Ethical Risks of Artificial Intelligence in Financial Management: Identification and Governance Based on Stakeholder Theory

Abstract

The in-depth application of artificial intelligence (AI) in the field of financial management (such as intelligent credit scoring and risk control) has significantly improved operational efficiency, but has also highlighted ethical risks such as algorithmic bias and data privacy breaches. Based on stakeholder theory, this paper takes banks and Internet financial enterprises as research objects to systematically identify the manifestations and formation mechanisms of AI ethical risks, and constructs a “technology-institution-ethics” trinity governance framework. The study finds that AI ethical risks are essentially the result of an imbalance in the interests of stakeholders (financial institutions, users, regulators, and technology providers). Algorithmic bias stems from historical discrimination in training data and the “black box” nature of algorithms, while privacy breaches are related to deficiencies in data governance and regulatory lag. Practices such as Microsoft Azure’s ethical assessment matrix and the European Union’s AI Act demonstrate that the synergy of technological prevention and control, institutional constraints, and ethical consensus can effectively mitigate risks. This paper provides theoretical support for the ethical governance of financial AI and offers references for corporate compliance practices.