Exploring Multi-Banking Customer-to-Customer Relations in AML Context with Poincar\'e Embeddings
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.
Cite
@article{arxiv.1912.07701,
title = {Exploring Multi-Banking Customer-to-Customer Relations in AML Context with Poincar\'e Embeddings},
author = {Lucia Larise Stavarache and Donatas Narbutis and Toyotaro Suzumura and Ray Harishankar and Augustas Žaltauskas},
journal= {arXiv preprint arXiv:1912.07701},
year = {2020}
}
Comments
NeurIPS 2019 Workshop on Robust AI in Financial Services (https://sites.google.com/view/robust-ai-in-fs-2019)