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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Money Laundering Detection using Machine Learning and Deep Learning

Author 1: Johrha Alotibi
Author 2: Badriah Almutanni
Author 3: Tahani Alsubait
Author 4: Hosam Alhakami
Author 5: Abdullah Baz

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0131087

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

  • Abstract and Keywords
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Abstract: In recent years, money laundering activities have shown rapid progress and have indeed become the main concern for governments and financial institutions all over the world. As per recent statistics, $800 billion to $2 trillion is the estimated value of money laundered annually, in which $5 billion of the total is obtained from cryptocurrency money laundering. As per the financial action task force (FATF), the criminals may trade illegally obtained fiat money for the cryptocurrency. Accordingly, detecting and preventing illegal transactions becomes a serious threat to governments and it has been indeed challenging. To combat money laundering, especially in cryptocurrency, effective techniques for detecting suspicious transactions must be developed since the current preventive efforts are outdated. In fact, deep learning and machine learning techniques may provide novel methods to detect suspect currency movements. This study investigates the applicability of deep learning and machine learning techniques for anti-money laundering in cryptocurrency. The techniques employed in this study are Deep Neural Network (DNN), random forest (RF), K-Nearest Neighbors(KNN), and Naive Bayes (NB) with the bitcoin elliptic dataset. It was observed that the DNN and random forest classifier have achieved the highest accuracy rate with promising findings in decreasing the false positives as compared to the other classifiers. In particular, the random forest classifier outperforms DNN and achieves an F1-score of 0.99%.

Keywords: Anti-money laundering; machine learning; supervised learning; cryptocurrency

Johrha Alotibi, Badriah Almutanni, Tahani Alsubait, Hosam Alhakami and Abdullah Baz, “Money Laundering Detection using Machine Learning and Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131087

@article{Alotibi2022,
title = {Money Laundering Detection using Machine Learning and Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131087},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131087},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Johrha Alotibi and Badriah Almutanni and Tahani Alsubait and Hosam Alhakami and Abdullah Baz}
}


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