Ai Governance and Risk management
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As Artificial Intelligence (AI) becomes fundamentally embedded within the financial services industry, the complexity of systemic risks—ranging from algorithmic opacity to regulatory non- compliance—has escalated significantly. While traditional financial institutions rely on technical oversight, current literature highlights a critical operational gap between high-level, conceptual AI ethical principles and the practical execution of traditional Model Risk Management (MRM). To address this disconnect, this study utilizes a qualitative, exploratory research design to synthesize current risk management paradigms with emerging global regulatory frameworks, such as the EU AI Act. The research constructs and proposes a dual-pillar theoretical oversight framework that bridges this gap. The first tier, AI Governance (the "Map"), establishes the strategic boundaries of ethical data usage, legal compliance, and corporate accountability. The second tier, Model Risk Management (the "Mechanic"), enforces the technical realities of algorithmic validation, continuous bias testing, and performance drift monitoring. Ultimately, this study concludes that by integrating this two-tiered governance framework throughout the entire AI system lifecycle, financial organizations can transition from reactive, isolated compliance to proactive, enterprise- wide risk mitigation, thereby securing both institutional trust and sustainable technological innovation
Keywords
AI Governance, Algorithmic Bias, Artificial Intelligence (AI), Explainable AI (XAI), Financial Technology (FinTech), Model Risk Management (MRM), Regulatory Compliance, Risk Mitigation