Privacy-preserving data mining of cross-border financial flows

dc.contributor.authorSekgoka, Chaka Patrick
dc.contributor.authorYadavalli, Venkata S. Sarma
dc.contributor.authorAdetunji, Olufemi
dc.date.accessioned2022-07-18T05:00:30Z
dc.date.available2022-07-18T05:00:30Z
dc.date.issued2022
dc.description.abstractCriminal networks continue to utilize the global financial system to launder their proceeds of crime, despite the broad enactment of anti-money laundering (aml) laws and regulations in many countries. Money laundering consumes capital resources and the tax revenue needed to fund infrastructure development and alleviate poverty in developing market economies. This paper, therefore, expands on the tools available for enabling privacy-preserving data mining in multidimensional datasets to combat cross-border money laundering. Most importantly, this paper develops a novel measure for detecting anomalies in cross-border financial networks, allowing financial institutions and regulatory organizations to identify suspicious nodes. The research used a sample dataset comprising international financial transactions and a hypothetical dataset to demonstrate the measure of node importance and the symmetric-key encryption algorithm. The results support the argument that the proposed network measure can detect node anomalies in the cross-border financial flows network, enabling regulatory authorities and law enforcement agencies to investigate financial transactions for suspicious activity and criminal conduct. The encryption algorithm can ensure adherence to information privacy laws and policies without compromising data reusability. Hence, the proposed methodology can improve the proactive management of money laundering risks associated with cross-border fund flows for the global financial system’s benefit.en_US
dc.description.departmentIndustrial and Systems Engineeringen_US
dc.description.librariandm2022en_US
dc.description.sponsorshipBanking Sector Education and Training Authority Bankseta.en_US
dc.description.urihttp://www.tandfonline.com/loi/oaen20en_US
dc.identifier.citationChaka Patrick Sekgoka, Venkata Seshachala Sarma Yadavalli & Olufemi Adetunji (2022) Privacy-preserving data mining of cross-border financial flows, Cogent Engineering, 9:1, 2046680, DOI: 10.1080/23311916.2022.2046680.en_US
dc.identifier.issn2331-1916 (online)
dc.identifier.other10.1080/23311916.2022.2046680
dc.identifier.urihttps://repository.up.ac.za/handle/2263/86261
dc.language.isoenen_US
dc.publisherCogent OAen_US
dc.rights© 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.en_US
dc.subjectInformation privacyen_US
dc.subjectSymmetric-key encryptionen_US
dc.subjectBipartite graphen_US
dc.subjectCross-border financial flowsen_US
dc.subjectCentralityen_US
dc.subjectAnti-money launderingen_US
dc.titlePrivacy-preserving data mining of cross-border financial flowsen_US
dc.typeArticleen_US

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