ALGORITHMIC MECHANISMS IN ANTI-MONEY LAUNDERING SYSTEMS: A SYSTEMATIC REVIEW OF EFFECTIVENESS AND PERFORMANCE
DOI:
https://doi.org/10.35631/IJEMP.933038Keywords:
Algorithms, Anti-Money Laundering Systems, Detection, Machine Learning, Systematic Literature ReviewAbstract
This study provides a comprehensive review and classification of the current literature on algorithmic approaches for combating money laundering. A systematic literature review (SLR) was conducted using the Universiti Sains Malaysia (USM) Digital Library. After applying the inclusion and exclusion criteria, 27 relevant publications published between 2015 and 2020 were selected for analysis. A classification framework was developed to analyse the selected studies, which includes solutions, machine learning, data sources, assessment techniques, implementation tools, sampling approaches, and study regions. The findings provide insights into the current research landscape of algorithmic anti-money laundering (AML) systems and identify trends, gaps, and opportunities for future research.
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About - Financial Action Task Force (FATF) (2020) “[WWW document]”, available www.fatf-gafi. org/about/ (accessed 28 November 2018).
Alexandre, C. and Balsa, J. (2015), A Multiagent Based Approach to Money Laundering Detection and Prevention, ICAART (1). pp. 230-235.
Alexandre, C. and Balsa, J. (2016), Integrating Client Profiling in an anti-Money Laundering Multiagent Based System, in New Advances in Information Systems and Technologies, Springer, pp. 931-941.
Aluko, A., & Bagheri, M. (2012). The impact of money laundering on economic and financial stability and on political development in developing countries: The case of Nigeria. Journal of Money Laundering Control.
Badal-Valero, E., Alvarez-Jareño, J.A. and Pavía, J.M. (2018), "Combining Benford's law and machine learning to detect money laundering. An actual Spanish court case", Forensic Science International, Vol. 282, pp. 24-34.
Colladon, A.F., and Remondi, E. (2017), "Using social network analysis to prevent money laundering," Expert Systems with Applications, Vol. 67, pp. 49-58.
Demetis, D.S. (2018), "Fighting money laundering with technology: a case study of Bank X in the UK," Decision Support Systems, Vol. 105, pp. 96-107.
Drez_ewski, R., Dziuban, G., Hernik, Ł. and Pączek, M. (2015b), "Comparison of data mining techniques for money laundering detection system," 2015 International Conference on Science in Information Technology (ICSITech), IEEE, pp. 5-10.
Drez_ewski, R., Sepielak, J. and Filipkowski, W. (2015a), "The application of social network analysis algorithms in a system supporting money laundering detection," Information Sciences, Vol. 295, pp. 18-32.
Financial Action Task Force (FATF). (2020). COVID-19-related Money Laundering and Terrorist Financing-Risks and Policy Responses. https://www.unodc.org/documents/Advocacy-Section/UNODC_-_MONEY_LAUNDERING_AND_COVID19_-_Profit_and_Loss_v1.1_-_14-04-2020_-_CMLS-COVID19-GPML1_-_UNCLASSIFIED_-_BRANDED.pdf
Friedman, J., Hastie, T., and Tibshirani, R. (2001), The Elements of Statistical Learning, Springer series in statistics New York, NY.
Jayasree, V. and Siva Balan, R. (2017), "Money laundering regulatory risk evaluation using bitmap index-based decision tree," Journal of the Association of Arab Universities for Basic and Applied Sciences, Vol. 23 No. 1, pp. 96-102.
Jullum, M., Løland, A., Huseby, R.B., AAnonsen, G. and Lorentzen, J. (2020), "Detecting money laundering transactions with machine learning," J. Money Laund. Control.
Kannan, S. and Somasundaram, K. (2017), "Autoregressive-based outlier algorithm to detect money laundering activities," Journal of Money Laundering Control, Vol. 20 No. 2, pp. 190-202.
Lawrencia, C. and Ce, W. (2019), "Fraud detection decision support system for Indonesian financial institution," in 2019 International Conference on Information Management and Technology (ICIMTech). Presented at the 2019 International Conference on Information Management and Technology (ICIMTech), IEEE, Jakarta/Bali, Indonesia, pp. 389-394.
Le Khac, N.A. and Kechadi, M.-T. (2010), "Application of data mining for anti-money laundering detection: a case study," in 2010 IEEE International Conference on Data Mining Workshops. Presented at the 2010 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, Sydney, TBD, Australia, pp. 577-584.
Le-Khac, N.-A. Markos, S. O'Neill, M. Brabazon, A. and Kechadi, T. (2016), "An efficient search tool for an anti-money laundering application of a multinational bank's dataset," ArXiv Prepr. ArXiv160902031.
Li, X., Cao, X., Qiu, X., Zhao, J. and Zheng, J. (2017), "Intelligent anti-money laundering solution based upon novel community detection in massive transaction networks on spark," in 2017 Fifth International Conference on Advanced Cloud and Big Data (CBD). Presented at the 2017 Fifth International Conference on Advanced Cloud and Big Data (CBD), IEEE, Shanghai, China, pp. 176-181.
Liu, R., Qian, X., Mao, S. and Zhu, S. (2011), "Research on anti-money laundering based on core decision tree algorithm," in 2011 Chinese Control and Decision Conference (CCDC). Presented at the 2011 23rd Chinese Control and Decision Conference (CCDC), IEEE, Mianyang, China, pp. 4322-4325.
Moustafa, T.H., Abd El-Megied, M.Z., Sobh, T.S. and Shafea, K.M. (2015), "Anti money laundering using a two-phase system," Journal of Money Laundering Control, Vol. 18 No. 3, pp. 304-329.
Mubalaike, A.M. and Adali, E. (2018), "Deep learning approach for intelligent financial fraud detection system," in 2018 3rd International Conference on Computer Science and Engineering (UBMK), IEEE, pp. 598-603.
Paula, E.L., Ladeira, M., Carvalho, R.N. and Marzagao, T. (2016), "Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering," in 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, pp. 954-960.
Salehi, A., Ghazanfari, M., and Fathian, M. (2017), "Data mining techniques for anti-money laundering," Int. J. Appl. Eng. Res, Vol. 12, pp. 10084-10094.
Sapozhnikova, M., Nikonov, A., Vulfin, A., Gayanova, M., Mironov, K. and Kurennov, D. (2017), "Anti- fraud system on the basis of data mining technologies," in 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), IEEE, pp. 243-248.
Savage, D., Wang, Q., Zhang, X., Chou, P. and Yu, X. (2017), "Detection of money laundering groups: Supervised learning on small networks," in Workshops at the Thirty-First AAAI Conference on Artificial Intelligence.
Singh, K. and Best, P. (2019), "Anti-money laundering: using data visualization to identify suspicious activity," International Journal of Accounting Information Systems, Vol. 34, p. 100418.
Soltani, R., Nguyen, U.T., Yang, Y., Faghani, M., Yagoub, A. and An, A. (2016), "A new algorithm for money laundering detection based on structural similarity," in 2016 IEEE 7th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), IEEE, pp. 1-7.
Suresh, C., Reddy, K.T. and Sweta, N. (2016), "A hybrid approach for detecting suspicious accounts in money laundering using data mining techniques," International Journal of Information Technology and Computer Science, Vol. 8 No. 5, p. 37.
Syed Mustapha Nazri, S.N.F., Zolkaflil, S. and Omar, N. (2019), "Mitigating financial leakages through effective money-laundering investigation," Managerial Auditing Journal, Vol. 34 No. 2, pp. 189-207.
Villalobos, M.A. and Silva, E. (2017), "A statistical and machine learning model to detect money laundering: an application."
Yang, S. and Wei, L. (2010), "Detecting money laundering using filtering techniques: a multiple-criteria index," Journal of Economic Policy Reform, Vol. 13 No. 2, pp. 159-178.
Zhang, Y. and Trubey, P. (2018), "Machine learning and sampling scheme: an empirical study of money laundering detection," Comput. Econ, pp. 1-21.
Zhou, Y., Wang, X., Zhang, J., Zhang, P., Liu, L., Jin, H. and Jin, H. (2018), "Analyzing and detecting money-laundering accounts in online social networks," IEEE Netw, Vol. 32 No. 3, pp. 115-121
