SimRank; Anti-Money Laundering; Know Your Customer (KYC); Risk Assessment
Résumé :
[en] The ever increasing regulatory requirements for Anti-Money Laundering (AML) compliance presents significant challenges for financial institutions and small businesses globally. Efficiently navigating these requirements is not only crucial for legal adherence but also for safeguarding the integrity of the global financial system.
In response to this challenge, we develop a framework that leverages advanced algorithms to improve the identification and risk assessment processes within Know Your Customer (KYC) procedures. By employing a technique for measuring graph-based node similarities, our approach enhances the detection of Politically Exposed Persons (PEPs) and their known associates, facilitating a more nuanced and comprehensive analysis than traditional methods allow.
We study the dataset of Ultimate Beneficial Owner (UBO) registry in Luxembourg and translate our findings into two risk indicators:involvement with underage shareholders, and number of companies at the address. We integrate these two indicators as well as several other components of AML compliance, including country risk indices, beneficial ownership structures, and adverse media exposure, into a singular, coherent risk metric. The framework is designed to be both modular, supporting various degrees of regulatory scrutiny, and scalable, suitable for evolving regulatory landscapes. This risk metric can be used to determine whether Enhanced Due Diligence (EDD) is required by European AML directives. The end result is a more robust defense against financial crimes and an enhancement of the overall AML/CFT efforts within the EU and beyond.
Centre de recherche :
NCER-FT - FinTech National Centre of Excellence in Research
Disciplines :
Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres Droit, criminologie & sciences politiques: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
JAFARNEJAD, Sasan ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Ubiquitous and Intelligent Systems (UBI-X)
ROBINET, François ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust > Ubiquitous and Intelligent Systems > Team Raphaël FRANK
FRANK, Raphaël ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Ubiquitous and Intelligent Systems (UBI-X)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
A Risk-Based AML Framework: Finding Associates Through Ultimate Beneficial Owners
Date de publication/diffusion :
2024
Nom de la manifestation :
IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE FOR FINANCIAL ENGINEERING
Lieu de la manifestation :
Hoboken, Etats-Unis - New Jersey
Date de la manifestation :
October 22 and 23, 2024
Manifestation à portée :
International
Titre de l'ouvrage principal :
CIFER 2024
Maison d'édition :
IEEE
Peer reviewed :
Peer reviewed
Projet FnR :
FNR16570468 - 2021 (01/07/2022-30/06/2030) - Yves Le Traon
Intitulé du projet de recherche :
RoboComp
Organisme subsidiant :
FNR - Luxembourg National Research Fund
N° du Fonds :
NCER22/IS/16570468/NCER-FT
Subventionnement (détails) :
This research was funded in whole, or in part, by the
Luxembourg National Research Fund (FNR), grant reference
NCER22/IS/16570468/NCER-FT. For the purpose of open
access, and in fulfilment of the obligations arising from the
grant agreement, the author has applied a Creative Com-
mons Attribution 4.0 International (CC BY 4.0) license to
any Author Accepted Manuscript version arising from this
submission.
United Nations Office on Drugs and Crime, “Estimating illicit financial flows resulting from drug trafficking and other transnational organized crimes,” https://www.unodc.org/documents/data-and-analysis/Studies/Illicit-financial-flows_31Aug11.pdf, 2011, accessed: 2023-11-03.
Europol, “Criminal asset recovery in the eu,” https://www.europol.europa.eu/cms/sites/default/files/documents/criminal_asset_recovery_in_the_eu_web_version.pdf, 2016, accessed: 2023-11-06.
J. C. Sharman, “Shopping for Anonymous Shell Companies: An Audit Study of Anonymity and Crime in the International Financial System,” Journal of Economic Perspectives, vol. 24, no. 4, pp. 127–140, Dec. 2010.
E. van der Does de Willebois, E. M. Halter, R. A. Harrison, J. W. Park, and J. C. Sharman, The Puppet Masters: How the Corrupt Use Legal Structures to Hide Stolen Assets and What to Do About It. World Bank, 2011.
A. N. Eddin, J. Bono, D. Aparício, D. Polido, J. T. Ascensão, P. Bizarro, and P. Ribeiro, “Anti-money laundering alert optimization using machine learning with graphs,” CoRR, vol. abs/2112.07508, 2021.
Z. Tang, H. E, M. Sun, L. Zhao, R. Wang, and M. Song, “Anti-money laundering method based on hierarchical risk control knowledge graph,” in International Conference on Artificial Intelligence and Computer Science, 2023.
B. Dumitrescu, A. Baltoiu, and S. Budulan, “Anomaly detection in graphs of bank transactions for anti money laundering applications,” IEEE Access, vol. 10, pp. 47 699–47 714, 2022.
A. K. Shaikh, M. Al-Shamli, and A. Nazir, “Designing a relational model to identify relationships between suspicious customers in anti-money laundering (aml) using social network analysis (sna),” Journal of Big Data, vol. 8, 2021.
G. Jeh and J. Widom, “Simrank: a measure of structural-context similarity,” in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002, pp. 538–543.
Court of Justice of the European Union. (2023) Judgment of 22 november 2022, joined cases c-37/20 and c-601/20. [Online]. Available: https://curia.europa.eu/juris/document/document.jsf?text=&docid=268059&doclang=en
Organized Crime and Corruption Reporting Project (OCCRP), “Boss babies: The children who own hundreds of luxembourg corporations,” OCCRP, 2021, accessed: 2023-11-03. [Online]. Available: https://www.occrp.org/en/openlux/boss-babies-the-childrenwho-own-hundreds-of-luxembourg-corporations
International Consortium of Investigative Journalists (ICIJ), “The paradise papers,” Online, 2017, available: https://www.icij.org/investigations/paradise-papers/.
International Consortium of Investigative Journalists (ICIJ), “The panama papers,” Online, 2016, available: https://www.icij.org/investigations/panama-papers/.
Financial Action Task Force (FATF), “High-risk and other monitored jurisdictions,” Financial Action Task Force, 2023, accessed: 2023-11-03. [Online]. Available: https://www.fatf-gafi.org/en/countries/blackand-grey-lists.html
P. S. H. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-augmented generation for knowledge-intensive NLP tasks,” CoRR, vol. abs/2005.11401, 2020.
M. A. Naheem, “Money laundering using investment companies,” Journal of Money Laundering Control, vol. 18, no. 4, pp. 438–446, Jan. 2015, publisher: Emerald Group Publishing Limited.
S. Varrette, H. Cartiaux, S. Peter, E. Kieffer, T. Valette, and A. Olloh, “Management of an Academic HPC & Research Computing Facility: The ULHPC Experience 2.0,” in Proc. of the 6th ACM High Performance Computing and Cluster Technologies Conf. (HPCCT 2022). Fuzhou, China: Association for Computing Machinery (ACM), July 2022.