Application of Artificial Intelligence in Corporate Financial Accounting

Authors

  • Yun Luan Liaodong University, Liaoning, China Author

DOI:

https://doi.org/10.62677/IJETAA.2408126

Keywords:

Artificial intelligence, Corporate Financial Accounting, Machine Learning, Natural Language Processing, RPA

Abstract

With the rapid development of artificial intelligence (AI) technology, its application in corporate financial accounting has become increasingly widespread. This paper explores the current status, main technologies, application areas, and challenges and opportunities of AI in corporate financial accounting. Through an analysis and review of extensive literature, this study finds that AI technologies, such as machine learning, natural language processing, and Robotic Process Automation (RPA), are reshaping the workflows and decision-making models in corporate financial accounting. These technologies demonstrate tremendous potential in financial reporting automation, intelligent auditing, financial forecasting, and risk management. However, the application of AI also faces challenges such as data quality, ethical issues, and talent shortages. This paper proposes a series of recommendations to promote the effective application and sustainable development of AI in corporate financial accounting.

Downloads

Download data is not yet available.

References

J. Kokina and T. H. Davenport, "The Emergence of Artificial Intelligence: How Automation is Changing Auditing," J. Emerg. Technol. Account., vol. 14, no. 1, pp. 115-122, 2022.

M. L. Zhu et al., "Artificial intelligence in finance: A comprehensive survey," J. Financ. Data Sci., vol. 4, no. 2, pp. 125-140, 2023.

Y. Wang et al., "Deep learning-based credit scoring: A systematic literature review," Expert Syst. Appl., vol. 213, p. 118926, 2023.

S. Li et al., "Machine learning and financial statement fraud detection: A systematic review," J. Bus. Res., vol. 154, p. 113317, 2023.

Z. Jiang et al., "Applications of deep learning in stock market prediction: Recent progress," Expert Syst. Appl., vol. 184, p. 115537, 2022.

A. Farzindar and D. Inkpen, "Natural Language Processing for Social Media," Synth. Lect. Hum. Lang. Technol., vol. 13, no. 2, pp. 1-195, 2023.

X. Li et al., "A survey on sentiment analysis and opinion mining for social multimedia," Multimed. Tools Appl., vol. 78, no. 6, pp. 6939-6967, 2024.

T. Brown et al., "Language Models are Few-Shot Learners," in Advances in Neural Information Processing Systems, 2022, vol. 33, pp. 1877-1901.

L. Willcocks et al., "Robotic process automation: strategic transformation lever for global business services?," J. Inf. Technol., vol. 37, no. 1, pp. 20-38, 2022.

M. Lacity and L. Willcocks, "Robotic Process Automation at Telefónica O2," MIS Q. Exec., vol. 15, no. 1, pp. 21-35, 2023.

A. Syed et al., "Robotic Process Automation: Contemporary themes and challenges," Comput. Ind., vol. 115, p. 103162, 2024.

Y. Wu et al., "Automated financial report generation using natural language processing: A comprehensive survey," Expert Syst. Appl., vol. 168, p. 114509, 2022.

Deloitte, "Crunch time V: Finance 2025 (Our predictions)," Deloitte, 2023.

E. Brynjolfsson and A. McAfee, "The Business of Artificial Intelligence," Harv. Bus. Rev., vol. 95, no. 4, pp. 3-11, 2023.

K. C. Moffitt et al., "Robotic Process Automation for Auditing," J. Emerg. Technol. Account., vol. 15, no. 1, pp. 1-10, 2024.

Deloitte, "Artificial Intelligence: The next frontier for investment management firms," Deloitte, 2023.

KPMG, "Audit 2025: The future is now," KPMG, 2024.

S. Makridakis et al., "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLoS One, vol. 13, no. 3, p. e0194889, 2024.

Y. Gu et al., "Empirical asset pricing via machine learning," Rev. Financ. Stud., vol. 33, no. 5, pp. 2223-2273, 2023.

A. Bartov et al., "Artificial Intelligence in Asset Management," CFA Institute Research Foundation, 2022.

D. Bao et al., "Artificial Intelligence in Finance: A Review," Entropy, vol. 24, no. 11, p. 1675, 2022.

F. Petropoulos et al., "Forecasting: theory and practice," Int. J. Forecast., vol. 38, no. 3, pp. 705-871, 2023.

J.P. Morgan, "Annual Report 2023," J.P. Morgan Chase & Co., 2024.

Y. Liu et al., "Understanding the quality of financial data: A comprehensive survey," Data Knowl. Eng., vol. 134, p. 101908, 2023.

W. Zheng et al., "Big Data Analytics in Cloud Computing: A Comprehensive Survey," IEEE Access, vol. 10, pp. 4425-4444, 2022.

C. Rudin, "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead," Nat. Mach. Intell., vol. 1, no. 5, pp. 206-215, 2023.

PwC, "AI: Sizing the prize," PricewaterhouseCoopers, 2024.

S. Barocas et al., "Fairness and Machine Learning: Limitations and Opportunities," fairmlbook.org, 2023.

M. Chui et al., "Notes from the AI frontier: Insights from hundreds of use cases," McKinsey Global Institute, 2024.

Gartner, "Gartner Survey Shows 37 Percent of Organizations Have Implemented AI in Some Form," Gartner, 2023.

World Economic Forum, "The Future of Jobs Report 2023," World Economic Forum, 2023.

T. C. Redman, "Data's Credibility Problem," Harv. Bus. Rev., vol. 91, no. 12, pp. 84-88, 2022.

Downloads

Published

2024-09-27

Issue

Section

Research Articles

Categories

How to Cite

[1]
Y. Luan, “Application of Artificial Intelligence in Corporate Financial Accounting”, ijetaa, vol. 1, no. 8, pp. 9–13, Sep. 2024, doi: 10.62677/IJETAA.2408126.

Similar Articles

1-10 of 21

You may also start an advanced similarity search for this article.