Banks and other financial institutions handle sensitive records regarding people, trusts, and corporations. Money, as a sensitive and useful commodity, makes financial organizations valuable and prone to criminal elements. Common criminal activities that target the banking sector include money laundering, identity and personal records theft, and terrorism financing. These are global issues that have garnered the attention of international bodies and governments. One method proposed to deal with illicit finance and money laundering is artificial intelligence (AI). AI implements various algorithms and techniques to monitor customers, markets, and financial transactions that help identify various banking habits. Understanding clients’ transactions and the nature of bank transfers enables AI to prevent and combat money laundering. This research offers an understanding of how artificial intelligence is used in the financial system to combat fraudulent activities such as money laundering. It is organized into five chapters covering various aspects of artificial intelligence and money laundering.
References
[1]
Financial Action Task Force (2019) FATF-GAFI. https://www.fatf-gafi.org/publications/?hf=10&b=0&s=desc(fatf_releasedate)
[2]
Huang, M.H. and Rust, R.T. (2018) Artificial Intelligence in Service. Journal of Service Research, 21, 155-172. https://doi.org/10.1177/1094670517752459
[3]
Allam, Z. and Dhunny, Z.A. (2019) On Big Data, Artificial Intelligence and Smart Cities. Cities, 89, 80-91. https://doi.org/10.1016/j.cities.2019.01.032
[4]
Kaya, O. (2019) Artificial Intelligence in Banking. Artificial Intelligence. http://dbresearch.com/PROD/RPS_EN-PROD/PROD0000000000495172/Artificial_intelligence_in_banking%3A_A_lever_for_pr.pdf
[5]
Yantis, B., Attia, M. and Lethouris, G. (2018) Money Laundering. American Criminal Law Review, 55, 1469.
[6]
Jensen, D. (1997) Prospective Assessment of AI Technologies for Fraud Detection: A Case Study. AAAI Workshop on AI Approaches to Fraud Detection and Risk Management, 34-38.
[7]
Gao, Z. and Ye, M. (2007) A Framework for Data Mining-Based Anti-Money Laundering Research. Journal of Money Laundering Control, 10, 170-179. https://doi.org/10.1108/13685200710746875
[8]
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, 18, 304-329. https://doi.org/10.1108/JMLC-05-2014-0015
[9]
Hanafi, A. (2010) The Dynamic Aspects of Criminal Act and Criminal Liability in Money Laundering Practices. Jurnal Hukum Ius Quia Iustum, 17, 633-650. https://doi.org/10.20885/iustum.vol17.iss4.art7
[10]
Hendriyetty, N. and Grewal, B.S. (2017) Macroeconomics of Money Laundering: Effects and Measurements. Journal of Financial Crime, 24, 65-81. https://doi.org/10.1108/JFC-01-2016-0004
[11]
Teichmann, F.M. and Falker, M.C. (2020) Money Laundering through Consulting Companies. Journal of Financial Regulation and Compliance, 28, 485-500. https://doi.org/10.1108/JFRC-07-2019-0091
[12]
Karim, A.S., Mohamed, N., Ahmad, M.A.N. and Prabowo, H.Y. (2019) Money Laundering in Indonesia Bankers: Compliance, Practice, and Impact. ICFF 2019: Proceedings of the 1st International Conference on Financial Forensics and Fraud, Bali, 13-14 August 2019, p. 13. https://doi.org/10.4108/eai.13-8-2019.2294249
[13]
Soroka, M. and Kugai, K. (2020) Money Laundering Consequences for the National Economy. Вітчизняна наука на зламі епох: Проблеми та перспективи розвитку. Університет Григорія Сковороди в Переяславі. https://er.knutd.edu.ua/bitstream/123456789/15973/1/Kugai_Soroka_Money_laundering_consequences_for_the_national_economy.pdf
[14]
Hetemi, A., Merovci, S. and Gulhan, O. (2018) Consequences of Money Laundering on Economic Growth—The Case of Kosovo and Its Trade Partners. Acta Universitatis Danubius, 14, 113-125. https://core.ac.uk/download/pdf/229459748.pdf
[15]
Savona, E.U. and Riccardi, M. (2017) Assessing the Risk of Money Laundering in Europe. Final Report of Project IARM. https://dspace.library.uu.nl/bitstream/handle/1874/373562/ProjectIARM_FinalReport.pdf?sequence=1
[16]
Oluwadayisi, A.O. and Mimiko, M.O. (2016) Effects of Money Laundering on the Economy of Nigeria. Beijing Law Review, 7, 158-169. https://doi.org/10.4236/blr.2016.72017
[17]
Brynjolfsson, E. and Mcafee, A. (2017) The Business of Artificial Intelligence. Harvard Business Review, 7, 3-11. https://starlab-alliance.com/wp-content/uploads/2017/09/The-Business-of-Artificial-Intelligence.pdf
[18]
Moor, J. (2006) The Dartmouth College Artificial Intelligence Conference: The Next Fifty Years. https://www.researchgate.net/publication/220605256_The_Dartmouth_College_Artificial_Intelligence_Conference_The_Next_Fifty_Years
[19]
Strelkova, P. (2017) Three Types of Artificial Intelligence. http://eztuir.ztu.edu.ua/bitstream/handle/123456789/6479/142.pdf?sequence=1&i
[20]
Joshi, P. (2017) Artificial Intelligence with Python. Packt Publishing, Birmingham.
[21]
Chen, Z., Teoh, E.N., Nazir, A., Karuppiah, E.K. and Lam, K.S. (2018) Machine Learning Techniques for Anti-Money Laundering (AML) Solutions in Suspicious Transaction Detection: A Review. Knowledge and Information Systems, 57, 245-285. https://doi.org/10.1007/s10115-017-1144-z
[22]
Bellomarini, L., Laurenza, E. and Sallinger, E. (2020) Rule-Based Anti-Money Laundering in Financial Intelligence Units: Experience and Vision. https://ceur-ws.org/Vol-2644/paper40.pdf
[23]
Jamshidi, M.B., Gorjiankhanzad, M., Lalbakhsh, A. and Roshani, S. (2019) A Novel Multiobjective Approach for Detecting Money Laundering with a Neuro-Fuzzy Technique. 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), Banff, 9-11 May 2019, 454-458. https://doi.org/10.1109/ICNSC.2019.8743234
[24]
Garcia-Bedoya, O., Granados, O. and Burgos, J.C. (2020) AI against Money Laundering Networks: The Colombian Case. Journal of Money Laundering Control, 24, 49-62. https://doi.org/10.1108/JMLC-04-2020-0033
[25]
Han, J., Huang, Y., Liu, S. and Towey, K. (2020) Artificial Intelligence for Anti-Money Laundering: A Review and Extension. Digital Finance, 2, 211-239. https://doi.org/10.1007/s42521-020-00023-1
[26]
Salehi, A., Ghazanfari, M. and Fathian, M. (2017) Data Mining Techniques for Anti Money Laundering. International Journal of Applied Engineering Research, 12, 10084-10094. http://www.ripublication.com/ijaer17/ijaerv12n20_120.pdf
[27]
Brundage, M., Avin, S. and Clark, J. (2018) The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. https://arxiv.org/ftp/arxiv/papers/1802/1802.07228.pdf
[28]
Bertrand, A., Maxwell, W. and Vamparys, X. (2020) Are AI-Based Anti-Money Laundering (AML) Systems Compatible with European Fundamental Rights? ICML 2020 Law and Machine Learning Workshop, Vienne, July 2020.
[29]
Truby, J., Brown, R. and Dahdal, A. (2020) Banking on AI: Mandating a Proactive Approach to AI Regulation in the Financial Sector. Law and Financial Markets Review, 14, 110-120. https://doi.org/10.1080/17521440.2020.1760454
[30]
Kurum, E. (2020) RegTech Solutions and AML Compliance: What Future for Financial Crime? Journal of Financial Crime, 30, 776-794. https://doi.org/10.1108/JFC-04-2020-0051
[31]
Claussén Karlsson, M. (2017) Artificial Intelligence and the External Element of the Crime. https://www.diva-portal.org/smash/get/diva2:1115160/FULLTEXT01.pdf
[32]
Isong, R.O. and Schellekens, M. (2019) Robots and Criminal Culpability in the United Kingdom. http://arno.uvt.nl/show.cgi?fid=147344
[33]
Bell, R.E. (2000) Proving the Criminal Origin of Property in Money-Laundering Prosecutions. Journal of Money Laundering Control, 4, 12-25. https://doi.org/10.1108/eb027258
[34]
Johari, R.J., Zul, N.B., Talib, N. and Hussin, S.A.H.S. (2020) Money Laundering: Customer Due Diligence in the Era of Cryptocurrencies. 1st International Conference on Accounting, Management and Entrepreneurship (ICAMER 2019), Cirebon, 26 September 2019, 130-135. https://doi.org/10.2991/aebmr.k.200305.033
[35]
ElYacoubi, D. (2020) Challenges in Customer Due Diligence for Banks in the UAE. Journal of Money Laundering Control, 23, 527-539. https://doi.org/10.1108/JMLC-08-2019-0065
[36]
Suntura, J.H.C. (2019) Customer Identification in Currency Exchange Companies as Per FATF Recommendations. Journal of Money Laundering Control, 23, 96-102.
[37]
Zhang, Y. and Trubey, P. (2019) Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection. Computational Economics, 54, 1043-1063. https://link.springer.com/article/10.1007/s10614-018-9864-z https://doi.org/10.1007/s10614-018-9864-z
[38]
Chen, T.H. (2020) Do You Know Your Customer? Bank Risk Assessment Based on Machine Learning. Applied Soft Computing, 86, Article ID: 105779. https://doi.org/10.1016/j.asoc.2019.105779
[39]
Mondal, P.C., Deb, R. and Huda, M.N. (2016) Know Your Customer (KYC) Based Authentication Method for Financial Services through the Internet. 2016 19th International Conference on Computer and Information Technology (ICCIT), Dhaka, 18-20 December 2016, 535-540. https://doi.org/10.1109/ICCITECHN.2016.7860255
[40]
Meunier, D. (2018) Hidden Beneficial Ownership and Control: Canada as a Pawn in the Global Game of Money Laundering. CD Howe Institute. https://www.cdhowe.org/sites/default/files/attachments/research_papers/mixed/Final%20for%20advance%20release%20Commentary_519_0.pdf
[41]
Colladon, A.F. and Remondi, E. (2017) Using Social Network Analysis to Prevent Money Laundering. Expert Systems with Applications, 67, 49-58.
[42]
Knobel, A. (2017) Technology and Online Beneficial Ownership Registries: Easier to Create Companies and Better at Preventing Financial Crimes. https://www.taxjustice.net/wp-content/uploads/2017/06/Technology-and-online-beneficial-ownership-registries-June-1-1.pdf
[43]
Whiting, J. (2020) Leveraging Artificial Intelligence to Revolutionise Ultimate Beneficial Ownership Discovery. The AI Journal. https://aijourn.com/leveraging-artificial-intelligence-to-revolutionise-ultimate-beneficial-ownership-discovery/
