Organizational governance of artificial intelligence for model risk management in FinTech

Abstract

The adoption of Artificial Intelligence (AI) in the financial sector has become one of the core components of the modern financial infrastructure, having a profound impact on decision-making, fraud detection, risk management, financial forecasting, and portfolio optimization. However, with the growth in use of advanced and non-transparent AI models, there have been serious concerns about transparency, explainability, accountability, and regulatory compliance. The thesis focuses on AI-driven model risk in the financial and FinTech setting, with particular emphasis on the role of explainable and trustworthy AI in the area of governance, ethics, and compliance with regulatory principles. By applying a structured literature survey and critical thematic analysis of the existing academic literature, industry frameworks, and regulatory guidance, the study assesses current AI methods, explainability methods, and models of governance applied in financial decision-making systems. The results indicate the continuing consistent gap between the theoretical developments in Explainable Artificial Intelligence (XAI) and its application in actual financial practices. The identified gap due to technical complexity, limited scalability of explainability techniques, organizational issues, and the changing demands of regulations. The thesis concludes that effective integration of AI into the field of financial sector will require comprehensive governance framework that includes model risk management, explainability, ethics, and governance strategies across the AI life-cycle, which will improve accountability, strengthen trust, and support longterm sustainability.