The growing adoption of AI in finance raises urgent ethical challenges, including the need to ensure beneficence, non-maleficence, explicability, and privacy in automated decision-making systems. Traditional Requirements Engineering (RE) methods focus on functional and non-functional requirements but do not systematically support the elicitation and operationalization of ethical requirements. Ontology-Based Requirements Engineering (OBRE) offers a structured approach to bridge this gap by formalizing ethical principles into precise, actionable requirements. By providing a shared conceptual framework, OBRE enables Requirements Analysts to translate abstract ethical concepts into operational terms, detect conflicts among requirements, and ensure traceability throughout the system lifecycle. A case study of the BlueCrest algorithmic trading system is used in this chapter to illustrate how ontology-based approaches facilitate the integration of ethical considerations into financial AI systems, supporting stakeholder trust, regulatory compliance, and responsible innovation.