Effects of Artificial Intelligence on Money Laundering in Southern Africa

Mufaro Dzingirai ()
Additional contact information
Mufaro Dzingirai: Midlands State University

A chapter in Towards Digitally Transforming Accounting and Business Processes, 2024, pp 483-500 from Springer

Abstract: 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.

Keywords: Artificial intelligence; Money laundering; Big data; Data analytics; Blockchain; Digital transformation (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-46177-4_26

Ordering information: This item can be ordered from
http://www.springer.com/9783031461774

DOI: 10.1007/978-3-031-46177-4_26

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().