[en] Financial applications of distributed ledger technologies (DLTs) generate regulatory concerns. In the
crypto sphere, pseudonymity may safeguard privacy and data protection, but lack of identifiability
cripples investigation and enforcement. This challenges the fight against money laundering and the
financing of terrorism and proliferation (AML/CFT/CPF). Nonetheless, forensic techniques trace transfers across blockchain ecosystems and provide intelligence to regulated entities. This working paper addresses anomaly detection in the crypto space, the role of machine learning, and the impact of disintermediation.
Disciplines :
Droit, criminologie & sciences politiques: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
POCHER, Nadia ; Universitat Autònoma de Barcelona ; Alma Mater Studiorum Università di Bologna ; Katholieke Universiteit Leuven - KUL
Zichichi, Mirko
Ferretti, Stefano
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
AML/CFT/CPF endeavors in the crypto space: from blockchain analytics to machine learning
Date de publication/diffusion :
30 octobre 2023
Nom de la manifestation :
Workshop on Artificial Intelligence Governance Ethics and Law (AIGEL)
Lieu de la manifestation :
Barcelona, Espagne
Date de la manifestation :
from 02-11-2022 to 19-12-2022
Manifestation à portée :
International
Titre de l'ouvrage principal :
Proceedings of Artificial Intelligence Governance Ethics and Law (AIGEL)
S. Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System, www.bitcoin.org (2008).
O. Ali, M. Ally, Y. Dwivedi, et al., The state of play of blockchain technology in the financial services sector: A systematic literature review, International Journal of Information Management 54 (2020) 102199.
B. Akhgar, M. Gercke, S. Vrochidis, H. Gibson, Dark Web Investigation, 2021.
M. Wu, W. McTighe, K. Wang, I. A. Seres, N. Bax, M. Puebla, M. Mendez, F. Carrone, T. De Mattey, H. O. Demaestri, M. Nicolini, P. Fontana, Tutela: An Open-Source Tool for Assessing User-Privacy on Ethereum and Tornado Cash (2022).
FATF, Virtual Assets Red Flag Indicators of Money Laundering and Terrorist Financing, 2020. URL: http://www.fatf-gafi.org/.
FATF, Virtual Assets and Virtual Asset Service Providers - Updated Guidance for a Risk-Based Approach (2021). URL: www.fatf-gafi.org.
FATF, International Standards on Combating Money Laundering and the Financing of Terrorism & Proliferation: FATF Recommendations, 2012. URL: https://www.fatf-gafi.org/.
T. Schrepel, Smart Contracts and the Digital Single Market Through the Lens of a'Law + Technology' Approach, 2021. URL: https://papers.ssrn.com/abstract=3947174.
C. Campajola, R. Cristodaro, F. M. De Collibus, T. Yan, N. Vallarano, C. J. Tessone, The Evolution Of Centralisation on Cryptocurrency Platforms (2022) 1-14.
CipherTrace, Cryptocurrency Crime and Anti-Money Laundering Report, 2021.
Chainalysis Team, The 2022 Crypto Crime Report (2022).
ITU-T Focus Group on DLT, Technical Report FG DLT D1.2 - Distributed ledger technology overview, concepts, ecosystem, 2019. URL: https://www.itu.int/.
F. Wang, P. De Filippi, Self-Sovereign Identity in a Globalized World: Credentials-Based Identity Systems as a Driver for Economic Inclusion, Frontiers in Blockchain 2 (2020) 1-22.
M. Quiniou, Blockchain: the advent of disintermediation, ISTE Ltd, 2019.
A. Pfitzmann, M. Hansen, A terminology for talking about privacy by data minimization: Anonymity, Unlinkability, Undetectability, Unobservability, Pseudonymity, and Identity Management, Technical University Dresden (2010) 1-98.
N. Amarasinghe, X. Boyen, M. McKague, A Survey of Anonymity of Cryptocurrencies, in: ACM International Conference Proceeding Series, Sydney, 2019.
J. Grijpink, C. Prins, Digital anonymity on the Internet: New rules for anonymous electronic transactions? An exploration of the private law implications of digital anonymity, Computer Law and Security Report 17 (2001) 379-389.
J. Harvey, I. Branco-Illodo, Why Cryptocurrencies Want Privacy: A Review of Political Motivations and Branding Expressed in “Privacy Coin” Whitepapers, Journal of Political Marketing 19 (2020) 107-136.
Bank of Italy, Provvedimento recante gli indicatori di anomalia per gli intermediari, 2010. URL: https://uif.bancaditalia.it/.
A. Kamišalić, R. Kramberger, I. Fister, Synergy of blockchain technology and data mining techniques for anomaly detection, Applied Sciences (Switzerland) 11 (2021).
M. Weber, J. Chen, T. Suzumura, A. Pareja, T. Ma, H. Kanezashi, T. Kaler, C. E. Leiserson, T. B. Schardl, Scalable Graph Learning for Anti-Money Laundering: A First Look (2018). arXiv:1812.00076.
T. Phan, Exploring Blockchain Forensics, 2021.
D. Silva Ramalho, N. Igreja Matos, What we do in the (digital) shadows: anti-money laundering regulation and a bitcoin-mixing criminal problem, ERA Forum 22 (2021).
N. Furneaux, Investigating Cryptocurrencies: Understanding, Extracting, and Analyzing Blockchain Evidence, Wiley, 2018.
M. Bartoletti, S. Carta, T. Cimoli, R. Saia, Dissecting Ponzi schemes on Ethereum: Identification, analysis, and impact, Future Generation Computer Systems 102 (2020) 259-277.
S. Ferretti, G. D'Angelo, On the ethereum blockchain structure: A complex networks theory perspective, Concurrency and Computation: Practice and Experience 32 (2020).
L. Tennant, Improving the Anonymity of the IOTA Cryptocurrency (2017) 1-20. URL: https://laurencetennant.com/papers/anonymity-iota.pdf.pdf.
P. Ince, J. K. Liu, P. Zhang, Adding confidential transactions to cryptocurrency IOTA with bulletproofs, volume 11058 LNCS, Springer International Publishing, 2018.
P. Moreno-Sanchez, M. Zafar, A. Kate, Listening to whispers of ripple: Linking wallets and deanonymizing transactions in the ripple network, Proceedings on PETs (2016).
M. Lischke, B. Fabian, Analyzing the bitcoin network: The First Four Years, Future Internet 8 (2016). doi:10.3390/fi8010007.
E. Androulaki, G. O. Karame, M. Roeschlin, T. Scherer, S. Capkun, Evaluating user privacy in Bitcoin, Lecture Notes in Computer Science 7859 (2013) 34-51.
H. Al Jawaheri, M. Al Sabah, Y. Boshmaf, A. Erbad, Deanonymizing Tor hidden service users through Bitcoin transactions analysis, Computers and Security 89 (2020).
S. Meiklejohn, M. Pomarole, G. Jordan, K. Levchenko, D. McCoy, G. M. Voelker, S. Savage, A fistful of Bitcoins: Characterizing payments among men with no names, Communications of the ACM 59 (2016) 86-93.
H. H. S. Yin, K. Langenheldt, M. Harlev, R. R. Mukkamala, R. Vatrapu, Regulating Cryptocurrencies: A Supervised Machine Learning Approach to De-Anonymizing the Bitcoin Blockchain, Journal of Management Information Systems 36 (2019) 37-73.
M. Fleder, M. S. Kester, S. Pillai, Bitcoin Transaction Graph Analysis (2015) 1-8. arXiv:1502.01657.
J. Wu, J. Liu, W. Chen, H. Huang, Z. Zheng, Y. Zhang, Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs, Journal of Latex Class Files 14 (2020). arXiv:2001.05233.
M. Weber, G. Domeniconi, J. Chen, D. K. I. Weidele, C. Bellei, T. Robinson, C. E. Leiserson, Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics (2019). arXiv:1908.02591.
A. N. Eddin, J. Bono, D. Aparício, D. Polido, J. T. Ascensão, P. Bizarro, P. Ribeiro, Anti-money laundering alert optimization using machine learning with graphs, 2021. doi: 10.48550/ARXIV.2112.07508.
J. S. Lorenz, Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity, Ph.D. thesis, 2021.
T. N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, arXiv:1609.02907 (2016).
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio, Graph attention networks, arXiv:1710.10903 (2017).
C. Oliveira, J. Torres, M. I. Silva, D. Aparício, J. T. Ascensão, P. Bizarro, Guiltywalker: Distance to illicit nodes in the bitcoin network, arXiv:2102.05373 (2021).
D. V. Kute, B. Pradhan, N. Shukla, A. Alamri, Deep learning and explainable artificial intelligence techniques applied for detecting money laundering-a critical review, IEEE Access (2021).
BCB Group, Centralized vs. Decentralized Exchanges, 2022. URL: https://www.bcbgroup.com/centralized-vs-decentralized-exchanges/.
The Block, DEX to CEX Spot Trade Volume, 2022. URL: https://www.theblock.co/data/decentralized-finance/dex-non-custodial/dex-to-cex-spot-trade-volume.
C. R. Goforth, Crypto Assets: A Fintech Forecast (2020) 5-25.
