Federated Data Governance for Cross‑Institution Anti‑Money Laundering (AML) using Data Warehousing and AI

Federated data governance enables banking institutions to leverage collaborative capabilities, effectively combating money laundering activities while upholding compliance requirements and safeguarding information autonomy. The structural design merges secure information warehousing with cognitive systems, creating identification frameworks that maintain confidentiality across institutional boundaries. Utilizing federated learning principles, institutions uncover intricate laundering schemes typically concealed within segregated systems. The governance structure employs cryptographic safeguards, detailed permission hierarchies, and permanent verification records to protect information throughout collaborative engagements. Successful deployment addresses system interoperability, allocates processing capacity, and harmonizes data structures among participating organizations. Regulatory aspects include navigating jurisdictional requirements, transnational information exchange protocols, and coherence with global financial security standards. The article yields improvements in identification precision, surveillance capabilities, and notification accuracy when compared with conventional isolated approaches. Financial organizations implementing federated governance enhance compliance positions while sustaining operational autonomy. Through a balance between security imperatives and functional requirements, this architecture provides a comprehensive solution to coordinated anti-money laundering challenges within interconnected financial markets, laying the groundwork for productive collaboration against increasingly sophisticated financial offenses.

The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.