Please use this identifier to cite or link to this item: https://cris.library.msu.ac.zw//handle/11408/6061
Title: Effects of Artificial Intelligence on Money Laundering in Southern Africa
Authors: Mufaro Dzingirai
Midlands State University
Keywords: Artificial Intelligence
Money Laundering
Southern Africa
Issue Date: 2024
Publisher: Springer Nature Switzerland
Abstract: It is interesting to observe that artificial intelligence is gaining popularity in both developing and developed countries as it attracted the interest of accounting, business, and management professionals. This necessitates the need to scrutinise the interaction between artificial intelligence and money laundering. There is an ongoing debate concerning the justifications of artificial intelligence in dealing with money laundering. In this regard, the Southern Africa region is no exception to money laundering just like any other region. As such, the application of artificial intelligence appears to be a rational strategy to curb financial leakages in the finance sector. Although there is an increase in the adoption of artificial intelligence, scanty is known concerning the association between the application of artificial intelligence and money laundering, especially in the Southern Africa region. In this respect, this research aims to provide the effects of artificial intelligence on money laundering in the Southern African region. The study adopted the structured literature review methodology and then six positive effects were observed. These are detecting money laundering activities, enhancing legal compliance, augmenting customer behavioural analytics, detecting money laundering networks, robust financial crime risk computation, and informing evidence-based policy formulation. However, the negative effects are in the form of infringing customer privacy rights, and poor data governance. Despite the existence of few negative effects, it is concluded that artificial intelligence helps to combat money laundering in the Southern African region. As such, it is suggested that financial institutions should up-skill their personnel and up-scale their business intelligence projects.
URI: https://cris.library.msu.ac.zw//handle/11408/6061
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