Open Access
SHS Web Conf.
Volume 65, 2019
The 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019)
Article Number 04013
Number of page(s) 7
Section Mathematical Methods, Models, Informational Systems and Technologies in Economics
Published online 29 May 2019
  1. Ministry of Economic Development and Trade of Ukraine. (2018). Accessed 20 Feb 2019 [Google Scholar]
  2. FATF-GAFI.ORG - Financial Action Task Force (FATF). (2019). Accessed 20 Feb 2019 [Google Scholar]
  3. The State Financial Monitoring Service. (2018). Accessed 20 Feb 2019 [Google Scholar]
  4. He, P.: A typological study on money laundering. Journal of Money Laundering Control. 13(1), 15-32 (2010). doi:10.1108/13685201011010182 [CrossRef] [Google Scholar]
  5. Betron, M.: The state of anti-fraud and AML measures in the banking industry. Computer Fraud ^ Security. 2012(5), 5-7 (2012). doi:10.1016/S1361-3723(12)70039-8 [CrossRef] [Google Scholar]
  6. Unger, B.: Can Money Laundering Decrease? Public Finance Review. 41(5), 658-676 (2013). doi:10.1177/1091142113483353 [CrossRef] [Google Scholar]
  7. Simser, J.: Money laundering: emerging threats and trends. Journal of Money Laundering Control. 16(1), 41-54 (2012). doi:10.1108/13685201311286841 [CrossRef] [Google Scholar]
  8. Chong, A., Lopez-De-Silanes, F.: Money laundering and its regulation. Economics & Politics. 27(1), 78-123 (2015). doi:10.1111/ecpo.12051 [CrossRef] [Google Scholar]
  9. Sat, D.M., Krylov, G.O., Bezverbnyi, K.E., Kasatkin, A.B., Kornev, I.A.: Investigation of money laundering methods through cryptocurrency. Journal of Theoretical and Applied Information Technology. 83(2), 244-254. 2.pdf (2016). Accessed 21 Mar 2019 [Google Scholar]
  10. Teichmann, F.M.J.: Twelve methods of money laundering. Journal of Money Laundering Control. 20(2), 130-137 (2017). doi:10.1108/jmlc-05-2016-0018 [CrossRef] [Google Scholar]
  11. Finance Stability Board: Global Shadow Banking Monitoring Report 2014. uploads/r_141030.pdf (2014). Accessed 21 Mar 2019 [Google Scholar]
  12. Isa, Y.M., Sanusi, Z.M., Haniff, M.N., Barnes, P.A.:Money Laundering Risk: From the Bankers’ and Regulators Perspectives. Procedia Economics and Finance. 28, 7-13 (2015). doi:10.1016/s2212-5671(15)01075-8 [CrossRef] [Google Scholar]
  13. Tsingou, E.: New governors on the block: the rise of anti-money laundering professionals. Crime, Law abd Socical Change. 69(2), 191-205 (2018). doi:10.1007/s10611-017-9751-x [CrossRef] [Google Scholar]
  14. Karuppiah, E.K., Lam, K.S., Chen, Z., Van Khoa, L.D., Teoh, E.N., Nazir, A.: Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review. Knowledge and Information Systems. 57(2), 245-285 (2018). doi:10.1007/s10115-017-1144-z [CrossRef] [Google Scholar]
  15. Pramod, V., Li, J., Gao, P.: A framework for preventing money laundering in banks. Information Management & Computer Security. 20(3), 170-183 (2012). doi:10.1108/09685221211247280 [CrossRef] [Google Scholar]
  16. Gao, S., Xu, D., Wang, H., Green, P.: Knowledgebased anti-money laundering: A software agent bank application. Journal of Knowledge Management. 13(2), 63-75 (2009). doi:10.1108/13673270910942709 [CrossRef] [Google Scholar]
  17. Divya, E., Umadevi, P.: Money laundering detection using TFA system. In: International Conference on Software Engineering and Mobile Application Modelling and Development (ICSEMA 2012), 19-21 Dec. 2012 (2013). doi:10.1049/ic.2012.0150 [Google Scholar]
  18. BPMN Specification - Business Process Model and Notation. (2019). Accessed 20 Feb 2019 [Google Scholar]
  19. Bizagi Studio Process Automation & Workflow Software - Free Download. studio (2019). Accessed 20 Feb 2019 [Google Scholar]
  20. BPWin Software Download. BPM Microsystems. (2019). Accessed 20 Feb 2019 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.