Abstract
The progress in IT gave people a bigger space for fraudulent activity. To help the analysis of criminal financial activity special software was invented. The problem is that the machine of money laundering becomes more sophisticated, but present ways of detecting such activities cannot match the level of fraud capabilities. The main objective in this case is finding methods to improve the available systems and designing new algorithms, understanding all principles that are used in money laundering. To accomplish this, all the steps in AML-systems should be revised or developed from the beginning, new tools should be included. This article gives an overview of the current situation with analysis of weaknesses in present AML-system versions and shows the examples of using machine learning.
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Acknowledgements
The research is being conducted with the finance support of the Ministry of Education and Science of the Russian Federation (Contract №14.578.21.0218) Unique ID for Applied Scientific Research (project) RFMEFI57816X0218. The data presented, the statements made, and the views expressed are solely the responsibility of the authors.
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Dorofeev, D., Khrestina, M., Usubaliev, T., Dobrotvorskiy, A., Filatov, S. (2018). Application of Machine Analysis Algorithms to Automate Implementation of Tasks of Combating Criminal Money Laundering. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O. (eds) Digital Transformation and Global Society. DTGS 2018. Communications in Computer and Information Science, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-02843-5_30
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