AI-Driven Governance Models for Autonomous FinTech Systems: Challenges and Frameworks

Abstract

The rise of autonomous FinTech systems powered by artificial intelligence (AI) has revolutionized financial services, enabling real-time decision-making, intelligent advisory platforms, and scalable automation across diverse functions such as credit scoring, fraud detection, and risk assessment. However, this transformation presents significant governance challenges. With generative AI, agentic systems, and decentralized infrastructures becoming integral to financial technologies, the need for robust AI-driven governance frameworks is more urgent than ever. This paper explores the intersection of AI and financial governance, examining how advanced algorithms can both introduce risk and offer mechanisms to mitigate it through dynamic controls, compliance automation, and ethical oversight.

The study proposes a multi-layered governance framework integrating regulatory compliance, risk modeling, explainability, and human-in-the-loop architectures. Emphasis is placed on adaptive risk assessment, trust in AI systems, and the alignment of autonomous decision-making with institutional governance objectives. Furthermore, the paper examines use cases such as fraud detection, AI-based auditing, and compliance workflows across decentralized finance (DeFi) ecosystems. Challenges including data sovereignty, accountability in agentic AI, and model drift in evolving market conditions are also addressed.

This work contributes a structured roadmap for implementing AI-driven governance models tailored for FinTech environments, emphasizing modularity, transparency, and auditability. Future research directions include federated governance mechanisms, self-regulating agents, and real-time compliance validation through blockchain-backed smart contracts. The findings aim to support FinTech institutions and regulators in building resilient, secure, and ethically aligned autonomous financial systems.