
Computational Intelligence for Autonomous Finance
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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.
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.
Audience This book is aimed at researchers, industry professionals, policymakers, and graduate students in finance, computational intelligence, and related fields.
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Persons
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.
Content
Preface xvii
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
1.3 Autonomous Finance: Prospects and Developments 9
1.4 Key Considerations for Implementing Autonomous Finance 14
1.5 Conclusion 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
2.5 Conclusion 39
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
3.4 Using Algorithms to Make Decisions Can be Biased 49
3.5 Ensuring Fairness in Autonomous Finance 50
3.6 Compliance with Regulations in the Field of Autonomous Finance 51
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
4 Involvement of Artificial Intelligence in Emerging Fintech Industry 4.0: A TCCM Framework 65
Annu and Ravindra Tripathi
4.1 Introduction 66
4.2 Data and Methodology 69
4.3 Results and Discussion 70
4.4 Finding, Conclusion, and Research Directions 76
4.5 Summary 77
5 Robotic Process Automation in the Financial Sector 81
Neha Sonik, Deepa Gupta and Parul Gupta
5.1 Introduction 81
5.2 How are Financial Institutions Making Use of Robotics and Automation? 83
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
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
6.3 Advantages of Fintech 104
6.4 Role of Data Science and AI 108
6.5 Data Science and AI (DSAI) Making Smart Fintech 111
6.6 Use Cases of Data Science in Fintech 116
6.7 Conclusion 119
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
7.3 Theoretical Overview 136
7.4 Measurement of the Success Factor of Fintech in Digital Banking 137
7.5 Summary 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
8.2 Thriving in the Tech Age: How Businesses Adapt to Emerging Technologies 148
8.3 The Future of Work and Innovation: Emerging Technologies Transforming Businesses 151
8.4 Major Emerging Technologies in Finance 159
8.5 Risk Associated with Emerging Technologies 164
8.6 Conclusion 165
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
9.2 Readiness of MFIs for Autonomous Finance Transformation 170
9.3 Solution Drivers in the Life Cycle Journey of an MFI Customer 173
9.4 Readiness of MFIs for Autonomous Finance Operations 177
9.5 Technology and AI and ML Enablers of Autonomous Finance for MFIs 180
9.6 Critical Business Needs of Autonomous Finance 182
9.7 AI and ML Analytical Models for MFIs 182
9.8 Overall Deployment and Suitability 188
9.9 Roadmap for Autonomous Finance in MFIs 188
9.10 Stage-1: Operation Moonwalk 190
9.11 Stage 2-Operation Sun Shine 192
9.12 Stage 3 Operation Bloomsdale 193
9.13 Improvement Opportunities of Autonomous Finance for MFIs 193
9.14 Embracing Future AI Agents and Robotics of Autonomous Finance 195
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
10.3 Reinforcement Learning 203
10.4 Neural Network Basics 209
10.5 Management of Information for Credit Risk 213
10.6 Sentiment Analysis with Data Mining Approach 215
10.7 Conclusion 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
11.2 Objectives of the Study 223
11.3 Methodology 223
11.4 Review of Literature 225
11.5 Machine Learning for Equity Valuation Models 228
11.6 Architecture of Refined Equity Models 232
11.7 Analysis-Checking the Valuation Accuracy of Revised and Refined Models Using Machine Learning Approach 234
11.8 Conclusion 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
12.8 Banking and Finance Applications 260
12.9 Conclusions 264
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
13.3 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
13.6 Conclusion 283
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
References 310
Index 313
1
The Role of Autonomous Finance in the Era of Automatic Civilization
Sanjeet Singh1*, Geetika Madaan2 and Jaskiran Kaur3
1Research Centre and Faculty of Management Studies, Marwadi University, Rajkot, Gujrat, India
2Research Centre, Marwadi University, Rajkot, Gujrat, India
3Division of Academic Affairs, Lovely Professional University, Phagwara, India
Abstract
Financial and accounting process management can be demanding in terms of both time and mental energy. Manually performing tasks like data mining and discovering financial facts takes a lot of time and effort. Long hours spent staring at spreadsheets and databases may cause accountants to experience mental fatigue. When automation can simplify the lives of your financial team members, it makes no sense to have them waste their time with inefficient manual operations. If we or our team are still manually copying and pasting information across Excel spreadsheets, then we need to get out of the 1990s as quickly as possible. We have officially entered the era of "automatic civilization." Instead of spending time on commonplace, repetitive chores, organizations can streamline their operations by integrating data-driven insights and cutting-edge analytics into their autonomous financial systems. In the chapter, author aims to uncover the role of autonomous finance in the era of automatic civilization. The term "autonomous finance" refers to a method of handling financial transactions and management based on data or algorithms. Due to the fast-paced and competitive nature of the modern corporate world, innovative strategies have surpassed conventional ones. Enterprises need to be nimbler and more forward-thinking if they are to stay up with the times and enhance their operations. Automation of financial procedures can save a lot of time and effort, as finance is one of the most complex and time-consuming aspects of any firm.
Keywords: AI, autonomous finance, accounting, machine learning, robotics, process automation
1.1 Introduction
The emergence of "automatic civilization," along with other technological breakthroughs [1], has made a huge difference in the way we deal with money in the real world. One of the most significant developments changing this is autonomous finance, which is transforming how budgets are managed in a big way.
The onset of the "automated civilization" period has brought about a significant change in the way individuals around the world manage their money. Free banking is a crucial component of this change [2]. This new technology makes it is easier for people to keep track of their money, transforming the financial world. For the most part, autonomous finance means letting AI along with other computerized decision-making tools handle your money. Managing money involves various tasks, such as automatically investing in the stock market, managing more complex accounts, and selecting the best ways to handle risk [3-5]. Organizations can use the tools for monitoring data and making market predictions that come with autonomous finance to make better, less risky decisions.
Autonomous finance and other AI-based technologies, such as predictive analytics, can help businesses excel in the present-day data-driven market. Autonomous finance has initiated an entirely new phase in financial management. Computers can now perform complex tasks that previously required human assistance.
1.2 The Concept of Autonomous Finance
Autonomous finance refers to the capability to perform routine monetary tasks with little or no oversight from a human operator. It automates financial procedures using AI and machine learning, leading to greater productivity and precision. Budgeting, investing, and managing risks are all part of this [6].
Forrester defines the term as algorithm-driven financial services that make decisions or take action on a customer's behalf.
In a nutshell, autonomous technology can perform its intended tasks without any assistance from a human operator. These technologies are like us in that they continually improve by taking in new information from their surroundings and applying it to their decision-making [7].
1.2.1 Autonomous Finance: The Technology and Factors Driving Its Widespread Deployment
Cloud computing, RPA, advanced analytics, NLP, and AI are the engines that propel the field of autonomous banking [8]. Finance analysts' productivity is boosted by the seamless two-way communication between humans and computers enabled by the program. Autonomous technologies are more common, with artificial intelligence (AI) being a prime example. Autonomous intelligence, a more advanced kind of AI, can make decisions and act without any human input.
Finance departments often follow the path shown in the following figure to achieve complete autonomy. Some companies have achieved 80% autonomy with AI-assistance, but the vast majority are in the 0-40% range, moving towards the 60% autonomous mark with RPA, AI, and analytics tools [9]. However, we have not yet reached a point when the department is entirely independent.
1.2.2 CFO's Function in Autonomous Finance
The chief financial officer's responsibilities in an autonomous financial system now extend beyond the conventional realm of finance to include managing and optimizing autonomous financial systems [10]. In autonomous finance, the Chief Financial Officer (CFO) plays a crucial role in making decisions about technology investments, monitoring data quality and integrity, and mitigating risks related to automation.
Figure 1.1 Finance transformation journey.
(Source: Authors' own compilation).
Adapted from [10].
CFOs are responsible for managing risks related to data privacy and security, maintaining compliance with legislation, and implementing and integrating new technology into autonomous finance systems. The CFO also needs to work with other division heads to ensure that the company's financial systems are aligned to achieve its overarching goals and strategy [11].
With the rise of autonomous finance, the chief financial officer's responsibilities are shifting from being a mere custodian of financial data to being an active participant in business strategy. The ability to analyze data in real-time is essential for chief financial officers (CFOs) who want to guide their companies towards strategic growth.
1.2.3 Motives to Switch to an Autonomous Finance Structure
With the advent of autonomous finance, future financial services will be safer and more convenient than ever before. It is a revolutionary new approach to wealth management that offers various advantages over the status quo [3-6].
First, autonomous finance boosts productivity. By automating the analysis of transactions and optimization of investments using artificial intelligence and machine learning, you can potentially save time and effort in budgeting.
It uses state-of-the-art data encryption technology to enhance the security of financial activities. This protects your financial information from unauthorized parties and keeps it secure from harm. Finally, autonomous finance allows you to manage your finances whenever and wherever is most convenient with you [11].
Autonomous finance, as a whole, might usher in an era of "automated civilization" with entirely new standards for the security and convenience of personal banking. Keeping up with the competition will be difficult for any business that has not automated its processes to improve efficiency. It is crucial for your business to prepare today so that it can reap the benefits of autonomous finance in the future.
Some of the most important benefits of making financial processes fully automated are as follows:
- Enhanced effectiveness
- Reduce expenses
- Boost output
- Superior Assistance to Clients
- Marketable Distinction
Figure 1.2 Benefits of autonomous finance.
(Source: Authors' own compilation).
Adapted from [11].
- Enhanced effectiveness
Automated solutions expedite operations, free up resources, and reduce human error. - Reduce expenses
Through more efficient use of available resources, autonomous financial technology enables firms to save money in areas like customer service, reporting, and more. - Boost output
Your financial staff will have more time for high-value, strategic work thanks to the automation provided by autonomous finance solutions. - Superior Assistance to Clients
The finance department will have more time to devote to high-value operations and strategic planning thanks to the automation of routine, repetitive work enabled by autonomous finance technology.Eighty-nine percent of financial service executives surveyed by Salesforce agreed that industry leaders who adopt autonomous finance early would establish new standards for client satisfaction.
- Marketable Distinction
CFOs stand to gain an advantage if their operations are fully automated. There is more time to devote to high-value activities such as customer or investor relationship management. Finance leaders can also expand to new markets faster, meet regulatory requirements accurately, and manage...
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