A. Nexis, Current Industry Perspectives into Anti-Money Laundering Risk Management and Due Diligence, 2015.

. Acpr-&-tracfin, Publication des Lignes directrices conjointes de l'Autorité de contrôle prudentiel et de résolution et de TRACFIN sur les obligations de déclaration et d'information à TRACFIN, 2018.

R. , Journal for Constitutional Theory and Philosophy of Law / Revija za ustavno teorijo in filozofijo prava, Number: 22 Publisher: Klub Revus -Center za raziskovanje evropske ustavnosti in demokracije, pp.51-65, 2014.

, Article 29 Working Party. Opinion on data protection issues related to the prevention of money laundering and terrorist financing, 2011.

V. Beaudouin, I. Bloch, D. Bounie, S. Clémençon, F. Buc et al., Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02506409

L. Dupont, O. Fliche, and S. Yang, Governance of Artificial Intelligence in Finance -Discussion Document, 2020.

, Assessing the necessity of measures that limit the fundamental right to the protection of personal data: A Toolkit, EDPS, 2017.

. Europol, From suspicion to action -Converting financial intelligence into greater operational impact, Library Catalog, 2017.

M. and G. Porcedda, SURVEILLE Deliverable 2.4 -Paper establishing a classification of technologies on the basis of their intrusiveness into fundamental rights, 2011.

D. Hart, Supreme Court on EU and ECHR proportionality -back to basics, Library Catalog: ukhumanrightsblog.com, 2015.

T. Hickman, The substance and structure of proportionality, Public Law, pp.694-716, 2008.

. Ibm, Fighting Financial Crime with AI, 2019.

W. J. Maxwell, In Smart(er) Internet Regulation Through Cost-Benefit Analysis : Measuring harms to privacy, freedom of expression, and the internet ecosystem, i3. Presses des Mines, 2017.

, Internet Regulation Through Cost-Benefit Analysis : Measuring harms to privacy, freedom of expression, and the internet ecosystem

J. Tang and J. Yin, Developing an intelligent data discriminating system of anti-money laundering based on SVM, 2005 International Conference on Machine Learning and Cybernetics, vol.6, pp.3453-3457, 2005.

P. Lascoumes and G. Favarel-garrigues, Les banques sentinelles de l'anti-blanchiment : l'invention d'une spécialité professionnelle dans le secteur financier, 2006.

N. A. Le-khac and M. Kechadi, Application of Data Mining for Anti-money Laundering Detection: A Case Study, 2010 IEEE International Conference on Data Mining Workshops, pp.577-584, 2010.

R. Liu, X. Qian, S. Mao, and S. Zhu, Research on anti-money laundering based on core decision tree algorithm, Chinese Control and Decision Conference (CCDC), pp.1948-9447, 2011.

G. Paravicini, Europe is losing the fight against dirty money, Politico, 2018.

E. L. Paula, M. Ladeira, R. N. Carvalho, and T. Marzagao, Deep Learning Anomaly Detection as Support Fraud Investigation in Brazilian Exports and Anti-Money Laundering, 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.954-960, 2016.

C. A. Anaheim, U. , and D. , , 2016.

A. Portuese, Principle of Proportionality as Principle of Economic E ciency, European Law Journal, vol.19, issue.5, pp.612-635, 2013.

J. Sauvé, Le principe de proportionnalité, protecteur des libertés, Library Catalog, 2017.

D. Savage, Q. Wang, P. Chou, X. Zhang, and X. Yu, Detection of money laundering groups using supervised learning in networks, 2016.

. Tracfin and . Rapport, Annuel d'activité, 2018.

S. Wang and J. Yang, A Money Laundering Risk Evaluation Method Based on Decision Tree, 2007 International Conference on Machine Learning and Cybernetics, pp.283-286, 2007.

M. Weber, J. Chen, T. Suzumura, A. Pareja, T. Ma et al., Scalable Graph Learning for Anti-Money Laundering: A First Look, 2018.

M. Weber, G. Domeniconi, J. Chen, D. K. Weidele, C. Bellei et al., Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics, p.7, 2019.