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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Fintech in a Sustainable Digital Society
- Sprache: Englisch
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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Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 The Role of Autonomous Finance in the Era of Automatic Civilization
1.1 Introduction
1.2 The Concept of 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
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
2.5 Conclusion
References
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
3.4 Using Algorithms to Make Decisions Can be Biased
3.5 Ensuring Fairness in Autonomous Finance
3.6 Compliance with Regulations in the Field of Autonomous Finance
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
References
4 Involvement of Artificial Intelligence in Emerging Fintech Industry 4.0: A TCCM Framework
4.1 Introduction
4.2 Data and Methodology
4.3 Results and Discussion
4.4 Finding, Conclusion, and Research Directions
4.5 Summary
References
5 Robotic Process Automation in the Financial Sector
5.1 Introduction
5.2 How are Financial Institutions Making Use of Robotics and 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
6.1 Introduction
6.2 Historical Background of Fintech
6.3 Advantages of Fintech
6.4 Role of Data Science and AI
6.5 Data Science and AI (DSAI) Making Smart Fintech
6.6 Use Cases of Data Science in Fintech
6.7 Conclusion
References
7 Evaluation of Fintech: The Techno-Functional Application in Digital Banking
7.1 Introduction
7.2 Overview of Fintech
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
8.1 Introduction
8.2 Thriving in the Tech Age: How Businesses Adapt to Emerging Technologies
8.3 The Future of Work and Innovation: Emerging Technologies Transforming Businesses
8.4 Major Emerging Technologies in Finance
8.5 Risk Associated with Emerging Technologies
8.6 Conclusion
References
9 AI and ML Modeling and Autonomous Finance in Microfinance: An Overview
9.1 Understanding Autonomous Finance and Microfinance
9.2 Readiness of MFIs for Autonomous Finance Transformation
9.3 Solution Drivers in the Life Cycle Journey of an MFI Customer
9.4 Readiness of MFIs for Autonomous Finance Operations
9.5 Technology and AI and ML Enablers of Autonomous Finance for MFIs
9.6 Critical Business Needs of Autonomous Finance
9.7 AI and ML Analytical Models for MFIs
9.8 Overall Deployment and Suitability
9.9 Roadmap for Autonomous Finance in MFIs
9.10 Stage-1: Operation Moonwalk
9.11 Stage 2—Operation Sun Shine
9.12 Stage 3 Operation Bloomsdale
9.13 Improvement Opportunities of Autonomous Finance for MFIs
9.14 Embracing Future AI Agents and Robotics of Autonomous Finance
References
10 Application of Machine Learning Models in the Field of Autonomous Finance
10.1 Overview
10.2 Introduction
10.3 Reinforcement Learning
10.4 Neural Network Basics
10.5 Management of Information for Credit Risk
10.6 Sentiment Analysis with Data Mining Approach
10.7 Conclusion
References
11 Machine Learning Algorithm in Indian Stock Market for Revising and Refining the Equity Valuation Models
11.1 Introduction
11.2 Objectives of the Study
11.3 Methodology
11.4 Review of Literature
11.5 Machine Learning for Equity Valuation Models
11.6 Architecture of Refined Equity Models
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
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
12.8 Banking and Finance Applications
12.9 Conclusions
References
13 Pre- and Post-COVID Autonomous Finance: Global Perspective
13.1 Introduction
13.2 Literature Review
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
13.6 Conclusion
References
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
End User License Agreement
List of Tables
Chapter 11
Table 11.1 List of Sensex stocks.
Table 11.2 RMSE comparison of revised and refined equity models.
Chapter 12
Table 12.1 RPA vendors: client market and source of revenue.
Chapter 14
Table 14.1 Materials for the different types of neuromorphic devices operating...
Table 14.2 Ideal parameters for a memristor based on two-dimensional materials...
List of Illustrations
Chapter 1
Figure 1.1 Finance transformation journey. (Source: Authors’ own compilation)....
Figure 1.2 Benefits of autonomous finance. (Source: Authors’ own compilation)....
Figure 1.3 Advantages of autonomous finance. (Source: Authors’ own compilation...
Figure 1.4 Challenges of autonomous finance. (Source: Authors’ own compilation...
Figure 1.5 Prospects or opportunities of autonomous finance. (Source: Authors’...
Figure 1.6 How autonomous finance works in financial system. (Source: Authors’...
Figure 1.7 Technologies associated with autonomous finance. (Source: Authors’ ...
Chapter 2
Figure 2.1 Brief workflow of investment analysis.
Figure 2.2 MetaTrader 4.
Figure 2.3 MotiveWave.
Figure 2.4 Spider tool.
Figure 2.5 Investar tool.
Figure 2.6 eSignal tool.
Figure 2.7 Sharekhan Trade Tiger tool.
Figure 2.8 NinjaTrader tool.
Figure 2.9 AmiBroker India.
Figure 2.10 VectorVest tool.
Figure 2.11 Profit Source tool.
Figure 2.12 AlgoTrader tool.
Figure 2.13 Deep learning experimentation for Apple and Microsoft Company hist...
Chapter 4
Figure 4.1 AI inclusion in Fintech. Source: Author’s illustration.
Figure 4.2 Summarization of data. Source: Authors’ illustration.
Figure 4.3 Most relevant sources. Source: Biblioshiny output.
Figure 4.4 Most relevant authors. Source: Biblioshiny output.
Figure 4.5 Most relevant affiliations. Source: Biblioshiny output.
Figure 4.6 Country scientific production. Source: Biblioshiny output.
Figure 4.7 Most cited countries. Source: Biblioshiny output.
Figure 4.8 Most global cited documents. Source: Biblioshiny output.
Figure 4.9 Thematic map. Source: Biblioshiny output.
Figure 4.10 Factorial analysis. Source: Biblioshiny output.
Figure 4.11 Co-citation network (authors). Source: Biblioshiny output.
Figure 4.12 TCCM framework. Source: Author’s Illustration.
Chapter 6
Figure 6.1 History of Fintech [3].
Figure 6.2 Data science structure [15].
Figure 6.3 Quantum of data at internet [4].
Figure 6.4 Integration of DS and variety of fields [6].
Figure 6.5 Fintech ecosystem (DSAI) [22].
Figure 6.6 Risk analysis and prevention of fraud.
Figure 6.7 Risk analysis and DS [29].
Figure 6.8 Application of DS in consumer behavior [30].
Figure 6.9 Analysis of credit allocation by banks [30].
Chapter 7
Figure 7.1 Investment of Fintech companies (2010 to 2022).
Figure 7.2 C++ code for client portfolio management based on basic inquiry and...
Figure 7.3 RF-GA-DNN algorithm workflow.
Figure 7.4 Framework combining Fintech and innovation for improvising digital ...
Figure 7.5 Advantages of using Fintech by banking users.
Chapter 8
Figure 8.1 Industry 4.0 bar value drivers.
Chapter 9
Figure 9.1 Life cycle journey of an MFI customer. Source: Authors.
Figure 9.2 Life cycle journey and analytical drivers. Source: Authors.
Figure 9.3 Tech solutions and outcomes.
Figure 9.4 Stages of autonomous finance maturity. Source: Authors.
Figure 9.5 Survey on readiness of autonomous finance. Source: Gartner.
Figure 9.6 Use cases of receivables.
Figure 9.7 Use cases of treasury.
Figure 9.8 Use cases of accounting.
Figure 9.9 Credit score range and remarks. Source: Authors.
Figure 9.10 Comparison of the performance of analytical models.
Figure 9.11 Stages of roadmap. Source: Authors.
Figure 9.12 CXO specific key result indicators. Source: Authors.
Figure 9.13 McKinsey’s straw man modules. Source: Authors and McKinsey.
Chapter 10
Figure 10.1 Collaboration connecting the agent and environment in an MDP.
Figure 10.2 At a point of state during tic-tac-toe gameplay, actions possible.
Figure 10.3 Showing a movable pawn piece on a chessboard.
Figure 10.4 The figure shows a conceptual flow of varied fields from the RL br...
Figure 10.5 The figure above shows how a neural network is structured.
Figure 10.6 Figure shows the complexity of a typical neural network.
Chapter 11
Figure 11.1 Steps used in the multiple linear regression machine learning mode...
Figure 11.2 Architecture of refined P/E model using multiple regression machin...
Figure 11.3 Architecture of refined P/B model using multiple regression machin...
Figure 11.4 Architecture of refined capital asset pricing model using multiple...
Figure 11.5 Comparison of select three revised and refined equity valuation mo...
Figure 11.6 RMSE of the P/E model and refined PE model.
Figure 11.7 RMSE of the P/B model and refined PB models.
Figure 11.8 RMSE of the CAPM and refined CAPM model.
Figure 11.9 Plot of the refined P/E, P/B, and CAPM models for Sensex stocks in...
Chapter 12
Figure 12.1 Path to hyper automation. Source: https://appian.com/resources/lea...
Figure 12.2 Could hyper automation be a name for AI plus RPA? Source: https://...
Figure 12.3 Sophistication of the automation. Source: https://www.xcubelabs.co...
Figure 12.4 Hyper automation process flow (document-based process). Source: AI...
Figure 12.5 Ecosystem of hyper automation technologies. Source: https://search...
Chapter 14
Figure 14.1 Basic operating principle of a synapse between neurons.
Figure 14.2 Two-dimensional materials for neuromorphic devices.
Figure 14.3 Cross-section through a lateral-operated resistive random-access m...
Figure 14.4 Cross-section of a vertical/lateral synaptic device using two-dime...
Figure 14.5 Memristor based on light modulation using phosphorene.
Figure 14.6 A crossbar based on memristors.
Figure 14.7 Cross-section of an MXene-based memristor used in a low-power syna...
Figure 14.8 Main sectors of memristor innovation.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Computational Intelligence for Autonomous Finance
Edited by
Deepa Gupta
GL Bajaj Institute of Management, India
Mukul Gupta
GL Bajaj Institute of Management, India
Rajesh Kumar Dhanaraj
Symbiosis International (Deemed University), India
Balamurugan Balusamy
Shivnadar University, Greater Noida, UP, India
and
Parth Mukul Gupta
Birla Institute of Technology & Sciences, Pilani, Rajasthan, India
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-23322-9
Front cover image courtesy of Adobe FireflyCover design by Russell Richardson
Preface
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 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 micro-finance, 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.
The editors are grateful to everyone who has contributed to improving the quality of the book through their constructive comments. We also thank Martin Scrivener and Scrivener Publishing for their support and publication.
1The 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 taxation rules more easily. Financial executives may benefit from automating all their processes. High-value pursuits like cultivating connections with clients and investors have more time allocated to them. Finance executives also benefit from streamlined international growth, improved compliance with regulatory standards, and simplified administration of tax policies.
1.2.4 What is the Process of Autonomous Finance (How Does it Work)?
You may be curious about the inner workings of autonomous finance and the advantages it offers. The term “autonomous finance” (or “auto finance”) refers to a specific type of technology that uses AI and ML to handle financial transactions without any human involvement [10].
Customers’ purchasing habits, payment histories, and other financial data are analyzed to determine the best way to manage their money, which is how auto financing works. Budgeting, investing, and debt repayment can all be automated with this newfound knowledge. In car financing, several algorithms are employed to improve the customer experience by providing each client with personalized recommendations on how to best manage their finances.
1.2.5 Advantages of Autonomous Finance
Online budgeting software:
One can quickly create a budget with auto finance that considers income and expenses. Rules may be automated for anything from saving to paying off debt [
12
].
Prudent financial decisions:
Autonomous finance takes the guesswork out of investing by automatically assessing the market and ensuring that the portfolio is diversified and risk-appropriate [
11
].
Available in any location:
Customers may log in to their auto finance accounts whenever they choose, from any device. This makes keeping track of one’s money simpler than ever [
2
].
Figure 1.3 Advantages of autonomous finance.
(Source: Authors’ own compilation).
Adapted from [12].
Due to its ability to improve digital banking’s transparency, convenience, and client financial decision-making, autonomous finance is swiftly becoming a major player in the industry.
1.2.6 Challenges Associated with Autonomous Finance
There are several issues to consider when it comes to autonomous finance. The reliability of automated systems depends on the accuracy and completeness of the data they use to make decisions. There is also the possibility that people will not trust algorithmic decision-making because they do not understand it [6].
Additionally, there is the problem of who should be held accountable when an autonomous system makes a mistake. Is it the developers? Those responsible for implementing it? Or something completely different? These are crucial issues that must be resolved for the safe and ethical use of these systems [8].
Although there are many unresolved issues regarding autonomous finance, many of them will likely be addressed as technology improves. We can get closer to an automated and easily controlled “automatic civilization” if we establish clear norms and standards now. Financial procedures that are straightforward, effective, and efficient are always preferred over more cumbersome and outdated alternatives [12]. While automation helps in practically all repetitive tasks, some businesses are still hesitant to adopt new technologies for various reasons. The various challenges of autonomous finance are as follows:
Figure 1.4 Challenges of autonomous finance.
(Source: Authors’ own compilation).
Adapted from [12].
Complex Software Operations
Companies that have been handling their finances entirely manually for years may struggle to learn the intricacies of a newly implemented automated system. One of the most prevalent reasons businesses hesitate to adopt automation is a lack of staff with the necessary technical expertise to use the software [
13
].
To utilize the automated software, employees may need to devote time to learning how the various financial procedures are implemented. The inability to fully automate tasks due to the learning curve of the software is a significant barrier [9].
Capital Budgeting
Another factor that keeps businesses from switching to automated financial processes is the high cost of making the transition. Decision-makers often worry about spending money on subpar software. There is no way to know in advance which software will integrate seamlessly with their existing manual procedures [
14
]. It is challenging for company executives to fully deploy automated financial processes since most organisations want their procedures to be repeatable, but not all software providers can achieve this [
3
].
In the end, a profitable return on investment is the goal of any business. The financial implications of automating processes are a primary concern. To convince key decision-makers that the substantial capital investment required to automate the process is worthwhile, businesses must outline the specific ways in which automation might improve their operations [15].
Pick the Best Option
Either a stand-alone RPA system or intelligent automation (a mix of AI and ML) can help finance executives transform their financial operations and add a greater degree of refinement to their end-to-end processes [
6
]. In addition to facilitating process improvement, autonomous finance is essential for maintaining a competitive edge in the market.
Choosing the right automation solution can make short work of the project. When processes are automated, they become more efficient, profitable, reliable, economical, and transparent.
Financial managers need to stop resisting change and start automating routine back-office financial tasks if they want to free up staff time for more strategic work.
Safety
Another major issue with autonomous banking is safety, as there is a higher risk of theft or hacker involvement as these systems become more complex and operate in multiple locations. If suitable measures are not in place, this could result in monetary losses or data breaches.
1.3 Autonomous Finance: Prospects and Developments
One needs to be on the leading edge to realize the full potential of autonomous finance, which is a whole new field. The possibilities are endless, and so are the changes in trends.
Blockchain technology is important for open financial systems because it acts as a global ledger that records transactions securely and permanently [16]. When this technology is used, central banks and other regulatory bodies do not need to get involved with autonomous banking [17–23]. Blockchain not only makes sharing resources more efficient and secure, but it also opens new ways for companies to collaborate [24].
Decentralized financing (DeFi) is an concept that has become increasingly popular. It is different from traditional centralized banking. By allowing a network of users to determine the amount of money and price of assets [25], DeFi eliminates the need for central bank or government intervention. This results in a decentralized banking system that is more efficient and stable than previous systems [26–29].
Artificial intelligence (AI) is important for autonomous finance because it allows data to be processed in real time. This tool helps detect fraudulent behavior and high-risk investments [30+]. AI also helps automate the insights needed to make smart investment decisions [34–38] by using smart contracts that execute predetermined actions.
Figure 1.5 Prospects or opportunities of autonomous finance.
(Source: Authors’ own compilation).
Adapted from [16].
With blockchain and AI working together, autonomous finance is an exciting new area for businesses that want to cut costs and boost productivity [34, 39, 44]. As we approach the era of autonomous financing, also known as the “Automatic Civilization,” banks need to prepare for significant changes. By eliminating the need for people interaction, automation speeds up tasks that used to take a long time to complete [31–33]. It is expected that markets will become fully automated as blockchain technology and AI continue to improve. Smart contracts will enable fully automated trades [6].
Because of this shift towards autonomous finance, the financial system has become more efficient and transparent. Decentralized autonomous finance provides clarity in decision-making, allowing investors to actively manage their investments and make informed choices [7, 8, 12, 13]. As a result of these changes, the banking industry needs to embrace the opportunity for greater efficiency and transparency that comes with blockchain and AI working together in autonomous finance. Autonomous finance also enables new types of collaboration, such working together on larger projects or developing new financial products that leverage the expertise of other markets. Furthermore, trust is enhanced on both sides by the immutability and traceability provided by autonomous finance.
As the financial services industry embraces new technologies, autonomous finance will continue to transform the landscape. As more people learn about the potential of autonomous finance, its importance will grow.
Bank customers are better served by financial organizations that use autonomous finance, as it streamlines and automates processes.
What kinds of tasks within the financial system should be prioritized for automation?
Finance is responsible for a wide variety of tasks, most of which have impact a company’s bottom line. Here are some of the most important financial procedures in a company.
Accounts Payable
A company’s obligation to pay its suppliers for products and services received on credit is recorded as accounts payable (AP) [
45
]. Sales orders are received, reviewed, reconciled, routed for approval, conditions are negotiated, payments are processed, and suppliers are paid on time [
46
]. Late payments cost money and might strain your relationship with suppliers.
Accounts Receivable
Customer payments that have not yet been received are known as accounts receivables. Invoicing clients, following up on overdue payments, and timely closing of open accounts are all aspects of managing accounts receivable [
47
]. Avoid disrupting your company’s cash flow by diligently tracking accounts receivable and collecting payments on time. In most businesses, a DSO of fewer than 45 days indicates a strong sales pipeline.
Account Reconciliation
The month-end and year-end closes are not the only times that account reconciliations are undertaken; they are just the most prominent. Accounts in the general ledger are cross-referenced with information from subsidiary ledgers and external sources, such as bank statements and other evidence of underlying transactions [
6
]. Accountants investigate the reasons for any inconsistencies and make adjustments to reflect any missing or incorrect transactions.
Figure 1.6 How autonomous finance works in financial system.
(Source: Authors’ own compilation).
Adapted from [24].
Finance managers should emphasize automating reconciliation due to its impact on a company’s bottom line. Let us analyze the CAGR for each type of automation solution used in the finance industry.
Forecasts put the accounts payable automation market’s value will reach $7.5 billion by 2030, up from $2.6 billion in 2021.
The receivables automation industry is projected to expand from its current value of $3.3 billion at a compound annual growth rate (CAGR) of 12.1% between 2022 and 2027.
In 2019, analysts predicted that the global account reconciliation software market will be worth $1.82 billion. By 2027, that number is expected to increase to $5.38 billion, a CAGR of 14.6%.
As the above numbers show, the three financial procedures are rapidly becoming automated. This suggests that your rivals have likely already deployed the appropriate autonomous finance systems.
When should financial automation be prioritized?
Existing procedures and the finance department need to be digitally transformed if any of the following problems are occurring:
Excessive routine work
Too many individuals in everyday activities
Workflow delays
Significant interference with other procedures and systems
Inconsistencies in audit trails and compliance
Reasons why the current financial system could fail?
The traditional method of handling finances relied solely on physical labor. Complete financial management was delegated to accountants, who spent most of their time on low-value activities like sending out invoices and collecting payments [11].
Merging documents like invoices and bank receipts proved difficult, among other mundane tasks. Due to the lack of a centralized data repository, accountants typically had to scour multiple databases to compile the necessary information [40–42].
Furthermore, dependence on several stakeholders slows down procedures that require information or input from various groups. If a key participant is unavailable due to illness or vacation, the entire operation might stall. The risk of failing to meet requirements or deadlines is heightened by these obstacles.
How does autonomous finance use technology?
Listed below are some of the most widespread technologies associated with autonomous finance:
Robotic Process Automation
Automating low-value, high-volume business processes using RPA frees up workers to focus on higher-value tasks [
48
]. It helps businesses boost their return on investment (ROI) and speed up their digital transformation efforts.
Rule-based automation, like RPA, is excellent for eliminating the need for humans to perform routine, monotonous tasks. RPA’s widespread appeal to medium-sized enterprises can be attributed to the adoption of its most common use cases, such as invoicing and cash processing automation [49].
Remittance aggregation is one example of an important process that can be automated by RPA technology. The system uses web bots to automatically retrieve relevant information, instead of relying on human users entering into and obtain customer-uploaded remittance data from online portals. In this scenario, the effort required to obtain remittance information from online systems is greatly reduced.
While robotic process automation (RPA) is certainly ground-breaking, it cannot be the sole focus. Complex situations can be challenging to address using RPA alone. For instance, RPA can parse emails for remittance information, but it cannot verify the data’s accuracy or anticipate and fill in any gaps. Therefore, RPA is best seen as a practical tool.
When RPA bots are given organized data and explicit instructions on how to use it, they perform flawlessly. However, they fail to provide the expected outcomes when processing unstructured input.
Figure 1.7 Technologies associated with autonomous finance.
(Source: Authors’ own compilation).
Adapted from [49].
Artificial Intelligence (AI)
Since RPA is not perfect and cannot do everything by itself, AI is necessary [
50
–
54
]. When RPA is used to automate mundane tasks, artificial intelligence can give bots the appearance of human intelligence, allowing for faster data extraction and more informed business choices [
49
,
55
–
57
].
By preventing the bots from breaking when the underlying rules on other websites change, AI increases the efficacy of RPA [58]. Predictive analysis and identifying trends in previous data are two more areas where AI becomes useful for locating the facts you need to make educated decisions [43, 44, 59–66]. The most advanced RPA software applications use AI to improve exception handling. For instance, they may foresee gaps in remittances [67].
Machine Learning (ML)
By using machine learning technology, automated financial solutions can make more precise predictions with little to no human input [
68
]. Machine learning algorithms use previously collected data to make predictions about the future [
69
–
72
].
ML enables proactive collections in many of the best financial automation applications. Collectors may anticipate future payment dates and be more proactive with dunning by analyzing historical payment data [73].
1.4 Key Considerations for Implementing Autonomous Finance
Investing in AI technology to create an autonomous financial structure to recession-proof your firm requires you to first ensure that your existing IT infrastructure can handle the additional load. To explain how:
Storage, Networks, and Computing Infrastructure
Artificial intelligence (AI) and deep learning (DL) techniques require much more computer power, as they rely on massive data sets and scalable neural networks. Invest in AI and NLP only after you have worked with reputable service providers to improve your IT infrastructure.
ProTip
Get rid of any slow, inflexible, and unscalable outdated systems, and replace them with newer, more capable ones.
Safeguarding Information Systems
Big data fuels AI systems. It is important to use correct and trustworthy data when training AI models. Also, ensure that any personal or financial data used by AI products is encrypted.
1.5 Conclusion
The concept of “autonomous finance” is on the rise, and it might completely alter the financial industry as we know it. It promises to automate financial decision-making, giving individuals who have not had access to financial services before a chance to do so, and giving those who have more control and transparency over their finances. It has the potential to revolutionize the delivery of financial services thanks to blockchain technology.
The field of autonomous finance is in its infancy, and many problems remain unanswered. However, it is evident that this technology has the capacity to usher in a new age of “automated civilization” and radically alter the way we approach financial matters. It is time to investigate how this innovation might improve our economic future.
References
1. Vieira, P.C. and Teixeira, A.A.C., Are finance, management, and marketing autonomous fields of scientific research? An analysis based on journal citations.
Scientometrics
, 85, 3, 627–646, 2010.
2. Verhoest, K., Peters, B.G., Bouckaert, G., Verschuere, B., The study of organisational autonomy: A conceptual review.
Public Adm. Dev
., 24, 2, 101–118, 2004.
3. Clark, B.Y., The impacts of autonomous vehicles on local government budgeting and finance: Case of solid waste collection.
Natl. Tax J
., 73, 1, 259, 2020.
4. Oulasvirta, L. and Turala, M., Financial autonomy and consistency of central government policy towards local governments.
Int. Rev. Adm. Sci
., 75, 2, 311–332, 2009.
5. Wang, S., Money and autonomy: Patterns of civil society finance and their implications.
Stud. Comp. Int. Dev
., 40, 4, 3–29, 2006.
6. Kristof, A.,
Autonomous Finance
, 1st ed., vol. 2, Elsevier Inc, Amsterdam, 2018.
7. Ashcroft, R.E., Personal financial incentives in health promotion: Where do they fit in an ethic of autonomy?
Health Expect
., 14, 2, 191–200, 2011.
8. Harwick, C. and Caton, J., What’s holding back blockchain finance? On the possibility of decentralized autonomous finance.
Q. Rev. Econ. Financ
., 84, 420–429, 2022.
9. Adler, M.W., Peer, S., Sinozic, T., Autonomous, connected, electric shared vehicles (ACES) and public finance: An explorative analysis.
Transp. Res. Interdiscip. Perspect
., 2, 100038, 2019.
10. Yusuf, A., Qureshi, S., Feroz Shah, S., Mathematical analysis for an autonomous financial dynamical system via classical and modern fractional operators.
Chaos Solitons Fractals
, 132, 23–27, 2020.
11. Kumar, P., Pillai, R., Kumar, N., Tabash, M., II, The interplay of skills, digital financial literacy, capability, and autonomy in financial decision making and well-being.
Borsa Istanb. Rev
., 23, 1, 169–183, 2023.
12. Ege, J. and Bauer, M.W., How Financial Resources Affect the Autonomy of International Public Administrations.
Glob. Policy
, 8, 75–84, August 2017.
13. Devaro, J., Teams, autonomy, and the financial performance of firms.
Ind. Relat. (Berkeley)
, 45, 2, 217–269, 2006.
14. Denison, C.A., Real Options and Escalation of Commitment: A Behavioral Analysis of Capital Investment Decisions.
Account. Rev
., 84, 1, 133–155, Jan. 2009.
15. Fulmer, I.S. and Ployhart, R.E., Our Most Important Asset: A Multidisciplinary/ Multilevel Review of Human Capital Valuation for Research and Practice.
J. Manag
., 40, 1, 161–192, 2014.
16. Anjum, A., Sporny, M., Sill, A., Blockchain Standards for Compliance and Trust.
IEEE Cloud Comput
., 4, 4, 84–90, 2017.
17. Beninger, P. and Ibara, M.A., Pharmacovigilance and Biomedical Informatics: A Model for Future Development.
Clin. Ther
., 38, 12, 2514–2525, 2016.
18. Brunese, L., Mercaldo, F., Reginelli, A., Santone, A., A blockchain based proposal for protecting healthcare systems through formal methods.
Procedia Comput. Sci
., 159, 1787–1794, 2019.
19. Tariq, N., Qamar, A., Asim, M., Khan, F.A., Blockchain and smart healthcare security: A survey.
Procedia Comput. Sci
., 175, 615–620, 2020.
20. Al-Janabi, S., Al-Shourbaji, I., Shojafar, M., Shamshirband, S., Survey of main challenges (security and privacy) in wireless body area networks for health-care applications.
Egypt. Inf. J
., 18, 2, 113–122, 2017.
21. Zhang, J., Xue, N., Huang, X., A Secure System for Pervasive Social Network-Based Healthcare.
IEEE Access
, 4, c, 9239–9250, 2016.
22. Shi, S., He, D., Li, L., Kumar, N., Khurram, M., Applications of blockchain in ensuring the security and privacy of electronic health record systems: A survey Shuyun.
Comput. Secur
., 1, 1–20, January 2020.
23. Cai, Y. and Zhu, D., Fraud detections for online businesses: a perspective from blockchain technology.
Financ. Innov
., 2, 1, 132–139, 2016.
24. Chukwu, E. and Garg, L., A systematic review of blockchain in healthcare: Frameworks, prototypes, and implementations.
IEEE Access
, 8, 21196–21214, 2020.
25. Abimbola, S., Baatiema, L., Bigdeli, M., The impacts of decentralization on health system equity, efficiency and resilience: A realist synthesis of the evidence.
Health Policy Plan
., 34, 8, 605–617, 2019.
26. Schär, F., Decentralized finance: on blockchain-and smart contract-based financial markets.
Fed. Reserve Bank St. Louis Rev
., 103, 2, 153–174, 2021.
27. Kumar, V. and Jayanta Mishra, A., Healthcare under the Panchayati Raj Institutions (PRIs) in a decentralised health system: Experiences from Hardoi district of India.
Leadersh. Heal. Serv
., 29, 2, 151–167, 2016.
28. Zarrin, J., Wen Phang, H., Babu Saheer, L., Zarrin, B., Blockchain for decentralization of internet: prospects, trends, and challenges.
Cluster Comput
., 24, 4, 2841–2866, 2021.
29. Mahal, A. and Rajaraman, I., Decentralisation, preference diversity and public spending: Health and education in India.
Econ. Polit. Wkly
., 45, 43, 57–63, 2010.
30. Madhavi, Role of AI in business development.
Open J. Soc. Sci
., 6, 6, 28–33, 2021.
31. Rathi, A. and Asava, T., The role of artificial intelligence in disinformation.
Data Policy
, 3, 01, 175–179, 2021.
32. Palanivelu, V.R. and Vasanthi, B., Role of artificial intelligence in business transformation.
Int. J. Adv. Sci. Technol
., 29, 4 Special Issue, 392–400, 2020.
33. Roundy, P.T., Artificial intelligence and entrepreneurial ecosystems: understanding the implications of algorithmic decision-making for startup communities.
J. Ethics Entrep. Technol
., 2, 1, 23–38, 2022.
34. Soni, N., Sharma, E.K., Singh, N., Kapoor, A., Artificial Intelligence in Business: From Research and Innovation to Market Deployment.
Procedia Comput. Sci
., 167, 2200–2210, 2020.
35. Mishra, S. and Tripathi, A.R., AI business model: an integrative business approach.
J. Innov. Entrep
., 10, 1, 1–19, 2021.
36. Anute, N., Paliwal, M., Patel, M., Kandale, N., Impact of artificial intelligence and machine learning on business operations.
J. Manage. Res. Anal
., 8, 2, 69–74, 2021.
37. Jain, V., Impact of Artificial Intelligence on Business.
Electron. J. Bus. Ethics Organ. Stud
., 24, 2, 302–308, 2019.
38. Enholm, I.M., Papagiannidis, E., Mikalef, P., Krogstie, J., Artificial intelligence and glaucoma: A literature review.
J. Fr. Ophtalmol
., 45, 2, 216–232, 2022.
39. Leitch, R., Artificial intelligence in engineering.
Comput. Control Eng. J
., 3, 4, 152–157, 2021.
40. Chalmers, D., MacKenzie, N.G., Carter, S., Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution.
Entrep. Theory Pract
., 45, 5, 1028–1053, 2021.
41. Sadiku, M.N.O., Fagbohungbe, O., Musa, S.M., Artificial Intelligence in Business.
Int. J. Eng. Res. Adv. Technol
., 06, 07, 62–70, 2020.
42. Geisel, A., The current and future impact of artificial intelligence on business.
Int. J. Sci. Technol. Res
., 7, 5, 116–122, 2018.
43. Yoopetch, C., Nimsai, S., Kongarchapatara, B., Bibliometric Analysis of Corporate Social Responsibility in Tourism.
Sustainability
, 15, 1, 56–59, 2023.
44. Soni, N., Sharma, E.K., Singh, N., Kapoor, A., Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models.
Open J. Bus. Manage
., 6, 2, 1–38, 2019.
45. Demirguc-Kunt, A., Klapper, L., Singer, D.,
Financial Inclusion and Inclusive Growth: A Review of Recent Empirical Evidence
, World Bank Group, USA, April 2017.
46. Hada, D.B.S., Impact of Internet Banking on the Customer Satisfaction: Evidence from the Indian Banking Sector.
Int. J. Recent Technol. Eng
., 8, 6, 3833–3841, 2020.
47. Xiong, H., Dalhaus, T., Wang, P., Huang, J., Blockchain Technology for Agriculture: Applications and Rationale.
Front. Blockchain
, 3, 1–7, February 2020.
48. Dirican, C., The Impacts of Robotics, Artificial Intelligence On Business and Economics.
Procedia Soc. Behav. Sci
., 195, 564–573, 2015.
49. Turkina, E., The importance of networking to entrepreneurship: Montreal’s artificial intelligence cluster and its born-global firm Element AI.
J. Small Bus. Entrep
., 30, 1, 1–8, 2018.
50. Bogachov, S., Kwilinski, A., Miethlich, B., Bartosova, V., Gurnak, A., Artificial intelligence components and fuzzy regulators in entrepreneurship development.
Entrep. Sustain. Issues
, 8, 2, 487–499, 2020.
51. Giuggioli, G. and Pellegrini, M.M., Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research.
Int. J. Entrep. Behav. Res
., 2, 112–123, June 2022.
52. Ahmed, A.A.A. and Ganapathy, A., Creation of Automated Content With Embedded Artificial Intelligence: a Study on Learning Management System for Educational Entrepreneurship.
Acad. Entrep. J
., 27, 3, 1–10, 2021.
53. Miao, Z., The Influence Factors of Psychological Understanding and Behavior Choice for Legal Industry Entrepreneurs Based on Artificial Intelligence Technology.
Front. Psychol
., 11, 1–10, July 2020.
54. Nuseir, M.T., Basheer, M.F., Aljumah, A., Antecedents of entrepreneurial intentions in smart city of Neom Saudi Arabia: Does the entrepreneurial education on artificial intelligence matter?
Cogent Bus. Manage
., 7, 1, 42–47, 2020.
55. Obschonka, M. and Audretsch, D.B., Artificial intelligence and big data in entrepreneurship: a new era has begun.
Small Bus. Econ
., 55, 3, 529–539, 2019.
56. Townsend, D.M. and Hunt, R.A., Entrepreneurial action, creativity, & judgment in the age of artificial intelligence.
J. Bus. Ventur. Insights
, 11, e00126, January 2019.
57. Ughulu, D.J., The importance of AI in starting, automating, and scaling businesses for entrepreneurs Department of Entrepreneurship & Innovation Cornerstone Christian University CCU The role of Artificial intelligence (AI) in Starting, automating and scaling busin.
Open J. Bus. Manage
., 2, 8, 1–7, 2022.
58. Garbuio, M. and Lin, N., Artificial intelligence as a growth engine for health care startups: Emerging business models.
Calif. Manage. Rev
., 61, 2, 59–83, 2018.
59. Al-Dalahmeh, M.L., Talent Management: a Systematic Review.
Oradea J. Bus. Econ
., 5, 115–123, 2020.
60. Sareen, P. and Mishra, S., A Study of Talent Management and Its Impact on Performance of Organizations.
Integr. J. Res. Arts Humanit
., 2, 4, 56–59, 2016.
61. Hongal, P. and Kinange, D.U., A Study on Talent Management and its Impact on Organization Performance-An Empirical Review.
Int. J. Eng. Manage. Res
., 10, 01, 64–71, 2020.
62. Sharma, M. and Rathore, B.S., Research Paper on Exploring Talent Management Practices: Antecedents and Consequences.
Int. J. Manage
., 11, 12, 1332–1363, 2020.
63. Nwanisobi, B.C. and Christopher, I.C., Talent Management: A Conceptual Framework with Practical Approach Talent Management: A Conceptual Framework with Practical Approach.
Open J. Bus. Manage
., 7, 1–10, March 2020.
64. Liu, M., An Empirical Study on Talent Management Strategies of Knowledge-Based Organizations Using Entrepreneurial Psychology and Key Competence.
Front. Psychol
., 12, 1–10, October 2021.
65. Pestonjee, D.M., Barot, H., Chhaniwal, P., A Study On Talent Management Practices In India.
Nveo Nat. Volatiles Essent. Oils
, 5, 1, 1–8, 2017.
66. Manthena, S.R.L., Talent Management: Employee Performance and impact on Organizational Performance.
Open J. Bus. Manage
., 8, 8, 226–229, 2022.
67. (Kevin) Chae, B. and Goh, G., Digital entrepreneurs in artificial intelligence and data analytics: Who are they?
J. Open Innov. Technol. Mark. Complex
., 6, 3, 21–38, 2020.
68. Huntingford, C., Jeffers, E.S., Bonsall, M.B., Christensen, H.M., Lees, T., Yang, H., Machine learning and artificial intelligence to aid climate change research and preparedness.
Environ. Res. Lett
., 14, 12, 6–9, 2019.
69. Vollmer, S.
et al
., Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness.
BMJ
, 368, 1–12, 2020.
70. Stocker, S., Csányi, G., Reuter, K., Margraf, J.T., Machine learning in chemical reaction space.
Nat. Commun
., 11, 1, 1–11, 2020.
71. Pathak, Y., Juneja, K.S., Varma, G., Ehara, M., Priyakumar, U.D., Deep learning enabled inorganic material generator.
Phys. Chem. Chem. Phys
., 22, 46, 26935–26943, 2020.
72. Wetterich, C.B., Felipe de Oliveira Neves, R., Belasque, J., Ehsani, R., Marcassa, L.G., Detection of Huanglongbing in Florida using fluorescence imaging spectroscopy and machine-learning methods.
Appl. Opt
., 56, 1, 15, 2017.
