Vol. 2, Issue 2, Part A (2025)

The increasing adoption of artificial intelligence (AI) in financial technology (FinTech) systems has transformed credit evaluation, fraud detection, and customer analytics, but it has also intensified concerns about fairness, accountability, and regulatory compliance. This study develops and validates a Responsible AI Governance (RAIG) framework tailored specifically for FinTech environments. The research integrates insights from global standards, including the EU Artificial Intelligence Act, NIST AI Risk Management Framework, OECD AI Principles, MAS FEAT Guidelines, and IOSCO-EBA supervisory expectations, to create a structured governance model emphasizing transparency, explainability, and ethical assurance across the AI lifecycle. A mixed-method design was employed, combining systematic literature and regulatory review, expert validation through a Delphi process, and comparative evaluation across two FinTech use cases AI-based credit scoring and fraud detection. Statistical analysis demonstrated that the RAIG framework significantly improved model fairness, compliance readiness, and drift detection without compromising accuracy. Quantitatively, fairness indicators such as statistical parity difference and equal opportunity difference improved by over 40%, while compliance readiness increased from 0.54 to 0.88, and time to drift detection reduced by more than 60%. Qualitative findings revealed that structured lifecycle governance, when embedded into institutional culture, fosters long-term trust, mitigates regulatory risk, and enhances system accountability. The results confirm that responsible AI practices can coexist with innovation, provided governance mechanisms are integrated from design to deployment. The study concludes with actionable recommendations for policymakers, regulators, and FinTech organizations, including the institutionalization of AI ethics boards, mandatory lifecycle audits, human-in-the-loop oversight, and cross-sector collaboration for harmonized standards. Overall, the RAIG framework offers a scalable, operational blueprint for ensuring transparency, fairness, and accountability in AI-driven financial systems, thereby aligning technological progress with public interest and global regulatory objectives.