Data Governance, Bias, and Ethical Risk in AI-Driven Financial Services

Abstract

The use of Artificial Intelligence (AI) in financial services has revolutionized decision-making in various sectors, including credit scoring, fraud detection, risk management, customer services, and investment analysis. However, even though there have been tremendous developments, increased reliance on AI has sparked serious issues surrounding data governance, algorithmic biases, and ethical risks. In this paper, the link between data governance and ethical issues in AI is examined. Poor data quality, lack of data governance mechanisms, and biased data sets will be identified as sources of discrimination, lack of transparency, and unethical risks in financial services firms. The paper examines other ways through which AI algorithms are biased in terms of their impacts on issues relating to financial inclusion, user trust, and governance. Furthermore, ethical challenges such as privacy intrusion, failure to provide any form of explainability in the process, and the black box phenomenon, among others, are identified. Using relevant literature and current frameworks guiding the governance of AI systems, the research focuses on the importance of responsible AI practices that include fairness auditing, explainable AI, and effective data management, among others. From the above findings, it becomes clear that effective data governance is crucial in addressing ethical challenges and making sure that AI technology operates in an ethical way within the banking industry. This paper reveals that there is a need for banks, regulators, and policymakers to implement effective governance frameworks and policies so as to achieve the right balance between innovation and responsibility.