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Artificial Intelligence-driven corporate finance: enhancing efficiency and decision-making through machine learning, natural language processing, and robotic process automation in corporate governance and sustainability
Open accessAbstract
This research paper delves into the transformative possibilities of Artificial Intelligence (AI) within corporate finance, specifically focusing on its role in improving efficiency and decision-making processes. Through the utilization of machine learning, natural language processing (NLP), and robotic process automation (RPA), AI introduces innovative methods for enhancing corporate governance and sustainability practices. In the contemporary business landscape, corporations encounter mounting pressure to streamline operations while simultaneously addressing concerns regarding environmental, social, and governance (ESG) issues. Conventional finance methodologies often struggle to efficiently handle large volumes of data and extract actionable insights promptly. However, AI presents a shift in paradigm by enabling automated data analysis, recognizing patterns, and conducting predictive modeling, thus enabling finance professionals to make data-informed decisions swiftly and accurately. Machine learning algorithms play a pivotal role in detecting patterns and correlations within financial data, facilitating proactive risk management and strategic planning. Additionally, NLP technologies facilitate the extraction of valuable insights from unstructured data sources like regulatory filings, news articles, and social media, thereby enabling informed decision-making in corporate governance and sustainability endeavors. Moreover, RPA simplifies repetitive tasks and workflows, thereby reducing operational expenses and freeing up human resources for more strategic pursuits. Through the automation of routine processes such as data entry, reconciliation, and reporting, RPA enhances operational efficiency and ensures adherence to regulatory standards. Through the adoption of AI technologies, corporations can unlock novel avenues for innovation, optimize resource allocation, and promote sustainable growth within today's dynamic business milieu.
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“Gradient-boosted trees often achieve competitive or superior predictive accuracy in ESGrelated tasks.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [49]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“From an SBM perspective, this static orientation may reinforce compliancedriven ESG optimization rather than long-term sustainability transformation [49].”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [49]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These models effectively capture non-linear interactions among ESG variables and demonstrate strong performance even when datasets contain missing or imperfect information.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [49]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“On the other side, tree-based models struggle with unstructured textual data, limiting their ability to capture qualitative governance and social signals unless combined with NLP.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [49]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Additionally, these models are typically trained on historical data and thus exhibit limited adaptability to dynamic ESG standards and evolving regulatory environments.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [49]
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“Their ability to rank feature importance provides partial interpretability, which is advantageous compared to neural networks.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [49]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Tree-based ML models are frequently applied in ESG risk classification and CSR adoption prediction due to their robustness to noisy and heterogeneous data.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [49]
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Research works printed but not linked: 31
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Cited by
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Mijit, R., Hu, Q., Xu, J., & Ma, G. (2025) · Energy Economics · doi:10.1016/j.eneco.2025.108507 · Cited by 114
Adelakun, B. O., Antwi, B. O., Ntiakoh, A., & Eziefule, A. O. (2024) · Finance & Accounting Research Journal · doi:10.51594/farj.v6i6.1234 · Cited by 74
Theodorakopoulos, L., Theodoropoulou, A., Tsimakis, A., & Halkiopoulos, C. (2025) · Electronics · doi:10.3390/electronics14091754 · Cited by 48
Ding, W., Abdel‐Basset, M., Ali, A. M., & Moustafa, N. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112663 · Cited by 46
Gupta, S., Wang, Y., Patel, P., & Czinkota, M. (2025) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.102871 · Cited by 39
Metadata
- Authors
- Rane, N. L., Choudhary, S. P., & Rane, J.
- Affiliations
- University of Mumbai (IN)
- Corresponding author
- Rane, Nitin Liladhar
- Venue
- Studies in Economics and Business Relations
- Publisher
- SABA Publishing
- Published
- 2024-06-01
- Volume
- 5
- Issue
- 2
- Pages
- 1-22
- ISSN
- 2709-670X (ISSN-L)
- OpenAlex
- W4399265647
- Cited by
- 81
- References
- 0
- Citations by year
- 2026: 16 · 2025: 46 · 2024: 19
- FWCI
- 12.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Studies in Economics and Business Relations (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Management Information Systems › Robotic Process Automation Applications
- Keywords
- machine learning, natural language processing, robotic process automation, corporate governance, corporate sustainability, ESG, financial decision-making, risk management, predictive modeling, unstructured data analysis, regulatory submissions, operational efficiency, financial reporting automation
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