IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v18y2025i8p441-d1719367.html
   My bibliography  Save this article

Disruption in Southern Africa’s Money Laundering Activity by Artificial Intelligence Technologies

Author

Listed:
  • Michael Masunda

    (Department of Management Studies, National Forensic Sciences University, Gandhinagar 382007, India)

  • Haresh Barot

    (Department of Management Studies, National Forensic Sciences University, Gandhinagar 382007, India)

Abstract

The rise in illicit financial activities across the South Africa–Zimbabwe corridor, with an estimated annual loss of $3.1 billion demands advanced AI solutions to augment traditional detection methods. This study introduces FALCON, a groundbreaking hybrid transformer–GNN model that integrates temporal transaction analysis (TimeGAN) and graph-based entity mapping (GraphSAGE) to detect illicit financial flows with unprecedented precision. By leveraging data from South Africa’s FIC, Zimbabwe’s RBZ, and SWIFT, FALCON achieved 98.7%, surpassing Random Forest (72.1%) and human auditors (64.5%), while reducing false positives to 1.2% (AUC-ROC: 0.992). Tested on 1.8 million transactions, including falsified CTRs, STRs, and Ethereum blockchain data, FALCON uncovered $450 million laundered by 23 shell companies with a cross-border detection precision of 94%, directly mitigating illicit financial flows in Southern Africa. For regulators, FALCON met FAFT standards, yielding 92% court admissibility, and its GDPR-compliant design (ε = 1.2 differential privacy) met stringent legal standards. Deployed on AWS Graviton3, FALCON processed 2 million transactions/second at $0.002 per 1000 transactions, demonstrating real-time scalability, making it cost-effective for financial institutions in emerging markets. As the first AI framework tailored for Southern Africa’s financial ecosystems, FALCON sets a new benchmark for ethical AML solutions in emerging economies with immediate applicability to CBDC supervision. The transparent validation of publicly available data underscores its potential to transform global financial crime detection.

Suggested Citation

  • Michael Masunda & Haresh Barot, 2025. "Disruption in Southern Africa’s Money Laundering Activity by Artificial Intelligence Technologies," JRFM, MDPI, vol. 18(8), pages 1-18, August.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:8:p:441-:d:1719367
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/18/8/441/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/18/8/441/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ren, Yi-Shuai & Ma, Chaoqun & Wang, Yiran, 2024. "A new financial regulatory framework for digital finance: Inspired by CBDC," Global Finance Journal, Elsevier, vol. 62(C).
    2. Yan Zhang & Peter Trubey, 2019. "Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1043-1063, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abbas Haider & Hui Wang & Bryan Scotney & Glenn Hawe, 2022. "Predictive Market Making via Machine Learning," SN Operations Research Forum, Springer, vol. 3(1), pages 1-21, March.
    2. Zanin, Luca, 2020. "Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    3. Petra P. Šimović & Claire Y. T. Chen & Edward W. Sun, 2023. "Classifying the Variety of Customers’ Online Engagement for Churn Prediction with a Mixed-Penalty Logistic Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 451-485, January.
    4. Durongkadej, Isarin & Hu, Wenyao & Wang, Heng Emily, 2024. "How artificial intelligence incidents affect banks and financial services firms? A study of five firms," Finance Research Letters, Elsevier, vol. 70(C).
    5. Petra Posedel v{S}imovi'c & Davor Horvatic & Edward W. Sun, 2021. "Classifying variety of customer's online engagement for churn prediction with mixed-penalty logistic regression," Papers 2105.07671, arXiv.org, revised Jul 2021.
    6. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
    7. Ma, Chao-Qun & Liu, Xukang & Klein, Tony & Ren, Yi-Shuai, 2025. "Decoding the nexus: How fintech and AI stocks drive the future of sustainable finance," International Review of Economics & Finance, Elsevier, vol. 98(C).
    8. Liu, Xukang & Ma, Chao-Qun & Ren, Yi-Shuai, 2025. "ESG reactions to fintech: The role of cross-border capital flows," Research in International Business and Finance, Elsevier, vol. 76(C).
    9. Umar, Muhammad, 2025. "Does CBDC launch increase tax collection? empirical evidence from China," Finance Research Letters, Elsevier, vol. 71(C).
    10. Chen, Jian & Katchova, Ani L. & Zhou, Chenxi, . "Agricultural loan delinquency prediction using machine learning methods," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(5).
    11. Albina Gaisina & Matthias Finger, 2025. "Central Bank Digital Currencies (CBDCs): a countermeasure to Anti-Money Laundering (AML) challenges posed by cryptocurrencies?," Digital Finance, Springer, vol. 7(2), pages 201-254, June.
    12. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    13. Hongcheng Ling & Xiaohong Ling, 2025. "The impact of digital inclusive finance on enterprise digital technology innovation: empirical evidence from the Chinese manufacturing industry," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-19, December.
    14. Königstorfer, Florian & Thalmann, Stefan, 2020. "Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:18:y:2025:i:8:p:441-:d:1719367. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.