The Reliance on Artificial Intelligence Measures to Curb Money Laundering Practices in the South African Banking Institutions and Real Estate Sector
Keywords:artificial intelligence; machine learning; money laundering; banks; automation
Money laundering includes any practice by which illicit perpetrators disguise the original ownership and control of their proceeds of criminal conduct by making them appear to have been derived from legitimate sources. Money laundering practices may give rise to poor market integrity and low public investor confidence in any country. Consequently, money laundering is outlawed in many countries, including South Africa. On the other hand, artificial intelligence (AI) could be defined as the simulation of human intelligence processes by computer systems and/or machines in order to learn or acquire certain information, reasoning and related rules, and/or applying such rules to reach approximate or definite conclusions and self-correction. Put differently, AI also involves the creation of intelligent machines that perform and react like humans. Accordingly, the article unpacks the flaws in the current South African anti-money laundering statutory regulatory framework. This done to, inter alia, recommend the use of artificial intelligence and other relevant measures to enhance the combating of money laundering in the South African banking and related financial institutions. In light of this, the author submits that South African banks should consider adopting artificial intelligence measures to detect and prevent the negative effects of money laundering in the banking sector, and related key sectors such as the real estate and financial markets sectors.
Álvarez-Jareño, J.A., Badal-Valero, E. & Pavía-Miralles, J.M. (2017). Using Machine Learning for Financial Fraud Detection in The Accounts of Companies Investigated for Money Laundering. Universitat Jaume Working Paper Series, 1-18.
Bester, H., Chamberlain, D., de Koker, L., Hougaard, C., Short, R., Smith, A., & Walker, R. (2008). Implementing FATF Standards in Developing Countries and Financial Inclusion: Findings and Guidelines. Genesis Analytics, 5(2), 1-88.
de Koker, L. (2009). The Money Laundering Risk Posed by Low‐Risk Financial Products in South Africa: Findings and Guidelines. Journal of Money Laundering Control, 12(4), 323-339.
de Koker, L. (2008). Money Laundering and Terror Financing Risk Management of Low Risk Financial Products and Services in South Africa. Centre for Financial Regulation and Inclusion (Cenfri) Report for FinMark Trust, 3-32.
de Koker, L. (2003). Money Laundering Control: The South African Model. Journal of Money Laundering Control, 6(2), 166-181.
de Koker, L. (2011). Will RICA’s Customer Identification Data Meet Anti-money Laundering Requirements and Facilitate The Development Of Transformational Mobile Banking in South Africa? An exploratory note. Centre for Financial Regulation and Inclusion (CENFRI), South Africa, 1-21.
Dilek, S., Çakır, H., & Aydın, M. (2015). Applications of Artificial Intelligence Techniques to Combat Cybercrimes: A Review. International Journal of Artificial Intelligence & Applications, 6(1), 21-34.
Ezrachi, A. & Stucke, M.E. (2017). Artificial Intelligence and Collusion: When Computers Inhibit Competition. University of Illinois Law Review, 5, 1775-1809.
Financial Action Task Force, (2007). Guidance on the Risk-based Approach to Combating Money Laundering and Terrorist Financing – High Level Principles and Procedures, High Level Principles and Procedures, 1-42.
Goldfarb, A. & Prince, J. (2008). Internet Adoption and Usage Patterns are Different: Implications for the Digital Divide. Information Economics and Policy, 20(1), 2–15. Kersop, M., & du Toit, S.F. (2015). Anti-money Laundering Regulations and the Effective Use of Mobile Money in South Africa – Part 1. PER Journal, 18(5), 1603-1627.
Kingdon, J. (2004). AI Fights Money Laundering. Applications: Banking, 87-89.
Moodley, M.S. (2008). Money Laundering and Countermeasures: A Comparative Security Analysis of Selected Case Studies with Specific Reference to South Africa. Master of Security Studies, University of Pretoria, 1-93.
Mugarura, N. (2014). Customer Due Diligence (CDD) Mandate and the Propensity of its Application as a Global AML Paradigm. Journal of Money Laundering Control, 17(1), 76-91.
Paula, E.L., Ladeira, M., Carvalho, R.N. & Marzag˜ao, T. (2016). Deep Learning Anomaly Detection as Support Fraud Investigation in Brazilian Exports and Anti-Money Laundering. 15th IEEE International Conference on Machine Learning and Applications, 954-960.
Smith C., McGuire, B., Huang, T., & Yang, G. (2006). The History of Artificial Intelligence. University of Washington Research Paper, 1-27.
Woodsome, J. & Ramachandran, V. (2018). Fixing AML: Can New Technology Help Address the De-risking Dilemma? Center for Global Development Paper Series, 1-83.
How to Cite
The author fully assumes the content originality and the holograph signature makes him responsible in case of trial.