Week 3 Discussion Response- Managerial Finance

2

Week 3 Discussion- Managerial Finance

Student’s First Name, Middle Initial(s), Last Name

Institution Affiliation

Course Name and Number

Instructor’s Name and Title

Assignment Due Date

Week 3 Discussion- Managerial Finance

Ethical financial management can also be supported using generative artificial intelligence to filter datasets, uncovering trends that humans can overlook, and to offer situations of informed decision-making. Generative AI models help the leaders in managerial finance by creating simulated effects of various factors, such as prices, dividend policies, and leverage, under different economic conditions. According to Zada et al. (2024), such a benefit is essential as it improves their decision-making processes and results in the managers upholding their fiduciary obligations to stakeholders. Natural language alerts can be generated on the same tools to detect transactions that are out of the expected pattern, thus enhancing the detection of fraud and monitoring against breaches of compliance in real-time and reducing the chances of abuse (Owolabi et al., 2024). The resultant systems of generation establish themselves as a decision support partner and do not substitute professional judgment.

As a financial manager, my approach to maintenance would be to assess and rank responsible projects with generative artificial intelligence. I would utilize the generative AI system to generate environmental, social, and governance reports, community impact reports, and climate risk data, and rank projects based on the anticipated financial payback and their contribution to social outcomes (Davidescu et al., 2025). Another thing I would request the model to do would be to write stakeholder communication materials, which would indicate how the decisions made in capital allocation communicate organizational values and long-term sustainability objectives, after which I would edit the language to make it clear and integrity-based. By so doing, generative tools would be helpful in transforming abstract commitments to positive social change into concrete, well-grounded decisions to invest, which connect strategy, ethics, and impact.

However, I would still be wary of risks when I trust in generative artificial intelligence in my financial management. The biased effects of algorithms and inadequate training information can continue to reinforce inequalities in giving credits, risk scores, or even access to sustainable investment services, which is counterproductive to the intentions of ethics and social responsibility (Owolabi et al., 2024). Accept the outcomes, as all facts undermine accountability. Respects to privacy, safety, and probable loss of professional skills whereby staff over-depend on the automated suggestions are also raised. Consequently, to be responsible, there must be good governance, human control, and morality in the way in which generative systems can influence financial decisions.

References

Davidescu, A. A., Bîrlan, I., Manta, E. M., & GEAMBAȘU, C. M. (2025). Artificial Intelligence in ESG and Sustainable Finance: A Bibliometric Analysis of Research Trends. In  Proceedings of the International Conference on Business Excellence (Vol. 19, No. 1, pp. 1506-1517). Sciendo. https://doi.org/10.2478/picbe-2025-0117

Owolabi, O. S., Uche, P. C., Adeniken, N. T., Ihejirika, C., Islam, R. B., Chhetri, B. J. T., & Jung, B. (2024). Ethical implications of artificial intelligence (AI) adoption in financial decision-making and Information Science,  17(1), 1–49. https://doi.org/10.5539/cis.v17n1p49

Zada, M., Khan, S., Mehmood, S., & Contreras-Barraza, N. (2024). Generative artificial intelligence in FinTech: Applications, environmental, social, and governance considerations, and organizational performance: The moderating role of ethical dilemmas.  Oeconomia Copernicana,  15(4). https://doi.org/10.24136/oc.3323