Explainable and Ethical Artificial Intelligence for Financial Data Stewardship

Abstract

The expansion of artificial intelligence in finance has created a tension between predictive efficiency and accountable decision-making. Financial institutions can now automate classification, risk assessment, customer screening, fraud detection, and compliance monitoring, yet these systems may also obscure reasoning, reproduce historical bias, threaten privacy, and weaken meaningful human control. This review examines explainability, fairness, privacy, security, auditability, and accountability as essential components of financial data stewardship. Drawing on the evidence base reported in the uploaded systematic review, it argues that ethical artificial intelligence cannot be added after model development as a separate checklist. It must be embedded in data governance, model design, validation, deployment, and institutional oversight. The article distinguishes technical explanation from meaningful explanation, discusses the limits of human-in-the-loop arrangements, and evaluates governance measures such as model inventories, documentation, access control, impact assessment, independent validation, audit trails, challenge rights, and continuous monitoring. It also outlines policy actions at international, sectoral, and organisational levels. The review concludes that responsible financial artificial intelligence requires institutions to treat legitimacy, transparency, and customer protection as performance requirements alongside accuracy and efficiency.