Application of Artificial Intelligence Technologies for the Monitoring of Transactions in AML-Systems Using the Example of the Developed Classification Algorithm

  • Authors

    • S. G. Magomedov
    • A. S. Dobrotvorsky
    • M. M.P. Khrestina
    • S. A. Pavelyev
    • T. R. Yusubaliev
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.36.23317
  • system, transactions, transaction, сcomputational, resource, processing
  • Abstract

    The article describes the application of artificial intelligence technologies in Anti Money Laundering (AML) systems for the purpose of transaction monitoring by the example of the developed transaction classification algorithm using machine learning methods. To improve the effectiveness of the algorithm a novel mechanism for forming a unique set of characteristics for the transactions and the participants of financial processes has been developed and a method for constructing a transaction graph has been proposed.

     

     

  • References

    1. [1] Using Artificial Intelligence and Machine Learning to Help Financial Institutions Increase Compliance with Know Your Customer (KYC) Regulations. 2017. P. 2. URL: www.h2o.ai/wp-content/uploads/2017/06/Know-Your-Customer_v3_pages.pdf (24.09.2018)

      [2] Pozzolo A. D., Caelen O., Borgne Y.-A. L. et al. Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications. 2014. Vol. 41. P. 4915–4928

      [3] Savage D., Wang Q., Zhang X. et al. Detection of Money Laundering Groups: Supervised Learning on Small Networks. 2017. URL: https://aaai.org/ocs/index.php/WS/AAAIW17/paper/view/15101 (24.09.2018)

      [4] Voit A., Stankus A., Magomedov Sh., Ivanova I. Big data processing for full-text search and visualization with elasticsearch International Journal of Advanced Computer Science and Applications. 2017. Т. 8. № 12. С. 76-83. DOI: 10.14569/IJACSA.2017.081211

      [5] Savage D., Wang Q., Chou P. et al. Detection of money laundering groups using supervised learning in networks. 2016. URL: https://arxiv.org/pdf/1608.00708.pdf (24.09.2018)

      [6] C. Suresh, K. T. Reddy, and N. Sweta, A Hybrid Approach for Detecting Suspicious Accounts in Money Laundering Using Data Mining Techniques," Information Technology and Computer Science, pp. 37-43, 2016.

      [7] Akoglu L., Tong H., Koutra D. Graph Based Anomaly Detection and Description: A Survey. Data Min. Knowl. Discov. 2015. Vol. 29, # 3. P. 626–688. URL: https://arxiv.org/pdf/1404.4679.pdf (24.09.2018)

      [8] Magomedov Sh. Organization of secured data transfer in computers using sign-value notation. ITM Web of Conferences. 2017. Т. 10. DOI: 10.1051/itmconf/20171004004

      [9] Brandes U. A faster algorithm for betweenness centrality. Journal of mathematical sociology. 2001. Vol. 25, # 2. P. 163–177. URL: http://www.algo.uni-konstanz.de/publications/b-fabc-01.pdf (24.09.2018)

      [10] Page L., Brin S., Motwani R. et al. The PageRank citation ranking: Bringing order to the web. Tech. Rep. Stanford InfoLab. 1999.

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  • How to Cite

    G. Magomedov, S., S. Dobrotvorsky, A., M.P. Khrestina, M., A. Pavelyev, S., & R. Yusubaliev, T. (2018). Application of Artificial Intelligence Technologies for the Monitoring of Transactions in AML-Systems Using the Example of the Developed Classification Algorithm. International Journal of Engineering & Technology, 7(4.36), 76-79. https://doi.org/10.14419/ijet.v7i4.36.23317

    Received date: 2018-12-07

    Accepted date: 2018-12-07

    Published date: 2018-12-09