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

TL;DR

This paper introduces a novel 3D topological algebra approach using Poincaré embeddings to analyze multi-banking customer relations for AML, addressing privacy and regulatory challenges in financial data sharing.

Contribution

It proposes a new topology leveraging Poincaré embeddings to model customer relations in AML, overcoming limitations of existing methods.

Findings

Effective modeling of customer-to-customer relations

Enhanced privacy-preserving analysis framework

Potential for improved AML detection accuracy

Abstract

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.