Algorithmic accountability and ethical AI frameworks for regulatory governance in financial technologies

The integration of Artificial Intelligence (AI) into financial technologies (FinTech) is reshaping the global financial landscape by enhancing efficiency, enabling predictive risk management, and automating regulatory compliance. Despite these advances, the growing reliance on opaque, data-driven algorithms raises fundamental concerns about accountability, fairness, and consumer protection. The absence of transparent mechanisms to explain or audit algorithmic decisions has generated skepticism among regulators and the public, particularly in sensitive areas such as credit scoring, fraud detection, and investment recommendations. This study examines how principles of algorithmic accountability and ethical AI can be systematically embedded into the governance of FinTech systems. It adopts an interdisciplinary approach, drawing on perspectives from computer science, financial regulation, and legal scholarship, to analyze existing ethical frameworks and identify their limitations. The paper proposes a lifecycle governance model that integrates continuous monitoring, bias mitigation, and explainability into the design and deployment of financial algorithms. The framework emphasizes regulatory tools such as adaptive oversight, algorithmic auditing, and regulatory sandboxes, while also highlighting the importance of stakeholder engagement and cross-disciplinary collaboration. By aligning technological innovation with ethical safeguards, the proposed model addresses the challenges of systemic risk, discrimination, and regulatory fragmentation. Ultimately, the study contributes a practical blueprint for balancing innovation with accountability, ensuring that AI in finance evolves in ways that are trustworthy, transparent, and socially responsible.