Regulating Artificial Intelligence in Finance: Lessons from Singapore

Artificial Intelligence (AI) is rapidly transforming the global financial sector, with widespread adoption across banking, insurance, and other financial services. Singapore exemplifies this trend. Its major financial institutions, including DBS, OCBC, and UOB, have extensively integrated AI and Generative AI (GenAI) to enhance efficiency, productivity, and customer experience. However, the growing use of AI in finance also introduces significant risks related to transparency, data privacy, cybersecurity, fairness, accountability, GenAI hallucinations, and deepfakes. This paper examines Singapore's regulatory approach to governing AI in finance, anchored by the Monetary Authority of Singapore's (MAS) multi-layered strategy that combines principles-based regulation, collaborative industry engagement, supervisory technology (SupTech) adoption, market-enabling measures, and deliberate regulatory gradualism. While Singapore's framework, comprising the FEAT Principles, Project MindForge, and the MAS Guidelines on AI Risk Management, provides a strong foundation, this paper suggests areas for improvement. These include elevating guidelines into enforceable codes, addressing third-party AI risks, leveraging SupTech capabilities, establishing a dedicated AI regulatory sandbox, and ensuring robust enforcement and international cooperation. Drawing lessons from Singapore's experience, this paper argues that effective AI governance in finance requires not a singular approach but a calibrated, adaptive combination of strategies suited to each jurisdiction's institutional context and evolving technological landscape. Ultimately, responsible AI governance demands a multi-layered regulatory ecosystem, and the institutional capacity to evolve alongside the technology it seeks to govern.