Artificial intelligence (AI) empowered the transformation of decision-making processes for lending services, delivering improved efficiency, scalability, and precision. However, the adoption of AI in loan origination and application processing has introduced significant ethical challenges, including recognizing biases, fairness, transparency, reliability, and accountability. The paper identifies primary challenges in automated lending services (ALS), AI-enabled decision-making, and the deriving of AI governance practices. This paper proposes a deterministic framework (DF) designed to systematically identify and address the ethical dimensions of AI in lending services. The DF spans comprehensive mechanisms encompassing data collection, preprocessing, model development, deployment, monitoring, and governance. Core ethical dimensions of explainability, transparency, and equitable outcomes are recognized within the governance lifecycle stages. The DF continuously integrates novel industry regulatory standards and governance methodologies to identify, measure, and mitigate ethical risks, ensuring operational efficiency and adherence to ethical principles. This research provides a structured approach grounded in deterministic methods, enabling measurable, repeatable, and auditable business processes to enable trust and accountability in AI-driven ALS. An empirical case study focusing on ALS for students and their families is presented to evaluate the DF’s applicability and effectiveness. The findings provide actionable insights for financial institutions, policymakers, and technologists seeking to implement ethical AI practices, strengthen risk management, and deliver equitable and accountable lending services to diverse populations.