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In the recent years money laundering schemes have grown in complexity andspeed of realization, affecting financial institutions and millions ofcustomers globally. Strengthened privacy policies, along with in-countryregulations, make it hard for banks to inner- and cross-share, and reportsuspicious activities for the AML (Anti-Money Laundering) measures. Existingtopologies and models for AML analysis and information sharing are subject tomajor limitations, such as compliance with regulatory constraints, extendedinfrastructure to run high-computation algorithms, data quality and span,proving cumbersome and costly to execute, federate, and interpret. This paperproposes a new topology for exploring multi-banking customer social relationsin AML context -- customer-to-customer, customer-to-transaction, andtransaction-to-transaction -- using a 3D modeling topological algebraformulated through Poincar\'e embeddings.

Exploring Multi-Banking Customer-to-Customer Relations in AML Context with Poincar\'e Embeddings