This paper examines the emerging field of ethics-based auditing in AI-driven financial systems, addressing the critical need for systematic evaluation of algorithmic fairness, transparency, and accountability in the rapidly evolving financial sector. As artificial intelligence adoption accelerates across credit assessment, fraud detection, and investment services, these systems introduce novel ethical challenges including potential algorithmic bias, decision opacity, and accountability gaps. The research analyzes comprehensive frameworks for conducting ethics-based auditing, detailing specific methodologies for testing fairness, evaluating transparency, assessing accountability mechanisms, and conducting privacy impact assessments. Through examination of organizational implementation models and case studies across various financial applications, the paper identifies practical challenges including skill gaps, regulatory uncertainty, and integration with legacy systems. The study demonstrates how structured ethics-based auditing can significantly mitigate risks while fostering stakeholder trust, with documented improvements in reducing disparate impact, enhancing explanation quality, and creating meaningful human oversight. The research concludes that proactive development of ethics-based auditing capabilities is increasingly essential for responsible AI governance in financial services, offering recommendations for short-term actions, medium-term infrastructure development, and long-term strategic positioning as organizations navigate this complex ethical landscape.