Core Concepts of Financial Reporting Automation in Corporations
Keywords:
financial reporting automation, artificial intelligence, RPA, machine learning, natural language processing, digital transformation, corporate finance, template-based report generation, AI ethics, complianceAbstract
The article presents a theoretical overview of the core concepts of financial reporting automation in corporations, with a focus on RPA, AI, ML, and NLP technologies. The study is conducted within an interdisciplinary paradigm that integrates digital finance, corporate governance, accounting, information technology, and regulatory compliance practice. The methodological basis is a qualitative comparative content analysis of domestic and international publications addressing the application of intelligent systems in financial modeling, variance analysis, planning, and report preparation. Automation approaches are identified and classified according to cognitive complexity, system architecture, and the degree of human involvement. Three analytical tables are provided: examples of AI use in auditing, a comparative review of the benefits and risks of RPA implementation, and the challenges and opportunities of AI in the financial domain. Based on empirical and conceptual data, the article demonstrates the effectiveness of a comprehensive “RPA + AI + Human-in-the-loop” model, ensuring interpretability, resilience, and regulatory compliance of financial reporting. The study highlights limitations related to the insufficient cognitive flexibility of RPA, algorithmic bias risks in AI, and the shortage of digital competencies among personnel. The article will be of interest to professionals in corporate finance, accounting process digitalization, auditors, developers of automated reporting systems, and executives responsible for financial function transformation in the digital economy.
References
[1]. Alao, O. B., Dudu, O. F., Alonge, E. O., & Eze, C. E. (2024). Automation in financial reporting: A conceptual framework for efficiency and accuracy in U.S. corporations. Global Journal of Advanced Research and Reviews, 2(2), 40–50. https://doi.org/10.58175/gjarr.2024.2.2.0057
[2]. Farea, M. M., Al-Ifan, B., Al-Dubai, M. M. M., & Bani Ahmad, A. (2024, June). Intelligent automation in accounting and financial reporting. Journal of Tianjin University Science and Technology, 57(06). https://doi.org/10.5281/zenodo.12516114
[3]. Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Challenges and opportunities for artificial intelligence in auditing: Evidence from the field. International Journal of Accounting Information Systems, 56, 100734. https://doi.org/10.1016/j.accinf.2025.100734
[4]. Noah, A., Oladele, S., & Bello, B. (2025, April 15). The strategic implications of automated financial reporting for business agility and decision-making. ResearchGate. https://www.researchgate.net/publication/390771399_The_Strategic_Implications_of_Automated_Financial_Reporting_for_Business_Agility_and_Decision-Making
[5]. Oesch, D., & Walser, T. (2025). The impact of automation on firms' reporting quality. Journal of Corporate Finance, 92, 102683. https://doi.org/10.1016/j.jcorpfin.2024.102683
[6]. Singh, A. (2025, March). The future of accounting: How AI and automation are changing the profession. International Journal for Multidisciplinary Research, 7(2). https://doi.org/10.36948/ijfmr.2025.v07i02.39838
[7]. Tabassum, S. (2025, April). Robotic Process Automation (RPA) in accounting: Studying the impact and implementation for automating repetitive tasks (Bachelor’s thesis). University of Dhaka. https://doi.org/10.13140/RG.2.2.12984.87043
[8]. Tan, X. W., & Kok, S. (2024). Explainable risk classification in financial reports (arXiv preprint arXiv:2405.01881). ICIS 2023 Proceedings. https://doi.org/10.48550/arXiv.2405.01881
[9]. Tian, Y.-E., Tang, Y.-C., Wang, K.-D., Yen, A.-Z., & Peng, W.-C. (2025). Template-based financial report generation in agentic and decomposed information retrieval (arXiv preprint arXiv:2504.14233). SIGIR 2025, short paper track. https://doi.org/10.48550/arXiv.2504.14233
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