Japan’s FSA Expands AI Use in Supervision

Japan’s Financial Services Agency (FSA) is expanding its use of AI in supervision and monitoring as part of a broader shift away from checklist-based oversight toward more qualitative assessments of financial institutions and their business viability, as reported by Regulation Asia. 

FSA Commissioner Yutaka Ito, who also serves as the agency’s Chief AI Officer, said AI would take on routine administrative tasks and help process large document sets, allowing staff to spend more time on supervisory engagement and on-site work. “We want to make clerical work as efficient as possible and let AI handle what AI can do,” Ito said.

The FSA’s use of AI forms part of a broader change in supervisory philosophy. Rather than relying primarily on checks against internal manuals, the regulator is moving toward assessments of institutions’ business viability, their contribution to regional economies, and their support for client growth. AI is intended to make data and document analysis more efficient, while supervisors can still turn to hearings and inspections where the data alone does not provide a clear picture of risk.

Earlier this year, Regulation Asia also reported that the FSA was promoting a flexible “agile governance” approach to AI use by financial institutions, under which firms would continually reassess and adapt their controls rather than rely on a fixed rulebook. The approach was discussed through the FSA’s AI Public-Private Forum, established to bring financial institutions and other stakeholders together to examine the opportunities and risks associated with AI. At the forum’s December 2025 meeting, participants discussed how governance could help firms capture the benefits of AI while keeping risks such as bias, hallucination, security vulnerabilities, privacy, and intellectual-property infringement within acceptable bounds.

The discussions also placed significant responsibility on senior management. A strategy report presented at the forum recommended that boards strengthen their AI literacy, understand how AI relates to corporate value, and treat adoption as a broader corporate-transformation effort rather than simply a technology project. Speakers also stressed senior-management accountability, while participants highlighted data quality, human resources, and data infrastructure as foundations for successful AI implementation.