The Use of Artificial İntelligence in Accounting and Auditing

Öz

This study examines the application of artificial intelligence (AI) in accounting and auditing, emphasizing its role in enhancing the efficiency and accuracy of accounting processes. Through a comprehensive analysis of existing literature, the study evaluates the impact of AI technologies, including machine learning and natural language processing, on key areas such as continuous auditing, financial forecasting, and regulatory compliance. The findings indicate that AI enhances the quality of financial reporting, facilitates early fraud detection, and reduces human error, thereby improving the reliability of financial statements. However, the practical adoption of these technologies is challenged by factors such as high implementation costs and the need to comply with regulatory requirements. To address these challenges, the study highlights the necessity of developing appropriate regulatory frameworks to support and promote the integration of AI in accounting and auditing.

Anahtar Kelimeler

• Artificial intelligence
• continuous auditing
• financial forecasting
• fraud detection.

Muhasebe ve Denetim Alanında Yapay Zekâ Kullanımı

Öz

Bu çalışma, yapay zekanın (YZ) muhasebe ve denetim alanındaki uygulamalarını inceleyerek, muhasebe süreçlerinin verimliliğini ve doğruluğunu artırmadaki rolüne odaklanmaktadır. Mevcut literatürün kapsamlı bir analizi doğrultusunda, makine öğrenimi ve doğal dil işleme gibi YZ teknolojilerinin sürekli denetim, finansal tahmin ve düzenleyici uyumluluk üzerindeki etkileri değerlendirilmektedir. Elde edilen bulgular, YZ'nin finansal raporlama kalitesini artırdığını, dolandırıcılığın erken tespit edilmesini sağladığını ve insan hatalarını azaltarak finansal tabloların güvenilirliğini güçlendirdiğini ortaya koymaktadır. Ancak, bu teknolojilerin uygulamaya geçirilmesi, yüksek maliyetler ve düzenleyici gerekliliklere uyum gibi çeşitli zorluklarla karşı karşıyadır. Bu bağlamda çalışma, YZ'nin muhasebe ve denetim süreçlerine entegrasyonunu desteklemek ve yaygınlaştırmak amacıyla uygun düzenleyici çerçevelerin geliştirilmesinin önemini vurgulamaktadır.

Anahtar Kelimeler

• Yapay zekâ
• sürekli denetim
• finansal tahmin
• dolandırıcılık tespiti.

Kaynakça

1. Abada, R et al. (2022). An overview on deep leaning application of big data. Mesopotamian Journal of Big Data, 31-35.
2. Abbas, A. (2024). A proposed approach to activating the use of artificial intelligence technology in the accounting field and its impact on supporting and developing the accounting profession. Scientific Journal of Financial and Commercial Studies and Research, 5(1)2, 631-666.
3. Abdel Nour, A. (2005). Introduction to Artificial Intelligence. King Abdulaziz City for Technical Sciences KACST: Saudi Arabia.
4. Abdelrazeik, A. (2022). Accounting in the big data era: a literature review. Journal of Financial and Business Research,255-272, (3)23,
5. Adelakun, B et al. (2024). Leveraging ai for sustainable accounting: developing models for environmental impact assessment and reporting. Finance & Accounting Research Journal, 6(6),
6. Adelakun, B. (2023). Ai-driven financial forecasting: innovations and implications for accounting practices. International Journal of Advanced Economics, 5(9), 323-338.
7. Adnan, M et al. (2024). Factors influencing the adoption of artificial intelligence (ai) based accounting system in malaysian organization: a conceptual paper. Accounting and Finance Research, 13(2), 80.
8. Agarwal, S. (2023). Rule-based analysis of disease detection. International Journal for Research in Applied Science and Engineering Technology, 11(5), 4991-4996.

9. Ahmadi, S. (2024). A comprehensive study on integration of big data and ai in financial industry and its effect on present and future opportunities. International Journal of Current Science Research and Review, 07(01).
10. Akula, R and Garibay, I. (2021). Audit and assurance of ai algorithms: a framework to ensure ethical algorithmic practices in artificial intelligence.
11. Alghamdi, O and Agag, G. (2023). Boosting innovation performance through big data analytics powered by artificial intelligence use: an empirical exploration of the role of strategic agility and market turbulence. Sustainability, 15(19), 14296.
12. Alotaibi, E and Alnesafi, A. (2023). Assessing the impact of audit software on audit quality: auditors' perceptions. International Journal of Applied Economics, Finance and Accounting, 17(1), 97-108.
13. Antwi, B et al. (2024). Enhancing audit accuracy: the role of ai in detecting financial anomalies and fraud. Finance & Accounting Research Journal, 6(6), 1049-1068.
14. Anwar, S and Ali, A. (2018). Anns-based early warning system for indonesian islamic banks. Buletin Ekonomi Moneter Dan Perbankan, 20(3), 325-342.
15. Appelbaum, D et al. (2017). Big data and analytics in the modern audit engagement: research needs. Auditing: A Journal of Practice & Theory, 36(4), 1-27.
16. Asher, C et al. (2021). The Role of AI in Characterizing the DCM phenotype. Frontiers in Cardiovascular Medicine, 8,1-20.
17. Askary, S et al. (2018). Artificial intelligence and reliability of accounting information. Lecture Notes in Computer Science, 315-324.
18. Aziz, F. (2023). Data analytics impacts in the field of accounting. World Journal of Advanced Research and Reviews, 18(2), 946-951.
19. Bochkay, K et al. (2022). Textual analysis in accounting: what's next? Contemporary Accounting Research, 40(2), 765-805.
20. Bonsu, M et al. (2023). Does fintech lead to better accounting practices? Empirical evidence. Accounting Research Journal, 36(2/3), 129-147.
21. Boudah, A. (2006/2007). The use of expert systems in the field of decision-making to grant bank loans. Doctoral thesis, Mentouri University - Faculty of Economics and Management Sciences, Constantine, Algeria.
22. Cabedo, J and Huguet, D. (2021). Textual analysis and sentiment analysis in accounting. Revista De Contabilidad, 24(2), 168-183.
23. Cai, C. (2022). Training mode of innovative accounting talents in colleges using artificial intelligence. Mobile Information Systems, 2022, 1-11.
24. Carmona, S and Ezzamel, M. (2007). Accounting and accountability in ancient civilizations: Mesopotamia and ancient Egypt. Accounting, Auditing & Accountability Journal, 20(2), 177-209.
25. Carvalho, C and Almeida, A. (2022). The adequacy of accounting education in the development of transversal skills needed to meet market demands. Sustainability, 14(10), 5755.
26. Changchit, C and Holsapple, C. (2004). The development of an expert system for managerial evaluation of internal controls. Intelligent Systems in Accounting. Finance and Management, 12(2), 103-120.
27. Chen, J and Storchan, V. (2021). Seven challenges for harmonizing explainability requirements.
28. Chen, W. (2023). The gso-deep learning-based financial risk management system for rural economic development organizations. International Journal of Advanced Computer Science and Applications, 14(10).
29. Chen, Y et al. (2022). A full population auditing method based on machine learning. Sustainability, 14(24), 17008.
30. Chopra, R and Sharma, G. (2021). Application of artificial intelligence in stock market forecasting: a critique, review, and research agenda. Journal of Risk and Financial Management, 14(11), 526.
31. Chu, M and Yong, K. (2021). Big data analytics for business intelligence in accounting and audit. Open Journal of Social Sciences, 09(09), 42-52.
32. Dagunduro, E et al. (2023). Application of artificial intelligence and audit quality in Nigeria. Advances in Multidisciplinary and Scientific Research Journal Publication, 11(1), 39-56.
33. Daoud, I. (2023). Ethical considerations in the era of digitalization: a closer look at the accounting profession. Iris Journal of Economics & Business Management, 1(4).
34. Deloitte (2018), 16 Artificial Intelligence projects from Deloitte - Practical cases of applied AI. https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/innovatie/delo itte-nl-innovatie-artificial-intelligence-16-practical-cases. pd
35. Deloitte, (t.y.) https://www2.deloitte.com/us/en/pages/consulting/topics/cortex-ai platform.html
36. Duan, Y et al. (2005). Web-based expert systems: benefits and challenges. Information & Management, 42(6), 799-811. https://doi.org/10.1016/j.im.2004.08.005
37. El Hajj, M and Hammoud, J. (2023). Unveiling the influence of artificial intelligence and machine learning on financial markets: a comprehensive analysis of ai applications in trading, risk management, and financial operations. Journal of Risk and Financial Management, 16(10), 434.
38. Elias, A and Rabie, Q. (2022). Implications of artificial intelligence on the auditing and accounting professions. Master Thesis, Ibn Khaldoun University.
39. Elias, A and Rabie, Q. (2022). Implications of artificial intelligence on the auditing and accounting professions, Master Thesis, Ibn Khaldoun University.
40. Ernst & Young (t.y.), Canvas. https://www.ey.com/en_gl/audit/technology/canvas
41. Ernst & Young (t.y.), EY Blockchain Analyzer: Explorer & Visualizer. EY Blockchain Analyzer: Explorer & Visualizer | EY - US
42. Ernst & Young (t.y.), Helix. https://www.ey.com/en_gl/audit/technology/helix
43. Faccia , A and Mosteanu , N. (2019). Accounting and blockchain technology: from double-entry to triple-entry. The Business and Management Review, 10(2),108-116.
44. Fedyk, A et al. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938-985.
45. Felländer, A et al. (2022). Achieving a data-driven risk assessment methodology for ethical ai. Digital Society, 1(2).
46. Fontanelli, D. (2022). Perception for autonomous systems: a measurement perspective on localization and positioning. IEEE Instrumentation & Measurement Magazine, 25(4), 4-9.
47. Gao, L. (2024). Construction and evaluation of financial distress early warning model based on machine learning. Journal of Electrical Systems, 20(3s), 315-327.
48. Golić, Z. (2020). Finance and artificial intelligence: the fifth industrial revolution and its impact on the financial sector. Open Journal Systems, 8(19), 67.
49. Goonatilleke, S and Hettige, B. (2022). Past, present and future trends in multi-agent system technology. Journal Européen Des Systèmes Automatisés, 55(6), 723-739.
50. Grissa, I and Abaoub, E. (2024). Enhancing fraud detection in financial statements with deep learning: an audit perspective. International Journal for Multidisciplinary Research, 6(1).
51. Hasan, A (2022). Artificial intelligence (ai) in accounting & auditing: a literature review. Open Journal of Business and Management, 10(01), 440-465.
52. Huang, A et al. (2023). Finbert: a large language model for extracting information from financial text. Contemporary Accounting Research, 40(2), 806-841.
53. Huang, Z et al. (2024). Application of machine learning-based k-means clustering for financial fraud detection. Academic Journal of Science and Technology, 10(1), 33-39.
54. Imeni, M. (2020). Fuzzy logic in accounting and auditing. Journal of fuzzy extension and application, 1 (1), 66-72.
55. Ivakhnenkov, S. (2023). Artificial intelligence application in auditing. Scientific Papers NaUKMA. Economics, 8(1), 54-60.
56. Ivakhnenkov, S. (2023). Artificial intelligence application in auditing. Scientific Papers NaUKMA. Economics, 8(1), 54-60.
57. Iwuanyanwu, U et al. (2023). Analyzing the role of artificial intelligence in it audit: current practices and future prospects. Computer Science & IT Research Journal, 4(2), 54-68.
58. Javaid, H. (2024). How Artificial Intelligence is Revolutionizing Fraud Detection in Financial Services. Innovative Engineering Sciences Journal,10(1),1-7.
59. Jerome, O et al. (2023). Effective cross-platform mobile app development using progressive web apps, deep learning and natural language processing. International Journal of Engineering Applied Sciences and Technology, 7(9), 14-20.
60. Kend, M and Nguyen, L. (2020). Big data analytics and other emerging technologies: the impact on the australian audit and assurance profession. Australian Accounting Review, 30(4), 269-282.
61. Kend, M and Nguyen, L. (2020). Big data analytics and other emerging technologies: the impact on the australian audit and assurance profession. Australian Accounting Review, 30(4), 269-282.
62. Klius, Y et al. (2020). International approaches to organizing an internal control system at an enterprise in the digital era. Economic Annals-ХХI, 185(9-10), 133-143.
63. Kokina, J and Davenport, Th. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115 – 122.
64. KPMG Clara. https://home.kpmg/xx/en/home/services/audit/kpmg-clara.html
65. Kureljusic, M and Karger, E. (2023). Forecasting in financial accounting with artificial intelligence – a systematic literature review and future research agenda. Journal of Applied Accounting Research, 25(1), 81-104.
66. Lai, G. (2023). Artificial intelligence techniques for fraud detection.
67. Lehner, O and Knoll, C (2022). Artificial Intelligence in Accounting. Taylor & Francis group an informa business, London.
68. Li, R. (2023). Research on the impact of ai application on capital chain resilience. Engineering Economics, 34(5), 536-553.
69. Li, Z. et al. (2023). Research on the application of machine learning in smart finance. Proceedings of the 2nd International Conference on Public Management, Digital Economy and Internet Technology.
70. Licardo, T. et al. (2024). Intelligent robotics—a systematic review of emerging technologies and trends. Electronics, 13(3), 542.
71. Lidiana, L. (2024). Ai and auditing: enhancing audit efficiency and effectiveness with artificial intelligence. Accounting Studies and Tax Journal (COUNT), 1(3), 214-223.
72. Liu, N. et al. (2021). Tracking developments in artificial intelligence research: constructing and applying a new search strategy. Scientometrics, 126(4), 3153-3192.
73. Liu, S. (2022). Robotic Process Automation (RPA) in Auditing: A Commentary. International Journal of Computer Auditing, 14(1), 23-28.
74. Liu, Z. et al. (2020). Finbert: a pre-trained financial language representation model for financial text mining. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 4513-4519.
75. Luger,G. (2009). Artificial Intelligence. The United States of America: University of New Mexico
76. Lui, G. and Shum, C. (2022). Impact of robotic process automation on future employment of accounting professionals. Proceedings of the Annual Hawaii International Conference on System Sciences.
77. Majumder, T. (2024). The evaluating impact of artificial intelligence on risk management and fraud detection in the commercial bank in Bangladesh. International Journal of Applied and Natural Sciences, 1(1), 67-76.
78. Maldonado, I et al. (2020). Big data and financial auditing in Portugal. 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), 1-7.
79. Martin, A. (2021). The impact of ai-driven risk compliance systems on corporate governance. Universal Research Reports, 8(4).
80. Mavi, V et al. (2023). Retrieval-augmented chain-of-thought in semi-structured domains. Proceedings of the Natural Legal Language Processing Workshop 2023.
81. Megeid, N. (2022). The role of big data analytics in supply chain “3fs”: financial reporting, financial decision making and financial performance “an applied study”. Accounting thought, 207-268, (2)26,
82. Minkkinen, M et al. (2022). Continuous auditing of artificial intelligence: a conceptualization and assessment of tools and frameworks. Digital Society, 1(3).
83. Moffitt, K et al. (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1-10.
84. Mökander, J. (2023). Auditing of ai: legal, ethical and technical approaches. Digital Society, 2(3).
85. Mpofu, F. (2023). The application of artificial intelligence in external auditing and its implications on audit quality? a review of the ongoing debates. International Journal of Research in Business and Social Science (2147- 4478), 12(9), 496-512.
86. Ng, M et al. (2021). A systematic literature review on intelligent automation: aligning concepts from theory, practice, and future perspectives. Advanced Engineering Informatics, 47, 101246.
87. Nica, I et al. (2024). Mathematical patterns in fuzzy logic and artificial intelligence for financial analysis: a bibliometric study. Mathematics, 12(5), 782.
88. Nurdiani, T et al. (2023). The impact of data volume and analytical complexity in big data technology on financial performance prediction in financial companies in Indonesia. The ES Accounting and Finance, 2(01), 64-76.
89. Odeyemi, O et al. (2023). The role of ai in transforming auditing practices: a global perspective review. World Journal of Advanced Research and Reviews, 21(2), 359-370.
90. Odeyemi, O et al. (2024). Forensic accounting and fraud detection: a review of techniques in the digital age. Finance & Accounting Research Journal, 6(2), 202-214.
91. Odonkor, B et al. (2024). Integrating artificial intelligence in accounting: a quantitative economic perspective for the future of u.s. financial markets. Finance & Accounting Research Journal, 6(1), 56-78.
92. Onwubuariri, E et al. (2024). Ai-driven risk assessment: revolutionizing audit planning and execution. Finance & Accounting Research Journal, 6(6), 1069-1090.
93. Owonifari, V et al. (2023). Evaluation of artificial intelligence and efficacy of audit practice in Nigeria. Asian Journal of Economics, Business and Accounting, 23(16), 1-14.
94. Oxford University Press, (2010), Topics English Language-Dictionaries, Internet Archive.
95. Özyiğit, H. (2022). Muhasebe alanına güncel yaklaşımlar: metin madenciliği̇. Muhasebe ve Vergi Uygulamaları Dergisi, 15(3), 637-663.
96. Peng, Y. et al. (2023). Riding the waves of artificial intelligence in advancing accounting and its implications for sustainable development goals. Sustainability, 15(19), 14165.
97. Peng, Y. et al. (2023). Riding the waves of artificial intelligence in advancing accounting and its implications for sustainable development goals. Sustainability, 15(19), 14165.
98. Prasetianingrum, S. and Sonjaya, Y. (2024). The evolution of digital accounting and accounting information systems in the modern business landscape. Advances in Applied Accounting Research, 2(1), 39-53.
99. PwC (t.y.), AI and the Audit https://www.pwc.com/gx/en/about/stories-from across-the-world/harnessing-ai-to-pioneer-new-approaches-to-the-audit.html,
100. PwC (t.y.), Discover Aura: The technology platform that powers your financial statement audit. Aura: Audit technology: PwC
101. PwC (t.y.), Harnessing the power of AI to transform the detection of fraud and error https://www.pwc.com/gx/en/about/stories-from-across-the-world/harnessing the-power-of-ai-to-transform-the-detection-of-fraud-and-error.html
102. PwC (t.y.), Supporting the auditing of cryptocurrency https://www.pwc.com/gx/en/services/audit-assurance/publications/halo solution-for-cryptocurrency.html
103. Qiao, G. (2020). Application research of big data technology in audit field. Theoretical Economics Letters, 10(05), 1093-1102.
104. Raharjo, W et al. (2024). An ideal regulatory framework for robotic surgery utilization in Indonesia. Advances in Social Science, Education and Humanities Research, 640-643.
105. Ranković, M et al. (2023). Artificial intelligence and the evolution of finance: opportunities, challenges and ethical considerations. EdTech Journal, 3(1), 20-23.
106. Rathor, K. (2024). Machine learning based smart e-auditor to prevent tax evasion. International Research Journal of Modernization in Engineering Technology and Science.
107. Rhee, Y. et al. (2022). Exploring knowledge trajectories of accounting information systems using business method patents and knowledge persistence-based main path analysis. Mathematics, 10(18), 3349.
108. Ríkharðsson, P. et al . (2022). Artificial intelligence and auditing in small‐ and medium‐sized firms: expectations and applications. AI Magazine, 43(3), 323-336.
109. Ríkharðsson, P. et al. (2022). Artificial intelligence and auditing in small‐ and medium‐sized firms: expectations and applications. AI Magazine, 43(3), 323-336.
110. Rozario, A. and Vasarhelyi, M. (2018). Auditing with Smart Contracts. The International Journal of Digital Accounting Research, 1-27.
111. Rui , Z. et al. (2023). The application of rpa technology in the financial and taxation domains of smes in the digital era. Academic Journal of Business & Management, 5(24).
112. Sakapaji, S and Puthenkalam, J. (2023). Harnessing ai for climate-resilient agriculture: opportunities and challenges. European Journal of Theoretical and Applied Sciences, 1(6), 1144-1158.
113. Sayed, T. (2021). Application of expert systems or decision-making systems in the field of education. Information Technology in Industry, 9(1), 1396-1405.
114. Schreyer, M. et al. (2019). Detection of accounting anomalies in the latent space using adversarial autoencoder neural networks.
115. Schreyer, M et al. (2022). Federated and privacy-preserving learning of accounting data in financial statement audits. Proceedings of the Third ACM International Conference on AI in Finance, 105-113.
116. Schreyer, M. et al. (2022). Federated and privacy-preserving learning of accounting data in financial statement audits. Proceedings of the Third ACM International Conference on AI in Finance.
117. Schultz, M. and Tropmann-Frick, M. (2020). Autoencoder Neural Networks Versus External auditors: detecting unusual journal entries in financial statement audits. Proceedings of the Annual Hawaii International Conference on System Sciences.
118. Seethamraju, R. and Hecimovic, A. (2022). Adoption of artificial intelligence in auditing: an exploratory study. Australian Journal of Management, 48(4), 780-800.
119. Shani, M and Al-Tameemi, L. (2021). The impact of using artificial intelligence in the audit process to enhance the transparency of financial reports and its reflection on the reputation of the external auditor. Journal of Advance Research in Business, Management and Accounting (ISSN: 2456-3544), 7(3), 21-32.
120. Shaveta (2023). A review on machine learning. International Journal of Science and Research Archive, 09(01), 281–285.
121. Shen, D. (2021). Grand challenges in radiology. Frontiers in Radiology, 1.
122. Sheng, B. et al. (2022). An overview of artificial intelligence in diabetic retinopathy and other ocular diseases. Frontiers in Public Health, 10.
123. Silva-Fernández, L and Carmona, L. (2019). Meta-analysis in the era of big data. Clinical Rheumatology, 38(8), 2027-2028.
124. Sinha, A and Zhao, H. (2011). Turning expert systems for cost-sensitive decisions. Advances in Artificial Intelligence, 2011, 1-12.
125. Smith, C et al. (2006), The History of Artificial Intelligence. University of Washington.
126. Ssetimba, I. et al. (2024). Advancing electronic communication compliance and fraud detection through machine learning, nlp and generative ai: a pathway to enhanced cybersecurity and regulatory adherence. World Journal of Advanced Research and Reviews, 23(2), 697-707.
127. Staub, S et al. (2015). Artificial Neural Network and Agility. Procedia - Social and Behavioral Sciences, 195, 1477-1485.
128. Strohm, L et al. (2020). Implementation of artificial intelligence (ai) applications in radiology: hindering and facilitating factors. European Radiology, 30(10), 5525-5532.
129. Sultan, M et al. (2024). Overview of federated learning. International Research Journal of Modernization in Engineering Technology and Science.
130. Supriadi, I. (2024). The audit revolution: integrating artificial intelligence in detecting accounting fraud. Akuntansi Dan Teknologi Informasi, 17(1), 48-61.
131. Tan, B and Low, K. (2019). Blockchain as the database engine in the accounting system. Australian Accounting Review, 29(2), 312-318.
132. Tan, B and Low, K. (2019). Blockchain as the database engine in the accounting system. Australian Accounting Review, 29(2), 312-318.
133. Tandiono, R. (2023). The impact of artificial intelligence on accounting education: a review of literature. E3S Web of Conferences, 426, 02016.
134. Tofan, O. and Airinei, D. (2024). Digital skills in collecting and interpreting audit evidence. Audit Financiar, 22(175), 498-509.
135. Truby, J. (2020). Governing artificial intelligence to benefit the unsustainable development goals. Sustainable Development, 28(4), 946-959.
136. Üçoğlu, D. (2020). Current machine learning applications in accounting and auditing. Pressacademia, 12(1), 1-7. https://doi.org/10.17261/pressacademia.2020.1337.
137. Veselovsky, M et al. (2021). Intellectual governance in the digital economy of Russia. Proceedings of International Scientific and Practical Conference “Russia 2020 - A New Reality: Economy and Society” (ISPCR 2020).
138. Wang, X. (2023). Algorithms and research in accounting application based on artificial intelligence. FFIT, EAI.
139. Wang, X. (2023). Quality evaluation of accounting information based on multi-layer neural network. Atlantis Highlights in Intelligent Systems, 609-615.
140. Xu, R. (2019). Path planning of mobile robot based on multi-sensor information fusion. EURASIP Journal on Wireless Communications and Networking, 2019(1).
141. Xu, Y et al. (2020). Ai customer service: task complexity, problem-solving ability, and usage intention. Australasian Marketing Journal, 28(4), 189-199.
142. Xu, Y. (2024). Financial statement text information mining and key information extraction model construction. Journal of Electrical Systems, 20(6s), 800-805.
143. Yao, M. (2024). RPA technology enables highly automated development of corporate financial accounting processes. Applied Mathematics and Nonlinear Sciences, 9(1).
144. Younas, M. (2019). Research challenges of big data. Service Oriented Computing and Applications, 13(2), 105-107.
145. Zaheer, S et al. (2023). A multi parameter forecasting for stock time series data using LSTM and deep learning model. Mathematics, 11(3), 590.
146. Zakaria, S et al. (2023), "Has the world of finance changed? A review of the influence of artificial intelligence on financial management studies. Information Management and Business Review, 15(4), 420-432.
147. Zaripova, R et al. (2023). Unlocking the potential of artificial intelligence for big data analytics. E3S Web of Conferences, 460, 04011.
148. Zeng, X. et al. (2022). Artificial intelligence adoption and digital innovation: how does digital resilience act as a mediator and training protocols as a moderator? Sustainability, 14(14), 8286.
149. Zhang, Y. et al. (2020). The impact of artificial intelligence and blockchain on the accounting profession. IEEE Access, 8, 110461-110477.