3 An Examination of Linear Discriminants 186
9.7.4 K-Nearest Neighbor 186
9.7.5 Decision Trees 186
9.7.6 Support Vector Machines 187
9.7.7 XGBoost 187
9.8 Overall Deployment and Suitability 188
9.9 Roadmap for Autonomous Finance in MFIs 188
9.9.1 Transformation Operations for MFI 188
9.10 Stage-1: Operation Moonwalk 190
9.10.1 Stakeholders Vision 190
9.10.2 Prioritize Autonomous Finance Goals 190
9.10.3 Set KRIs and Its Impact 190
9.10.4 Straw Man Project 190
9.10.5 Assess Current State 191
9.10.6 Funding Needs 192
9.11 Stage 2—Operation Sun Shine 192
9.11.1 Setup Governance 192
9.11.2 Other Key Drivers 192
9.12 Stage 3 Operation Bloomsdale 193
9.13 Improvement Opportunities of Autonomous Finance
for MFIs 193
9.13.1 Precautions in Adopting Autonomous Finance
by MFIs 193
9.13.2 Data Privacy 193
9.13.3 AI and ML Governance 194
9.13.4 More Machine vs Less Human 194
9.13.5 Ethical Considerations 194
9.13.6 Regulatory Compliance 195
9.13.7 Surveillance and Discrimination 195
9.14 Embracing Future AI Agents and Robotics of Autonomous
Finance 195
References 196
10 Application of Machine Learning Models in the Field
of Autonomous Finance 199
Umesh Gupta, Shriyash Saxena, Sachin Kumar Yadav
and Aditya Bhardwaj
10.1 Overview 199
10.2 Introduction 201
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xiv Contents
10.3 Reinforcement Learning 203
10.3.1 Demerits of Reinforcement Learning Techniques 204
10.3.2 Markov Decision Process (MDP) 204
10.3.2.1 Transition Function 205
10.3.3 Reinforcement Learning and Deep
Reinforcement Learning 208
10.3.3.1 Deep Reinforcement Learning 209
10.4 Neural Network Basics 209
10.4.1 Fully Connected Neural Network (FNN) 210
10.4.2 Convolutional Neural Network (CNN) 211
10.4.3 Recurrent Neural Networks (RNN) 212
10.4.4 Deep Value-Based Methods 212
10.5 Management of Information for Credit Risk 213
10.5.1 Management of Information for Fraud Detection 213
10.5.2 Portfolio Optimization Driven by Big Data 213
10.5.3 Management of Information for Assets
and Derivative Market 213
10.5.4 Algorithmic Trading 214
10.5.5 Big Data Analysis with the Usage of Text Mining 214
10.5.6 Essence of Convolutional Neural Network 215
10.6 Sentiment Analysis with Data Mining Approach 215
10.6.1 Case Study of Wang 215
10.7 Conclusion 216
References 216
11 Machine Learning Algorithm in Indian Stock Market
for Revising and Refining the Equity Valuation Models 221
Nitha K. P., Suraj E. S. and Ranjith Karat
11.1 Introduction 221
11.1.1 Multiple Regression Machine Learning Algorithm 222
11.1.2 Classification 222
11.2 Objectives of the Study 223
11.3 Methodology 223
11.3.1 Softwares Used 224
11.4 Review of Literature 225
11.5 Machine Learning for Equity Valuation Models 228
11.6 Architecture of Refined Equity Models 232
11.6.1 Architecture of Refined Price to Earnings Model
(P/E) Using Multiple Regression Machine
Learning Approach 232
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Contents xv
11.6.2 Architecture of Refined Price to Book Value
Model Using Multiple Regression Machine
Learning Approach 232
11.6.3 Architecture of Refined Capital Asset Pricing
Model Using Multiple Regression Machine
Learning Approach 232
11.7 Analysis—Checking the Valuation Accuracy of Revised
and Refined Models Using Machine Learning Approach 234
11.8 Conclusion 240
References 240
12 Hyper Automation and its Applicability in Automation
Finance 243
Pushpendra Pal Singh, Rakesh Kumar Dixit
and Rajesh Kumar Dhanaraj
12.1 Introduction 244
12.2 Background 245
12.3 Hyper Automation: Evolution, Technologies, and Impact
in the Digital Era 247
12.4 Automation-(2)-Hyper Automation: Gartner 249
12.5 Could Hyper Automation be a Name
for AI Plus RPA? 250
12.6 Sophistication of the Automation 252
12.7 Hyper Automation Process Flow 254
12.7.1 Technologies 255
12.7.2 Robotics Process Automation (RPA) 256
12.7.3 Artificial Intelligence (AI) 256
12.7.4 Machine Language (ML) 257
12.7.5 Optical Character Recognition (OCR) 258
12.7.6 Language Understanding Intelligent Service (LUIS) 258
12.7.7 Hyper Automation Technological Ecosystem 259
12.8 Banking and Finance Applications 260
12.8.1 Marketing 261
12.8.2 Sales and Distribution 261
12.8.3 Regulatory Reporting 262
12.8.4 ICICI Bank 262
12.8.5 Softbank 263
12.9 Conclusions 264
References 265
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xvi Contents
13 Pre- and Post-COVID Autonomous Finance: Global
Perspective 269
Shikha Singh, Deepa Gupta, Roshan Kumar
and Balamurugan Balusamy
13.1 Introduction 270
13.2 Literature Review 271
13.2.1 Objectives 273
13.2.2 Research Design 273
13.2.3 Data Collection 273
13.3 Factors Behind the Digitalization of Financial Services
During the COVID Pandemic 273
13.4 Challenges/Barriers for FinTech 279
13.5 Advantages and Disadvantages of Market Structure
Modifications Towards the Digitalization of FinTech
Services 280
13.5.1 Advantages 281
13.5.2 Disadvantages 282
13.6 Conclusion 283
References 285
14 Emerging Trends and Future Directions in Artificial
Intelligence for Next-Generation Computing 289
Rafael Vargas-Bernal
14.1 Introduction 290
14.2 Concepts of Neuromorphic Computing,
Artificial Intelligence, and Memristor 291
14.3 Advantages of Two-Dimensional Materials Used in
Neuromorphic Computing 294
14.4 Devices Implemented with Two-Dimensional Materials
to Evolve Artificial Intelligence 298
14.5 Future Research Directions 308
14.6 Summary 309
Acknowledgments 310
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