The Institutional Logic and Regulatory Architecture of Financial Data Regulatory Sandboxes

Abstract

The core challenge in financial data governance has shifted from mere rights protection to the construction of trustworthy circulation of data. Traditional regulation, which relies on ex-ante rules and ex-post accountability, struggles to adapt to the compound risks generated by continuous data flows and iterative model development. It often finds itself trapped between regulatory lag and stifled innovation. The institutional significance of the financial data regulatory sandbox lies in embedding data processing and algorithmic operations within an observable and verifiable regulatory process, transforming uncertain risks into empirical knowledge necessary for rule recalibration. Although extraterritorial practices vary across jurisdictions, they collectively point toward a transformation of regulatory capacity—from case-by-case approvals to infrastructure development. Whether through data infrastructure supporting regulatory learning, model validation tools embedded in regulatory workflows, or real-world scenarios testing data authorization and consumer protection, the essence is to ground regulatory judgment in empirical evidence through controlled testing. China's construction of a financial data regulatory sandbox should transcend the mindset of temporary exemptions and project-based pilots, moving toward an institutionalized regulatory mechanism anchored in lawful data entry, model impact assessments, process auditing, consumer remedies, and rule feedback.