Ethical Challenges in Adoption of AI in Financial Services: A Conceptual Framework

Abstract

The rapid integration of artificial intelligence (AI) into financial services has transformed how institutions operate, assess risk, and engage with customers. While AI enhances efficiency, reduces costs, and supports financial inclusion, it also raises significant ethical concerns. This study develops a conceptual understanding of these challenges, focusing on key issues such as algorithmic bias, lack of transparency, data privacy, accountability, and their implications for trust and financial stability. Adopting a conceptual research design, the study synthesises recent literature to examine how ethical risks emerge across different stages of the AI lifecycle, from data collection to deployment and monitoring. Through thematic analysis, a multi-dimensional framework is proposed, integrating ethical risk dimensions, lifecycle stages, and governance mechanisms. The study also advances a set of propositions linking ethical factors with outcomes such as user trust, perceived risk, and AI adoption. The findings reveal that ethical challenges are highly interconnected, with fairness and transparency playing a central role in shaping trust and acceptance. The study highlights the importance of governance mechanisms, including explainable AI and ethical oversight, in mitigating risks. Overall, the research provides a foundation for responsible and sustainable AI adoption in financial services.