Application of Artificial Intelligence in Corporate Financial Accounting

Yun Luan

doi:10.62677/IJETAA.2408126

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.

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References

R. Seethamraju and A. Hecimovic, "Adoption of artificial intelligence in auditing: An exploratory study," Australian Journal of Management, vol. 48, no. 4, pp. 780-800, 2023.

G. Almufadda and N. A. Almezeini, "Artificial intelligence applications in the auditing profession: a literature review," J. Emerg. Technol. Account., vol. 19, no. 2, pp. 29-42, 2022.

Y. Hayashi, "Emerging trends in deep learning for credit scoring: A review," Electronics, vol. 11, no. 19, p. 3181, 2022.

J. P. Noriega, L. A. Rivera, and J. A. Herrera, "Machine learning for credit risk prediction: A systematic literature review," Data, vol. 8, no. 11, p. 169, 2023.

W. Jiang, "Applications of deep learning in stock market prediction: recent progress," Expert Systems with Applications, vol. 184, p. 115537, 2021.

J. Camacho-Collados et al., "TweetNLP: Cutting-edge natural language processing for social media," arXiv preprint arXiv:2206.14774, 2022.

E. O. Omuya, G. Okeyo, and M. Kimwele, "Sentiment analysis on social media tweets using dimensionality reduction and natural language processing," Engineering Reports, vol. 5, no. 3, p. e12579, 2023.

X. Liu et al., "Large language models are few-shot health learners," arXiv preprint arXiv:2305.15525, 2023.

S. Bu, U. A. Jeong, and J. Koh, "Robotic process automation: A new enabler for digital transformation and operational excellence," Business Communication Research and Practice, vol. 5, no. 1, pp. 29-35, 2022.

D. Pramod, "Robotic process automation for industry: adoption status, benefits, challenges and research agenda," Benchmarking: An International Journal, vol. 29, no. 5, pp. 1562-1586, 2022.

D. Fernandez and A. Aman, "The challenges of implementing robotic process automation in global business services," International Journal of Business and Society, vol. 22, no. 3, pp. 1269-1282, 2021.

R. Goyal, P. Kumar, and V. P. Singh, "A systematic survey on automated text generation tools and techniques: application, evaluation, and challenges," Multimedia Tools and Applications, vol. 82, no. 28, pp. 43089-43144, 2023.

B. Gao and V. Balyan, "Construction of a financial default risk prediction model based on the LightGBM algorithm," Journal of Intelligent Systems, vol. 31, no. 1, pp. 767-779, 2022.

J. P. Bharadiya, R. K. Thomas, and F. Ahmed, "Rise of artificial intelligence in business and industry," Journal of Engineering Research and Reports, vol. 25, no. 3, pp. 85-103, 2023.

A. Dahiyat, "Robotic process automation and audit quality," Corporate Governance and Organizational Behavior Review, vol. 6, no. 1, pp. 160-167, 2022.

A. Naqvi, Artificial Intelligence for Asset Management and Investment: A Strategic Perspective. John Wiley & Sons, 2021.

L. Ferry, V. S. Radcliffe, and I. Steccolini, "The future of public audit," Financial Accountability & Management, vol. 38, no. 3, pp. 325-336, 2022.

S. Makridakis et al., "Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward," Journal of the Operational Research Society, vol. 74, no. 3, pp. 840-859, 2023.

W. Drobetz and T. Otto, "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, vol. 22, no. 7, pp. 507-538, 2021.

L. Rampini and F. R. Cecconi, "Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities," Journal of Information Technology in Construction, vol. 27, pp. 884-913, 2022.

W. Lakhchini, R. Wahabi, and M. El Kabbouri, "Artificial intelligence & machine learning in finance: A literature review," International Journal of Accounting, Finance, Auditing, Management and Economics, 2022.

M. Hamoudia, S. Makridakis, and E. Spiliotis, Eds., Forecasting with Artificial Intelligence: Theory and Applications. Springer Nature, 2023.

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

M. U. Hadi et al., "Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects," Authorea Preprints, vol. 1, no. 3, pp. 1-26, 2023.

B. Berisha, E. Mëziu, and I. Shabani, "Big data analytics in Cloud computing: an overview," Journal of Cloud Computing, vol. 11, no. 1, p. 24, 2022.

U. Czerwinska, "Interpretability of Machine Learning Models: How Can One Explain Machine Learning Models?," in Applied Data Science in Tourism: Interdisciplinary Approaches, Methodologies, and Applications, Cham: Springer International Publishing, 2022, pp. 275-303.

X. Li, A. Sigov, L. Ratkin, L. A. Ivanov, and L. Li, "Artificial intelligence applications in finance: a survey," Journal of Management Analytics, vol. 10, no. 4, pp. 676-692, 2023.

S. Shi, R. Tse, W. Luo, S. D'Addona, and G. Pau, "Machine learning-driven credit risk: a systemic review," Neural Computing and Applications, vol. 34, no. 17, pp. 14327-14339, 2022.

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

V. Truong, "Understanding Opinions and Emotions of Generative Artificial Intelligence Assets Using the Gartner Hype Cycle and the Kübler-Ross Change Curve," in World Congress on Engineering Asset Management, Cham: Springer Nature Switzerland, Oct. 2023, pp. 13-30.

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

Y. Wang, L. Zhang, Y. Yao, and Y. Fu, "How to trust unlabeled data? instance credibility inference for few-shot learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 10, pp. 6240-6253, 2021.