Generative Artificial Intelligence in Finance E-Book
188,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Fintech in a Sustainable Digital Society
- Sprache: Englisch
This comprehensive volume delves deep into the diverse applications and implications of generative AI across accounting, finance, economics, business, and management, providing readers with a holistic understanding of this rapidly evolving landscape.
Generative Artificial Intelligence in Finance: Large Language Models, Interfaces, and Industry Use Cases to Transform Accounting and Finance Processes provides a comprehensive guide to ethically harnessing generative AI systems to reshape financial management. Generative AI is a key theme across the accounting and finance sectors to drive significant optimizations leading to sustainability. Across 22 chapters, leading researchers supply innovative applications of large language models across the economic realm. Through detailed frameworks, real-world case studies, and governance recommendations, this book highlights applied research for generative AI in finance functions. Several chapters demonstrate how data-driven insights from AI systems can optimize complex financial processes to reduce resource usage, lower costs, and drive positive environmental impact over the long term. In addition, chapters on AI-enabled risk assessment, fraud analytics, and regulatory technology highlight applied research for generative AI in finance. The book also explores emerging applications like leveraging blockchain and metaverse interfaces to create generative AI models that can revolutionize areas from carbon credit trading to virtual audits. Overall, with in-depth applied research at the nexus of sustainability and optimization enabled by data science and generative AI, the book offers a compilation of best practices in leveraging AI for optimal, ethical, and future-oriented financial management.
Audience
The audience for this book is quite diverse, ranging from financial and accounting experts across banking, insurance, consultancies, regulatory agencies, and corporations seeking to enhance productivity and efficiency; business leaders want to implement ethical and compliant AI practices; researchers exploring the domain of AI and finance.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 724
Veröffentlichungsjahr: 2025
Ähnliche
Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Part I: Foundations and Applications of AI in Finance
1 Artificial Intelligence Application and Research in Accounting, Finance, Economics, Business, and Management
1.1 Introduction
1.2 Literature Review
1.3 Artificial Intelligence Applications in Accounting
1.4 Artificial Intelligence Applications in Finance
1.5 Artificial Intelligence Applications in Economics
1.6 Artificial Intelligence Applications in Business and Management
1.7 Risks of AI
1.8 Conclusion
References
2 Automating Data Entry in the Indian Banking Industry Through Generative AI
2.1 Introduction
2.2 Literature Review
2.3 Methodology
2.4 Data Entry Automation with Generative AI
2.5 Results and Analysis
2.6 Discussion
2.7 Conclusion
References
3 Future Approach Generative AI, Stylized Architecture, and its Potential in Finance
3.1 Introduction
3.2 Risk Considerations
3.3 Risk Considerations in AI Application
3.4 Significant Challenge
3.5 Generative AI and its Architecture
3.6 Conclusion
References
4 Generative Artificial Intelligence (GAI) for Accurate Financial Forecasting
4.1 Introduction
4.2 Literature Review
4.3 Methodology
4.4 Analysis of the Research Results
4.5 Conclusion
References
5 The Far-Reaching Impacts of Emerging Technologies in Accounting and Finance
5.1 Introduction
5.2 Objectives of the Study
5.3 Artificial Intelligence (AI): Meaning and Definition
5.4 Accounting and Finance Applications for Artificial Intelligence
5.5 Applications for Blockchain Technology in the Financial Sector
5.6 Accounting and Financial Robotic Process Automation
5.7 Accounting and Financial Analytics Using Big Data
5.8 Combining AI with Blockchain, Robotic Process Automation, and Data Science
5.9 Ethical Considerations and Data Privacy Concerns
5.10 Potential Impact and Emerging Trends
5.11 Conclusion
Bibliography
Part II: Generative AI in Risk Management and Fraud Detection
6 Deep Diving into Financial Frauds via Ad Click, Credit Card Management and Document Dispensation in E-Commerce Transactions
6.1 Introduction and Background
6.2 Ad-Click Fraud Detection in the Banking and Financial Sectors
6.3 Credit Card Management Fraud Detection
6.4 Document Dispensation Fraud Detection in E-Commerce Transactions
6.5 Cross-Domain Analysis: Frauds in Banking and Financial Industry
6.6 Ethical and Privacy Considerations: Frauds in Banking and Financial Industry
6.7 Advancements in AI/ML Techniques
6.8 Challenges and Risks
6.9 Conclusion and Future Scope
References
7 Generative AI: A Transformative Tool for Mitigating Risks for Financial Frauds
7.1 Introduction
7.2 Generative AI and Its Characteristics
7.3 Various Types of AI Used in Financial Assets
7.4 Fears in the Financial Sector
7.5 Risk Mitigation in the Finance Industry
7.6 Risk of Financial Fraud
7.7 Requirement for Employee Training
7.8 Regulatory Bodies and Industry Associations
7.9 Hallucination Concern in the Present Times
7.10 Proper Training Requirements
7.11 Future Research Directions
7.12 Conclusion
References
8 Innovation Unleashed Charting a New Course in Risk Evaluation with Generative AI
8.1 Introduction
8.2 New Challenges and Roles
8.3 Reviews
8.4 Findings
8.5 Conclusion
Direction for Future Research
References
9 The Significance of Generative AI in Enhancing Fraud Detection and Prevention Within the Banking Industry
9.1 Introduction
9.2 Literature Review
9.3 Generative AI in Banking Fraud Detection
9.4 Case Studies
9.5 Challenges and Ethical Considerations
9.6 Future Directions
9.7 Conclusion
9.8 Recommendations
References
10 Role of Generative AI for Fraud Detection and Prevention
10.1 Introduction
10.2 Understanding Fraud
10.3 Generative AI Fundamentals
10.4 Applications of Generative AI in Fraud Detection
10.5 Conclusion
References
Part III: Ethical, Legal, and Regulatory Considerations
11 Ethical and Regulatory Compliance Challenges of Generative AI in Human Resources
11.1 Introduction
11.2 Importance of Compliance and Ethical Considerations
11.3 Research Objectives and Methodology
11.4 Literature Review
11.5 Methodology
11.6 Ethical Implications of Generative AI in HR
11.7 Ensuring Compliance with Legal Standards
11.8 Best Practices and Strategies
11.9 Discussion
11.10 Conclusion
11.11 Summary of Main Findings
11.12 Significance of Ethical AI in HR Practices
11.13 Future Research Directions and Potential Advancements
References
12 Navigating the Frontier of Finance: A Scoping Review of Generative AI Applications and Implications
Introduction
Background of the Study
Risks of Generative AI within the Financial Context
Methodology
Identifying the Research Question
Identifying Relevant Studies
Selecting the Studies to be Included
Charting the Data
Results
Discussion
Conclusion
References
Appendix 1
13 Ensuring Compliance and Ethical Standards with Generative AI in Fintech: A Multi-Dimensional Approach
13.1 Introduction to Generative AI in Fintech
13.2 Literature Review
13.3 Methodology
13.4 Case Study
13.5 Findings
13.6 Conclusion
References
14 Privacy Laws and Leak of Financial Data in the Era of Generative AI
Introduction
Case Study
Conclusion
References
15 Ethics and Laws: Governing Generative AI’s Role in Financial Systems
Introduction
Applications of AI in Financial Systems
Ethical Challenges [5, 8, 12]
Ethical AI in Indian Finance: Case Studies and Insights
Conclusion
References
Part IV: Industry-Specific Applications and Innovations
16 Generative AI Tools for Product Design and Engineering
16.1 Introduction
16.2 Concept Generation and Ideation
16.3 Topology Optimization
16.4 Design Customization
16.5 Rapid Prototyping and Iteration
16.6 Multi-Objective Optimization
16.7 AI-Powered Collaboration
16.8 Material Selection and Integration
16.9 Generative Simulations and Testing
16.10 Generative Design for Additive Manufacturing
16.11 Sustainability and Environmental Impact
16.12 Regulatory Compliance and Standards
16.13 Cost Optimization
16.14 Market Trends and Consumer Insights
16.15 Conclusion
References
17 AI-Driven Generative Design Redefines the Engineering Process
17.1 Introduction
17.2 Literature Survey
17.3 Fundamentals of Generative AI
17.4 Generative Design in Product Development
17.5 Case Studies
17.6 Conclusions
References
18 Insurance Disruption: Analytics on Blockchain Transforming Indian Insurance Industry
Introduction
Blockchain Technology
Why is Blockchain Important?
Enabling Industry Collaboration
Blockchain and Insurance
What is it?
Where it is Applicable?
How will it Benefit?
Insurance Sector: India
Challenges
Blockchain and the Insurance Regulatory Framework
Prospects
Conclusion
References
19 Application of Explainable Artificial Intelligence in Fintech
19.1 Introduction
19.2 The Current Landscape of Explainable Artificial Intelligence (XAI)
19.3 Advancing Financial Predictive Analysis: Integrating Explainable AI and Machine Learning in Finance
19.4 Advancements of Explainable AI in Financial Predictions: Methodologies, Regulatory Compliance, and Machine Learning Techniques
19.5 Conclusion and Future Scope
References
20 Empowering Financial Efficiency in India: Harnessing Artificial Intelligence (AI) for Streamlining Accounting and Finance
20.1 Introduction
20.2 Integrating AI into Accounting and Finance
20.3 Benefits of Using AI to Simplify Tasks in Accounting and Finance
20.4 Challenges in Implementing AI in Accounting and Finance
20.5 Future Prospects and Trends
20.6 Valuable Insights for Businesses, Policymakers, and Stakeholders
20.7 Conclusion
References
21 Framework and Interface: The Backbone of AI Systems in Banking in India
21.1 Introduction
21.2 Literature Review
21.3 Framework of AI Systems in Banking
21.4 Interface Design for AI Systems
21.5 Impact of AI in Indian Banking
21.6 Regulatory Environment
21.7 Case Studies
21.8 Future Trends
21.9 Conclusion
References
22 Harnessing Generative AI for Engineering and Product Design: Conceptualization, Techniques, Advancements and Challenges
22.1 Introduction to Generative AI
22.2 Working on Generative AI
22.3 Benefits of Generative AI
22.4 Generative AI Technique
22.5 Data Requirements
22.6 Applications in Concept Generation
22.7 Prototyping and Iteration
22.8 Optimization and Simulation
22.9 Significance of Human-AI Collaboration
22.10 Challenges and Limitations
22.11 Future Trends and Developments
References
Index
End User License Agreement
List of Tables
Chapter 12
Table 12.1 Criteria for inclusion and exclusion for scoping review.
Chapter 19
Table 19.1 Goals of AI.
Table 19.2 AI models used for different purposes.
Chapter 20
Table 20.1 Overview of challenges and corresponding solutions for AI integrati...
Table 20.2 Intricacies in implementing AI-based methods in accounting and fina...
List of Illustrations
Chapter 3
Figure 3.1 Analytical analysis of generative AI system.
Chapter 4
Figure 4.1 Benefits of GAI in finance [1].
Figure 4.2 Use cases of AI in finance [2].
Figure 4.3 Discriminative uses of AI vs. generative uses of AI [3].
Figure 4.4 Types of financial forecasting [4].
Figure 4.5 Flowchart of GAI in financial forecasting.
Figure 4.6 GAN vs. traditional methods in finance.
Chapter 5
Figure 5.1 Elements of artificial intelligence. Source: Own compilation.
Chapter 10
Figure 10.1 Steps to generate synthetic data.
Figure 10.2 Word cloud of the chapter [source: author].
Chapter 12
Figure 12.1 PRISMA flowchart of the study selection process.
Figure 12.2 Distribution of articles by research focus (source: authors).
Chapter 14
Figure 14.1 Screenshot of Amazon app where financial information is being stor...
Figure 14.2 Area that needs attention in terms of personal information rule co...
Figure 14.3 Alert generation if sensitive data are found.
Chapter 16
Figure 16.1 Artificial intelligence operation principle.
Figure 16.2 Generative design system.
Figure 16.3 Manufacturing process.
Figure 16.4 Manufacturing process using a generative design system.
Figure 16.5 Hybrid design system.
Figure 16.6 Architecture of machine learning for a generative design system.
Chapter 17
Figure 17.1 A comparative view of AI, machine learning, deep learning, and gen...
Figure 17.2 Types of machine learning.
Figure 17.3 Architecture of neural networks.
Figure 17.4 Ethical and legal aspects.
Chapter 18
Figure 18.1 Properties of DLT [29].
Figure 18.2 AXA process
Figure 18.3 Benefits of blockchain
Figure 18.4 Cyber security guidelines
Chapter 19
Figure 19.1 Google trends results for interest in explainable AI [2].
Figure 19.2 Model of an ML algorithm (decision tree) to forecast recovery rate...
Figure 19.3 K-nearest neighbors (KNNs) accuracy.
Figure 19.4 SHAP summary plot codes.
Figure 19.5 SHAP summary plot.
Figure 19.6 LIME in random classifier model.
Figure 19.7 LIME in logistic regression.
Chapter 22
Figure 22.1 Benefits of generative AI.
Figure 22.2 (a) VAEs versus (b) GAN.
Figure 22.3 Data requirements.
Figure 22.4 Application of generative AI in concept generation.
Figure 22.5 Applications of generative AI.
Figure 22.6 Significance of human-AI collaboration.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
Pages
ii
iii
iv
xxi
xxii
xxiii
xxiv
xxv
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
149
150
151
152
153
154
155
156
157
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
467
468
469
470
471
472
Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Fintech in Sustainable Digital Society
Series Editors: Ernesto DR Santibanez Gonzalez andPrasenjit Chatterjee ([email protected])
New and disruptive financial strategies and practices based on technology are key to reduce carbon emissions and save the planet. By establishing new sustainable cross-industry ecosystems and business models, the series “Fintech in a Sustainable Digital Society” aims to get a deeper understanding of fintech, insurtech, and blockchain at the intersection of sustainability. It also covers application-focused research in fintech perspectives on AI, cloud computing, machine learning, optimization, and scientific computing. The series disseminates monographs and edited volumes concentrating on all new fintech fields.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Generative Artificial Intelligence in Finance
Large Language Models, Interfaces, and Industry Use Cases to Transform Accounting and Finance Processes
Edited by
Pethuru Raj Chelliah
Edge AI Division, Reliance Jio Platforms Ltd., Bengaluru, India
Pushan Kumar Dutta
School of Engineering and Technology, Amity University Kolkata, West Bengal, India
Abhishek Kumar
Chandigarh University, Punjab, India
Ernesto D.R. Santibanez Gonzalez
Faculty of Engineering, University of Talca, Curico, Chile
Mohit Mittal
Knowtion GmbH, Karlsruhe, Germany
and
Sachin Gupta
Department of Business Administration, Mohanlal Sukhadia University, Udaipur 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.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-ability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-27104-7
Cover image: Adobe FireflyCover design by Russell Richardson
Preface
The financial industry is on the precipice of a transformative revolution, driven by rapid advancements in artificial intelligence (AI) and, more specifically, the emergence of generative AI. This comprehensive volume explores deep into the diverse applications and implications of generative AI across accounting, finance, economics, business, and management, providing readers with a holistic understanding of this rapidly evolving landscape.
The primary purpose of this book is to equip a wide range of stake-holders—from industry practitioners and policymakers to academics and students—with the knowledge and insights necessary to navigate the transformative potential of generative AI in the financial sector. Through contributions from leading experts and researchers, this volume shows the myriad ways this innovative technology is driving efficiency, enhancing decision-making, and mitigating risks across various financial domains.
The chapters herein cover a diverse range of topics. Each highlights the unique challenges, opportunities, and best practices associated with the deployment of generative AI in the financial ecosystem. The book begins by exploring the current applications of AI in accounting, finance, economics, and business management, setting the stage for a deeper dive into the specific use cases.
Whether you are a finance professional seeking to enhance productivity and efficiency using generative AI for competitive advantage, a business leader aiming to implement ethical and compliant AI practices, or a researcher exploring the frontiers of this domain, this book promises to be an invaluable resource in navigating the exciting future where artificial intelligence and the world of finance converge.
Part I: Foundations and Applications of AI in Finance
The opening chapter of this book provides a comprehensive overview of Artificial Intelligence applications across various business disciplines. It begins with an introduction to AI in business and finance, followed by a literature review examining existing research. The chapter then explores into specific AI applications in accounting, finance, economics, and business management, before discussing the potential risks associated with AI implementation. This broad exploration sets the stage for the more focused discussions that follow. The second and third chapters narrow the focus to specific applications and technical aspects of Generative AI in finance. Chapter 2 examines the automation of data entry in the Indian banking industry through Generative AI, providing a practical case study of AI implementation. It covers the methodology used, the specifics of data entry automation, and an analysis of the results. Chapter 3 then explores the future approach of Generative AI, its stylized architecture, and its potential in finance. This chapter bridges the gap between technical understanding and practical applications, discussing risk considerations such as data privacy and embedded bias, as well as significant challenges like explainability and cybersecurity. The final two chapters of this section examine specific applications and broader impacts of AI and related technologies in finance. Chapter 4 focuses on the use of Generative Artificial Intelligence (GAI) for accurate financial forecasting, detailing the methodology, model selection, and performance metrics used in this application. It provides insights into how GAI can improve forecasting accuracy compared to traditional methods. Chapter 5 broadens the scope once again, exploring the far-reaching impacts of emerging technologies in accounting and finance. This chapter covers not only AI but also blockchain, robotic process automation, and big data analytics, examining how these technologies work together to reshape the financial landscape. It concludes by discussing ethical considerations and potential future trends, providing a holistic view of the technological transformation in finance.
Part II: Generative AI in Risk Management and Fraud Detection
The exploration of Generative AI in financial fraud detection begins with a deep dive into various types of financial frauds, including ad-click fraud, credit card management fraud, and document dispensation fraud in e-commerce transactions. This comprehensive overview sets the stage for understanding the complex landscape of financial fraud and the challenges faced by the industry. Building on this foundation, the next chapter introduces Generative AI as a transformative tool for mitigating risks in financial fraud. It discusses the characteristics of Generative AI, its applications in financial assets, and the associated risks and mitigation strategies. This chapter bridges the gap between traditional fraud detection methods and cutting-edge AI technologies, paving the way for innovative approaches to risk evaluation. As the narrative progresses, the focus shifts to charting a new course in risk evaluation with Generative AI. This chapter explores novel applications and emerging roles in the field, showcasing how Generative AI is reshaping the financial industry’s approach to risk assessment. The discussion then narrows to the specific context of the banking industry, examining the significance of Generative AI in enhancing fraud detection and prevention within this sector. By presenting case studies and addressing ethical considerations, this chapter provides a practical perspective on the implementation of Generative AI in real-world banking scenarios. The next chapter ties together the preceding discussions by examining the overarching role of Generative AI in fraud detection and prevention across various financial sectors. It offers a comparative analysis of Generative AI with other fraud detection methods and outlines best practices for implementation. By exploring future trends and potential developments, this chapter not only concludes the current state of Generative AI in financial fraud detection but also opens up avenues for future research and innovation. Throughout these interconnected chapters, readers gain a comprehensive understanding of how Generative AI is revolutionizing the fight against financial fraud, from theoretical concepts to practical applications and future possibilities.
Part III: Ethical, Legal, and Regulatory Considerations
The next set of chapters in this exploration of Generative AI in finance begins with an in-depth examination of ethical and regulatory compliance challenges in human resources. Chapter 11 explores into the complexities of using AI in HR processes, addressing critical issues such as bias in hiring, privacy concerns, and the impact on diversity and inclusion. It also provides practical guidance on compliance with laws like General Data Protection Regulation and Equal Employment Opportunity, emphasizing best practices for responsible AI implementation in HR. This discussion sets the stage for broader considerations of AI’s role in finance, which is further explored in Chapter 12. This chapter offers a scoping review of Generative AI applications and implications in the financial sector, examining its evolution, potential risks, and diverse applications in financial analysis and strategy. By exploring regulatory, ethical, and user-centric perspectives, it provides a comprehensive overview of the current state and future potential of AI-driven finance. The subsequent chapters build upon this foundation, addressing specific aspects of Generative AI in finance. Chapter 13 focuses on ensuring compliance and ethical standards in fintech, likely proposing a multi-dimensional approach that encompasses legal, ethical, operational, and technological considerations. This is followed by Chapter 14, which tackles the critical issue of privacy laws and data protection in the era of Generative AI, exploring the challenges of safeguarding sensitive financial information in an increasingly AI-driven landscape. The final chapter in this set, Chapter 15, brings together these threads by examining the overarching theme of ethics and laws governing Generative AI’s role in financial systems. This chapter likely discusses the need for new regulatory frameworks and ethical guidelines to ensure responsible AI use in finance.
Throughout these chapters, a common thread emerges: the need to balance the transformative potential of Generative AI in finance with robust ethical considerations and regulatory compliance. From HR practices to broader financial systems, the chapters collectively address the multifaceted challenges and opportunities presented by AI technology. They provide a comprehensive view of how the financial sector can navigate the complex landscape of AI implementation, ensuring that innovation is tempered with responsibility and ethical considerations. This set of chapters not only offers valuable insights for practitioners and policymakers but also sets the stage for future research and development in the field of AI-driven finance.
Part IV: Industry-Specific Applications and Innovations
The final section of the book explores into the broader implications and future directions of generative AI in the financial industry. The next set of three chapters (16-18) focus on the application of generative AI in product design and engineering. They explore how AI is revolutionizing these fields by enhancing creativity, streamlining workflows, and driving innovation. The chapters cover various applications such as concept generation, topology optimization, and rapid prototyping, while also delving into the fundamentals of generative AI in engineering, including machine learning and neural networks. Chapter 18 shifts focus to a specific case study, examining how blockchain and analytics are transforming the Indian insurance industry. The next three chapters (19-21) concentrate on AI applications in finance, particularly in the Indian context. Chapter 19 addresses the crucial topic of explainable AI in fintech, exploring how AI decisions can be made transparent and interpretable. Chapter 20 examines how AI is being used to improve efficiency in accounting and finance processes in India, showcasing practical applications for cost savings and enhanced decision-making. Chapter 21 focuses on the technical infrastructure required to implement AI systems in the Indian banking sector, providing insights into system architecture and integration with existing processes. The final chapter (22) serves as a comprehensive overview of generative AI in engineering and product design. It ties together many of the concepts introduced in earlier chapters, providing a holistic view of the current state of generative AI in this field. This chapter covers the conceptualization, techniques, and recent advancements in generative AI for engineering and product design, while also addressing the challenges that need to be overcome. Collectively, these chapters offer a thorough exploration of generative AI and related technologies in finance, engineering, and product design, with a particular emphasis on applications in India. Whether you are a finance professional seeking to enhance productivity and efficiency using generative AI for competitive advantage, a business leader aiming to implement ethical and compliant AI practices, or a researcher exploring the frontiers of this domain, this book promises to be an invaluable tool in navigating the exciting future where artificial intelligence and the world of finance converge.
The editors are grateful to the reviewers who have contributed to improving the quality of the book through their constructive comments. The editors also thank Martin Scrivener and Scrivener Publishing for their support and publication.
The EditorsOctober 2024
Part IFOUNDATIONS AND APPLICATIONS OF AI IN FINANCE
1Artificial Intelligence Application and Research in Accounting, Finance, Economics, Business, and Management
Peterson K. Ozili
Central Bank of Nigeria, Abuja, Nigeria
Abstract
Artificial intelligence is a branch of computer science that develops intelligent machines to perform human tasks. Recently, there has been growing interest in AI applications in professions that have many processes that can be easily automated. There is widespread optimism that AI systems can lead to new innovations or improve existing processes. This study focuses on some applications of artificial intelligence in the accounting, finance, economics, business, and management professions. The study provides a basic understanding of how AI will be useful in the accounting, finance, economics, business, and management professions. The study also offered some insights into the risks posed by the use of artificial intelligence.
Keywords: Artificial intelligence, AI, machine learning, accounting, finance, economics, business, management
1.1 Introduction
This paper presents some applications of artificial intelligence (AI) in the accounting, finance, economics, business, and management professions.
The rise of AI in business can be traced to the rise of financial technology (fintech) [1]. A few decades ago, there was a rapid rise in fintech developments. During this time, innovative technologies were used to deliver financial solutions through software [2]. These technologies were also used to develop software to support accounting professionals and business analysts in their work [3]. However, these technologies did not have the capacity to anticipate the needs of users or to suggest innovative solutions by themselves to users. In other words, these technologies lacked human-like features. This led to the need to develop advanced technology solutions that will offer basic solutions in business, anticipate user needs, and suggest innovative alternatives to users. This led to the development of AI technologies that mimic human behavior and perform human tasks.
Today, AI is widely seen as the use of intelligent machines to perform human tasks [4]. AI has grown beyond being a hype. It has become a disruption that cannot be stopped even though AI developments can be slowed down by regulation [5]. Corporations are already adopting AI to improve their processes, and it has the potential to make a significant difference in certain professions, but whether the big difference it makes is a good thing or a bad thing is a question of ‘ethics’. Some professions that AI will affect are the accounting, finance, economics, business, and management professions because these professions have numerous processes that can be easily automated, and they also have processes that require data that AI can collect and process efficiently. This has led to widespread interest in AI applications in the accounting, finance, economics, business, and management professions. Therefore, there is a need to understand how AI may affect these professions.
The study explains how AI might be useful in the accounting, finance, economics, business, and management professions and the risks of AI. The study does not examine in detail the harm that AI poses to these professions. It only focuses on the potential applications of AI in these professions. By focusing solely on the application of AI in the accounting, finance, economics, business, and management professions, this article hopes to provide another viewpoint on how to think about the role of AI in these professions. The discussion in the articles contributes to the existing AI literature that examines the impact of technology on corporations and society [6–10].
The remaining section of this article is structured in the following way. Section 1.2 discusses some applications of AI in the accounting profession. Section 1.3 discusses some applications of AI in the finance profession. Section 1.4 discusses some applications of AI in the economics profession. Section 1.5 discusses some applications of AI in the business and management profession. Section 1.6 highlights some risks associated with AI. The conclusion of the study is presented in Section 1.7.
1.2 Literature Review
The existing literature has offered arguments for and against AI in the accounting, finance, economics, business, and management disciplines. For instance, Berdiyeva et al.[11] conducted a review of studies that analyze the impact of AI on accounting and found that the majority of the studies predict a positive impact of AI systems in the accounting process. Askary et al.[12] suggested that AI might be more helpful in internal control functions as it can assist managers in generating decision-useful accounting information. This will help to reduce internal control weaknesses and improve audit quality. Shi [13] took a more pessimistic stance toward AI and argued that while AI may improve efficiency and reduce risk, AI is a double-edged sword because it can cause accounting professionals to lose their jobs if accounting professionals do not upgrade their computer skills to meet the needs of the industry. Li and Zheng [14] contradicted the argument of Shi [13] by arguing that AI will not lead to the loss of massive jobs; rather, it will create new jobs for accountants because AI systems cannot make decisions by themselves. This means that accountants will be needed to make decisions using the outputs produced by AI systems. Mohammad et al.[15] examined the potential effect of AI on new-generation accounting professionals using qualitative document analysis and found that new-generation accountants raised concerns that they think AI systems will replace their jobs. The authors then suggested that accountants must learn to adapt to AI systems for them to remain relevant. In response to this concern, Zhang et al.[16] suggested that higher institutions should prepare future accountants for the realities of the AI world by teaching them computerized accounting and how to interpret the output produced by accounting software. Hasan [17] also emphasized the need to prepare accounting educators, standard setters, regulators, and students for the challenges of the AI world so that accounting jobs can be preserved.
In the AI-finance literature, Mhlanga [18] examined the effect of AI on digital finance and found that AI can enable people to participate in the financial system. Königstorfer and Thalmann [19] undertook a literature review to determine the applications of AI in banks. They found that AI is helpful in reducing losses that arise from bank lending. It also increases the speed of payment processing, enables seamless compliance in regulatory reporting, and improves banks’ service to their customers. Goodell et al.[20] presented an overview of AI in finance and found that the finance industry is relying on AI-based computational methods to develop complex models that generate new information. They further argued that AI is also helping to transform trading and investment decisions. Cao [21] showed that AI has a major application in fintech and that AI-led fintech is leading to more personalized products, services, and applications. Ashta and Herrmann [22] showed that AI in finance is encouraging fruitful mergers and acquisitions among financial institutions and fintech providers, and it has also led to increased volatility, uncertainty, and complexity in the investment and wealth management field of finance. They identified other risks of AI in finance which are the bias in AI data and the poor choice of AI algorithm. Farooq and Chawla [23] also argued that AI in the financial sector is leading to the development of complicated and complex financial products and systems that have both benefits and unknown risks. Ozili [10] showed that AI can support financial inclusion efforts by improving efficiency and the risk management function of financial services providers, providing smart financial products and services to banked adults, simplifying the account opening process for unbanked adults, and creating credit scores for unbanked adults using alternative information.
In the economics profession, Aghion et al.[24] argued that AI can affect economic growth. They argued that when AI is introduced into the production of goods and services, it would automate production and stabilize per capita gross domestic product (GDP) growth. They further argued that AI can increase growth depending on how AI is introduced into the production process. They further pointed out that AI can limit population growth and lead to exponential growth in GDP per capita. Wagner [25] argued that AI in economics leads to the creation of a new type of economic agent, and it will lead to a micro-division of labor and a greater triangular agency relationship. It can lead to market dominance and push labor out of the labor market. Szczepanski [26] argued that AI can increase efficiency, but its disruptive effect on the economy and society is enormous as it can lead to the emergence of super firms that create undesirable monopolies or oligopolies, it will widen the gap between developed and developing countries, and it will lead to the demand for workers with AI-relevant skills while rendering traditional labor redundant. Agrawal et al.[27] argued that AI in economics may be challenged by intellectual property policy and labor and antitrust policies that will seek to mitigate the negative consequences of AI on unemployment, inequality, and competition. Furman and Seamans [28] argued that AI will affect the economy through an increase in AI-related activity which will increase productivity growth but will also lead to massive job losses in the labor market and a widening of income inequality as people with AI-relevant skills will be paid more than people doing menial jobs. Korinek and Stiglitz [29] stated that AI can lead to positive effects in the economy if workers are fully insured against the adverse effects of AI innovation and if there is a mechanism for income redistribution, but this type of economy does not exist because people will not be insured against the adverse effect of AI innovation. Ozili [30] showed that AI systems can assist central bank economists in detecting financial stability risks, automating central banking operations, and searching for granular microeconomic/non-economic data that can support central banks in making policy decisions.
In the business and management field, Akerkar [31] showed that AI can solve some problems in business management. They showed that AI can be used to detect abnormal patterns, forecast future events, or automate a process in business. Pendy [32] suggested that AI is relevant in business management because it offers benefits such as increasing efficiency and productivity, improving accuracy, and enhancing customer experience. The author also identified some challenges of AI in business management, which include data quality, lack of AI skills and expertise, ethics and bias, and interoperability issues with existing systems. Bharadiya [33] argued that AI is mostly used in business to analyze data, gain insights, and make informed decisions, and it is very relevant in predictive analytics. It can help companies to unravel hidden patterns and trends which would enable businesses to make accurate forecasts. Soni et al.[34] examined the implications of AI and found that AI increases innovation and entrepreneurial activities that have a positive impact on businesses and society. Raisch and Krakowski [35] suggested that AI will have two effects on business management. It may either lead to business automation which means that machines will take over human tasks, or it can lead to augmentation, which means that humans will collaborate with machines to perform tasks and the choice of the two depends on organizational choice and the business need at a particular point in time.
1.3 Artificial Intelligence Applications in Accounting
Accountants or accounting professionals often follow long-established methodologies and professional standards to analyze information and prepare reports. AI tools for accounting have emerged and are embedded in accounting software and internal control systems. AI-based software and systems are able to accurately automate accounting, tax, and audit tasks, and provide results to accounting professionals who will use their professional judgment to review the produced information. AI will bring a major disruption to the accounting profession by automating most accounting processes. As a result, it is anticipated that AI could lead to 94% job losses in the accounting profession in the next 20 years according to a report published in The Economist1. Despite this, AI offers many benefits to the accounting profession such as saving time, improving accuracy, faster analysis, continuous accounting, and active insights. Examples of AI products or services that can aid the accounting profession are Vic.ai, Indy, Docyt, Booke AI, Truewind, Gridlex Sky, ZENI.AI, Blue Dot, Bill & Divvy, and Sage Intacct. Let us now turn to some of the possible applications of AI in accounting.
Complete automation
– AI-based systems can be used to automate bookkeeping, tax, and audit tasks and provide results to accountants so that they can use their professional judgment to review and use the produced information.
Clearing invoice payment
– AI algorithms can be trained to analyze invoice payment data, clear out invoices, and generate new invoice payments. AI can also be used to quickly match multiple payments and invoices using some pre-set criteria.
Efficient auditing
– AI can help companies to comply with the company’s accounting policies and regulatory policies. AI can also be used to detect and flag inaccurate data entries for review or approval by a human accountant. It can also eliminate the long hours that would be used to manually identify inaccurate entries.
Artificial fiduciaries
– AI systems can be developed to become better fiduciaries than human accounting professionals because AI systems can be trained to make sound decisions and to become artificial fiduciaries that are less prone to errors and undue influences.
Financial reporting
– AI systems can be used to produce quality financial reports, store financial reports, compare the financial reports of many companies in a given period, and compare the financial reports of a company over time, with no human error.
Client communication
– Accountants can use AI chatbots to handle routine client inquiries and provide support to suppliers and customers, to free up accounting staff so that they can focus on more important accounting tasks.
Sales or revenue forecasting
– Accountants can use AI systems to analyze trends in past and present sales or revenue data in order to make accurate predictions about future sales or revenue. This will help accountants and companies in planning and budgeting.
Fraud detection and protection
– Accountants can use AI algorithms to analyze financial data and detect abnormalities that could point to fraudulent activity.
Effective data analysis
– AI can help accountants to analyze historical data quickly and identify patterns that aid present and future decision-making.
Intelligent financial analysis
– AI algorithms can analyze large financial data, identify patterns, trends, and anomalies, and provide valuable insights. AI algorithms can also assist in cash flow forecasting, profitability analysis, and trend identification to reduce costs, maximize profitability, and identify areas for improvement in the company.
AI-assisted research
– Accountants can use AI systems to assist in conducting research related to changes in accounting standards, taxation, and regulation each year. AI can also assist accountants in conducting research on the emerging financial and macroeconomic factors that could affect revenue.
Automating supplier acquisition and procurement processes
– Accountants can use AI systems to evaluate existing supplier data to determine if specific suppliers should be retained and whether more suppliers need to be reached with minimal human intervention. AI systems can also be used to eliminate the onerous paperwork involved in purchasing and procurement processes. This will ensure a more seamless procurement process.
1.4 Artificial Intelligence Applications in Finance
AI in finance means using computers and machines to perform finance functions and to manage money more effectively. AI is commonly applied in financial services to increase efficiency, accuracy, speed, optimize processes, automate processes, and serve customers better. This is often achieved by embedding AI algorithms or AI systems into the core and noncore systems used by financial institutions to assist finance professionals and managers in making important business decisions and meeting the needs of their clients. Presently, there are many AI systems for lending (e.g., Enova, Ocrolus, DataRobot, Scienaptic AI, Zest AI, underwrite.ai, and Socure), financial risk management (e.g., Workiva, Kensho Technologies, Derivative Path, Simudyne, Symphony AyasdiAI, and Range), trading (e.g., Tegus, Canoe, Entera, AlphaSense, Kavout Corporation, and Alpaca), banking (e.g., Kasisto, Abe.ai, and Trim), and for fraud detection (e.g., Vectra AI, Jumio, and F5). AI also has wide applications in different areas of finance, as shown below.
Personal finance
– AI systems can be designed to offer personalized insights and suggest advice to customers on how to manage their personal finances. For example, if a customer wants to make a small payment using a digital app, the AI system embedded in the digital app can advise the customer to make the payment using cash, instead of digital payment, if the transaction amount is too small. It can also offer personalized insights such as where to invest in, how much savings to keep in one’s account based on average monthly or annual income, etc.
Fraud detection and compliance
– AI algorithm can be used to detect fraud and to conduct anti-money laundering (AML) monitoring. For example, AI algorithms can be designed to detect abnormal flow of funds or to detect financial transactions whose purpose is outside the approved purpose, thereby preventing money laundering. AI systems can also be used to detect uncommon or suspicious financial activity and block such activity until the activity log is reviewed by an ALM staff.
Risk management
– AI algorithms can be used to analyze data for the purpose of detecting unacceptable risks. Such algorithms can detect and flag abnormal patterns in data that could signal risks that are beyond the risk tolerance level of the company. Risk managers can use this insight or information to reduce risk-taking.
Consumer banking
– The customer service department of financial institutions can use an AI chatbot or AI robot advisor to assist customers with day-to-day financial, banking, and other inquiries. Financial institutions can also use AI to streamline long processes.
Investment
– Investment analysts and investment bankers can use AI to analyze available investment information of companies and identify companies that need to raise equity or debt. AI can also be used to identify companies that are possible candidates for a merger or acquisition. The insight gained from such AI systems can assist investors in knowing which companies to invest in.
Financial statement analysis
– AI analytics can be used to quickly analyze the financial statements of companies to determine the level of a company’s cash flow, profitability, and efficiency.
Trading and sentiment analysis
– AI can be used to develop an algorithm that tells a trader when to buy, hold, or sell financial assets like stocks, bonds, and cryptocurrencies. AI can also be designed to generate signals through sentiment analysis by analyzing people’s online comments and feelings about specific financial assets and using that information to determine whether to sell, buy, or hold the assets that are being talked about on the Internet.
Fintech and digital lending
– Fintech companies can use AI models to generate unique credit scores and forecast the creditworthiness of online borrowers based on the generated credit scores.
Asset management
– AI algorithms can be used to analyze investment data and the insight generated from it can be used to manage assets remotely. This will enable the rise of passive fund managers and the decline of active fund managers.
Hedge fund
– AI tools can be used to usher in the era of quantitative investing rather than the usual traditional fundamentals-driven long or short strategies. Hedge fund managers can use sophisticated AI tools to analyze large amounts of data to generate short-term winner-takes-all strategies to beat the market within a short time.
Financial forecasting
– AI can be used in conducting high-powered predictive analysis of financial variables. AI algorithms can also be used to predict financial changes in local, regional, and global markets. Companies can use such insights to make better decisions within the company based on the insights generated from AI forecasting and to reduce the company’s exposure to certain markets.
Preserving financial stability
– Financial institutions regularly submit regulatory compliance returns to the regulatory authorities. The authorities can use AI analytics to scrutinize the data in the regulatory returns to detect early any sharp drop in bank deposits, a sudden and significant decrease in banking sector liquidity, and excessive debt in the financial system. This insight will enable the authorities to take quick action to prevent a bank run or a liquidity crisis in the financial system as well as to prevent a financial crisis.
Pensions
– AI can be used to engage and communicate more actively with pensioners using various channels such as chatbots, robot advisors, and voice assistants. AI can also help pension funds automate pension data collection, reporting, compliance, and auditing processes in order to reduce costs, human errors, and the risks of fraud.
Insurance
– AI can assist insurance companies in analyzing risk, detecting fraud, and reducing human error in the insurance application process. It can enable the automation of claims payout requests and assist in insurance underwriting and risk monitoring.
1.5 Artificial Intelligence Applications in Economics
The major application of AI in the economics discipline is to analyze micro and macro-economic data and use the insights gained to make meaningful economic decisions. Depending on the field of economics, AI tools can be used to make forecasts and price and output optimization decisions. AI tools will be more useful in specific economics disciplines and less useful in other disciplines. Below are some applications of AI in the economics discipline.
AI can enable price discovery in markets
– AI can make it easy to discover the actual price or average price of goods and services in markets where sellers sell at different prices and in markets where there are many transactions in a particular good or service. AI systems can also be used to determine the exact time of sudden changes in the price of goods and services. Policymakers can use such insights to intervene in markets.
AI in behavioral economics
– Behavioral economists can use AI tools to analyze large amounts of data obtained from individuals and markets to understand how individuals and markets make decisions. AI can also assist behavioral economists in conducting sentiment analysis to understand the sentiment or feelings of individuals and investors about products, markets, industries, or the economy.
AI in labor economics
– Labor economists and policymakers can use AI tools to collect data from employers’ recruitment platforms and from job search websites and agencies. The collected data can be analyzed using AI analytics to determine whether there is a tight labor supply market and to make early interventions to improve labor supply before official labor statistics are published. Recruiters can also use AI-based systems to reduce the lengthy process involved in recruiting. This will save time, reduce the cost of recruiting, and motivate employers to recruit more employees, thereby reducing unemployment to some extent.
AI in monetary economics
– Monetary economists in the central bank can use AI analytics to forecast the appropriate level of money supply that is needed to support economic growth. Central bank economists can also use AI data analytics to determine the sectors in the economy where there is too much money supply. Central banks can also use AI analytics to tackle inflation. They can use AI tools to collect data from online consumer market forums to identify the goods and services that consumers feel are unjustly inflated. This insight can assist central banks in understanding the drivers of inflation in consumer markets. It can also assist central banks in knowing the appropriate monetary policy tools to deploy to tackle inflation.
AI in development economics
– AI can assist development economists in combating poverty by analyzing demographic data and identifying the people who need more resources for healthcare, welfare, and education to eradicate poverty. AI data analytics can also be used to assist development economists in identifying the most vulnerable people in the population who need prolonged welfare intervention to enable them to live a good life.
AI in financial economics
– Financial economists and investor analysts can use AI analytics to make better decisions when trading in financial assets such as bonds and stocks. AI systems can quickly analyze historical and present stock or bond prices and make predictions about the future direction of stock and bond prices.
AI in economic research
– AI can be used to collect and analyze real-world data to understand economic behavior particularly the pricing, consumption, and savings behavior of households and firms.
1.6 Artificial Intelligence Applications in Business and Management
AI is rapidly changing the way work is done in the business and management profession. Companies are deploying AI to automate jobs that can be easily automated to save time, save cost, and achieve better financial and non-financial performance. Automating many business management processes will allow managers to focus on the things that matter and the things that add value to their organizations. The downside is that AI may lead to the loss of jobs, particularly for employees who perform routine administrative tasks that can be easily automated. Employees who are affected will need to develop adaptation mechanisms and adapt to AI needs in the workplace. The affected employees may be moved to job roles that AI cannot automate within a company. Below are some AI applications in the business and management profession.
AI will automate business administration and control functions
– AI will automate many administrative functions that take most of the time of managers. Many of these functions are “repeat tasks” that need to be automated using AI tools so that these tasks can continue even when an employee is sick or when an employee is on annual leave or vacation.
AI will change employees’ role in the company
– The automation of routine easy-to-do tasks will lead companies to focus on hiring employees whose role is to develop and gain insights from AI-automated functions and make informed judgments that lead to better decision-making. The role of managers will be focused on interpreting the information obtained from organizational AI systems based on their experience and their knowledge of organizational culture, policies, and strategy.
AI will improve customer service processes
– Business managers can use AI to deliver a positive experience for customers by using AI robotics to multi-task and anticipate customer needs, proffer solutions and present the solutions in a way that gives customers more choice while meeting their needs. This way, AI will be able to displace a human customer service representative who cannot multi-task and can only work from 9 am to 5 pm daily.
AI will improve employee appraisal
– Managers in charge of a team of employees can use AI robotics to test and assess employees’ knowledge of the tasks assigned to them. AI robotics can also be used to evaluate the performance of employees in the past year by requiring employees to provide an oral presentation to the AI robot who will use natural language processing to evaluate the employee’s presentation and match it against some predetermined criteria. The AI robot will use this information to appraise employees and suggest areas where each employee needs more training, better coaching, or positive feedback.
AI can be used to monitor competitors’ activities
– Business managers can use AI tools to gain insight into what competitors are doing in the industry. Business managers can use AI tools to search the Internet to obtain new information about competitors’ activities such as new products, new services, or a new technology that has been recently deployed by competitors. The insight gained from such information can help managers make rapid changes either by doing what the competitor has done or by doing something much better. This will help managers to remain competitive in the industry and ensure that managers are not behind the competition in the industry.
AI can facilitate competitive advantage
– Business managers can use AI tools to develop a formidable competitive advantage over their competitors. Companies can use AI to develop new products and service offers that meet new demand in the market. This will give the company a formidable advantage in the industry and set the company apart from its competitors.
1.7 Risks of AI
While the previous sections have shown that AI has important applications in accounting, finance, economics, business, and management, it must be acknowledged that AI also presents many risks. We know that AI will be used to access lots of data, and as a result, data privacy risks will emerge [30, 36]. Also, the data used to train the AI system may have human bias [37]. There is also the risk or problem of accountability in terms of who takes responsibility if AI-based insights lead companies to make bad decisions that result in huge losses or loss of reputation [38]. In such cases, a computer model should not be blamed, rather, a person should receive the blame. Therefore, there is a need to develop a system for accountability whenever AI systems are being deployed to aid decision-making. There is also the problem of lack of transparency on how AI models reach the conclusions they generate [39]. Many times, the assumptions embedded into the AI systems are unknown, and when they are known, they may not be understood. Another area of concern is that, as the demand for AI expertise in accounting, finance, economics, and management grows, there may be a shortage of skilled professionals who can develop, implement, and manage AI solutions in these fields [40]. Furthermore, it may be costly for companies to train and upskill their employees to meet the demands of an AI-driven company. Finally, there is the risk that AI systems can be weaponized and corrupted to make them engage in unethical practices such as AI system refusing to forget or delete sensitive confidential information, or the AI system hacking into computers to access people’s private information [41]. These risks suggest that the future of AI will depend on AI ethics and governance. There is a need to regulate or govern AI developments and applications because allowing people and corporations to use AI to perform every business function and to access and analyze all types of data may not be good for society due to the sensitive nature of corporate and personal data.
