Machine Learning in Corporate Financial Sustainability: A Critical Evaluation of Models Bias and Outcomes
DOI:
https://doi.org/10.54536/ajarai.v1i1.6814Keywords:
Algorithmic Bias, Corporate Financial Sustainability, ESG, Explainable Machine Learning, Machine LearningAbstract
The integration of machine learning (ML) into corporate financial sustainability (CFS) is a double-edged sword: while offering transformative potential for predictive analytics, risk modeling, and reporting efficiency, it also introduces significant risks of algorithmic bias, opacity, and erosion of accountability. This critical literature review synthesizes 31 peer-reviewed articles to evaluate ML's role in CFS contexts, moving beyond techno-optimism to foreground ethical and governance challenges. Our analysis reveals that current applications, such as ESG scoring, predictive CFS analytics, and automated reporting, often prioritize scalability and efficiency over validity, equity, and substantive performance, thereby risking “automated greenwashing” and reinforcing structural inequalities. In response, we propose an integrative, four-pillar framework for responsible ML deployment, emphasizing Transparent and Explainable Models, Bias-Audit and Ethical Governance, Integrated ESG-ML Reporting Standards, and Stakeholder-Inclusive ML Deployment. Institutional, technological, and cultural contexts moderate the effectiveness of these pillars. We argue that without deliberate governance, ML may undermine the very goals of sustainable value creation it seeks to advance. This review calls for interdisciplinary collaboration, standardized auditing protocols, and proactive regulation to align ML innovation with the long-term imperatives of CFS.
Downloads
References
Adams, C. A., & Whelan, G. (2009). Conceptualising future change in corporate sustainability reporting. Accounting, Auditing & Accountability Journal, 22(1), 118–143. https://doi.org/10.1108/09513570910923033
Aduloju, O. D., Adedotun, A. K., & Taiwo, A. A. (2025). An exploratory study on the use of robotics to enhance marketing strategies in business organizations: A case study of selected firms. American Journal of Data Science and Artificial Intelligence, 1(1), 27–35. https://doi.org/10.54536/ajdsai.v1i1.4933
Alipasa, C. D. L. (2026). The parents' perception on the extent of implementation of virtual learning: Basis for a faculty e learning training program. Perspectives in Sustainable Development Studies, 1(1), 1–11. https://doi.org/10.65401/642700
Allen, F., Qian, J., Shan, C., & Zhu, J. L. (2024). Dissecting the long-term performance of the Chinese stock market. The Journal of Finance, 79(2), 993–1054. https://doi.org/10.1111/jofi.13312
Atif, M., Nadarajah, S., & Richardson, G. (2023). Staggered adoption of stakeholder constituency statutes and corporate cash holdings in the U.S. Economic Modelling, 124, 106325. https://doi.org/10.1016/j.econmod.2023.106325
Bernardini, E., Fanari, M., Foscolo, E., & Ruggiero, F. (2024). Environmental data and scores: Lost in translation. Corporate Social Responsibility and Environmental Management, 31(5), 4796–4818. https://doi.org/10.1002/csr.2829
Blue, G., Chahrdahcheriki, M., Rezaee, Z., & Khotanlou, M. (2025). A model for predicting creative accounting in emerging economies. International Journal of Accounting and Information Management, 33(1), 1–31. https://doi.org/10.1108/IJAIM-09-2023-0240
Cheng, L. T. W., Cheong, T. S., Wojewodzki, M., & Chui, D. K. H. (2025). The effect of ESG divergence on the financial performance of Hong Kong-listed firms: An artificial neural network approach. Research in International Business and Finance, 73, 102616. https://doi.org/10.1016/j.ribaf.2024.102616
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101
Downing, N. J. (2025). Missing value imputation in environmental, social, and governance data: An impact on emissions scores. Finance Research Letters, 85, 107818. https://doi.org/10.1016/j.frl.2025.107818
Fang, J., Li, J., & Bi, P. (2025). Green paradox of AI: Short-term pain and long-term redemption—The two faces of Chinese enterprises’ sustainable development. Finance Research Letters, 86, 108544. https://doi.org/10.1016/j.frl.2025.108544
Ferro, A., Marazzina, D., & Stocco, D. (2025). Uncovering ESG ratings: The (im)balance of aspirational and performance features. Corporate Social Responsibility and Environmental Management, 32(5), 5895–5917. https://doi.org/10.1002/csr.70007
Freeman, R. E. (1984). Strategic management: A stakeholder approach. Pitman.
Gribnau, H. (2025). Sustainable tax governance: A shared responsibility. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5065836
Hąbek, P. (2025). Evaluating ESG software solutions for sustainability reporting in the manufacturing sector. Management Systems in Production Engineering, 33(3), 420–432. https://doi.org/10.2478/mspe-2025-0041
Hsieh, M. Y. (2024). An empirical investigation into the enhancement of decision-making capabilities in corporate sustainability leadership through Internet of Things (IoT) integration. Internet of Things, 28, 101382. https://doi.org/10.1016/j.iot.2024.101382
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Khan, S., & Gupta, S. (2025). Boosting the efficacy of green accounting for better firm performance: Artificial intelligence and accounting quality as moderators. Meditari Accountancy Research, 33(2), 472–496. https://doi.org/10.1108/MEDAR-02-2024-2379
Knoepfel, I. (2001). Dow Jones Sustainability Group Index: A global benchmark for corporate sustainability. Corporate Environmental Strategy, 8(1), 6–15. https://doi.org/10.1016/s1066-7938(00)00089-0
Li, J., Wu, T., Hu, B., Pan, D., & Zhou, Y. (2025). Artificial intelligence and corporate ESG performance. International Review of Financial Analysis, 102, 104036. https://doi.org/10.1016/j.irfa.2025.104036
Lin, B. W., Chen, C. J., & Wu, H. L. (2006). Patent portfolio diversity, technology strategy, and firm value. IEEE Transactions on Engineering Management, 53(1), 17–26. https://doi.org/10.1109/tem.2005.861813
Martin, G., Farndale, E., Paauwe, J., & Stiles, P. G. (2016). Corporate governance and strategic human resource management: Four archetypes and proposals for a new approach to corporate sustainability. European Management Journal, 34(1), 22–35. https://doi.org/10.1016/j.emj.2016.01.002
Naveed, K., Farooq, M. B., Zahir-ul-Hassan, M. K., & Rauf, F. (2025). AI adoption, ESG disclosure quality and sustainability committee heterogeneity: Evidence from Chinese companies. Meditari Accountancy Research, 33(2), 708–732. https://doi.org/10.1108/MEDAR-02-2024-2374
Sandberg, H., Alnoor, A., & Tiberius, V. (2023). Environmental, social, and governance ratings and financial performance: Evidence from the European food industry. Business Strategy and the Environment, 32(4), 2471–2489. https://doi.org/10.1002/bse.3259
Schneider, J. C., & Schwab, B. (2025). Advancing loss reserving: A hybrid neural network approach for individual claim development prediction. Journal of Risk and Insurance, 92(2), 389–423. https://doi.org/10.1111/jori.12501
Seele, P. (2017). Predictive sustainability control: A review assessing the potential to transfer big data driven ‘predictive policing’ to corporate sustainability management. Journal of Cleaner Production, 153, 673–686. https://doi.org/10.1016/j.jclepro.2016.10.175
Shabbir, M. S. (2025). Corporate sustainability reimagined: A bibliometric–systematic literature review of governance, technology, and stakeholder-driven strategies for SDG impact. Business Strategy and the Environment, 34(7), 9203–9222. https://doi.org/10.1002/bse.70070
Shilbayeh, S. A., & Grassa, R. (2024). Creditworthiness pattern prediction and detection for GCC Islamic banks using machine learning techniques. International Journal of Islamic and Middle Eastern Finance and Management, 17(2), 345–365. https://doi.org/10.1108/IMEFM-02-2023-0057
Spagnuolo, F., Casciello, R., Martino, I., & Meucci, F. (2025). Exploring the impact of artificial intelligence on the pursuit of SDGs: Evidence from European state-owned enterprises. Corporate Social Responsibility and Environmental Management, 32(2), 1987–2001. https://doi.org/10.1002/csr.3047
Strätling, R. (2007). The legitimacy of corporate social responsibility. Corporate Ownership and Control, 4(4), 80–88. https://doi.org/10.22495/cocv4i4p6
Suárez Giri, F., & Sánchez-Chaparro, T. (2024). Unveiling the blackbox within ESG ratings' blackbox: Toward a framework for analyzing AI adoption and its impacts. Business Strategy and Development, 7(4), e70038. https://doi.org/10.1002/bsd2.70038
Subramaniam, R. K., Samuel, S. D., Seera, M., & Alam, N. (2024). Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climate. Sustainable Futures, 8, 100329. https://doi.org/10.1016/j.sftr.2024.100329
Sulimany, H. G., Ramakrishnan, S., Chaudhry, A., & Bazhair, A. H. (2021). Impact of corporate governance and financial sustainability on shareholder value. Estudios de Economía Aplicada, 39(4). https://doi.org/10.25115/eea.v39i4.4318.
Vintilă, G., Onofrei, M., & Vintilă, A. I. (2025). Boosting IT companies’ performance through corporate governance standards: An empirical analysis. Oeconomia Copernicana, 16(2), 761–809. https://doi.org/10.24136/oc.3627
Wibbens, P. D., & Siggelkow, N. (2020). Introducing LIVA to measure long‐term firm performance. Strategic Management Journal, 41(5), 867–890. https://doi.org/10.1002/smj.3114
Zhai, S., & Zhang, Z. (2023). Read the news, not the books: Forecasting firms' long-term financial performance via deep text mining. ACM Transactions on Management Information Systems, 14(1). https://doi.org/10.1145/3533018
Zhao, J., & Gómez Fariñas, B. (2023). Artificial intelligence and sustainable decisions. European Business Organization Law Review, 24(1), 1–39. https://doi.org/10.1007/s40804-022-00262-2
Zhou, Z., Zhou, X., Zhang, X., & He, Q. (2025). Not all sparks ignite the same flame: Firm AI innovation and ESG performance. Journal of Business Research, 201, 115738. https://doi.org/10.1016/j.jbusres.2025.115738
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Zulkiffly Baharom (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
