  In the recent years money laundering schemes have grown in complexity and
speed of realization, affecting financial institutions and millions of
customers globally. Strengthened privacy policies, along with in-country
regulations, make it hard for banks to inner- and cross-share, and report
suspicious activities for the AML (Anti-Money Laundering) measures. Existing
topologies and models for AML analysis and information sharing are subject to
major limitations, such as compliance with regulatory constraints, extended
infrastructure to run high-computation algorithms, data quality and span,
proving cumbersome and costly to execute, federate, and interpret. This paper
proposes a new topology for exploring multi-banking customer social relations
in AML context -- customer-to-customer, customer-to-transaction, and
transaction-to-transaction -- using a 3D modeling topological algebra
formulated through Poincar\'e embeddings.
