Application of Artificial Intelligence Technologies for the Monitoring of Transactions in AML-Systems Using the Example of the Developed Classification Algorithm
About this article
DOI:
https://doi.org/10.14419/ijet.v7i4.36.23317Keywords:
system, transactions, transaction, Ñcomputational, resource, processingAbstract
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
Using Artificial Intelligence and Machine Learning to Help Finan-cial 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)
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
Savage D., Wang Q., Zhang X. et al. Detection of Money Launder-ing Groups: Supervised Learning on Small Networks. 2017. URL: https://aaai.org/ocs/index.php/WS/AAAIW17/paper/view/15101 (24.09.2018)
Voit A., Stankus A., Magomedov Sh., Ivanova I. Big data pro-cessing for full-text search and visualization with elasticsearch In-ternational Journal of Advanced Computer Science and Applica-tions. 2017. Т. 8. № 12. С. 76-83. DOI: 10.14569/IJACSA.2017.081211
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)
View more references (5)
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 Sci-ence, pp. 37-43, 2016.
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)
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
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)
Page L., Brin S., Motwani R. et al. The PageRank citation ranking: Bringing order to the web. Tech. Rep. Stanford InfoLab. 1999.