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Modeling cross-border financial flows using a network theoretic approach
Thesis (PhD)--University of Pretoria, 2021.
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| Format: | Thesis |
| Language: | en_US |
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University of Pretoria
2021
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| _version_ | 1867613480774795264 |
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| access_status_str | Open Access |
| author2 | Adetunji, Olufemi |
| author_browse | Adetunji, Olufemi |
| author_facet | Adetunji, Olufemi |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Thesis (PhD)--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/78773 |
| institution | University of Pretoria (South Africa) |
| language | en_US |
| last_indexed | 2026-06-10T12:36:49.486Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/78773 Modeling cross-border financial flows using a network theoretic approach Adetunji, Olufemi chakasekgoka@gmail.com Yadavalli, Venkata S. Sarma Sekgoka, Chaka Patrick UCTD Complex Networks Money Laundering Risk-based Approach Directed and Weighted Bipartite Graph Node Centrality Thesis (PhD)--University of Pretoria, 2021. Criminal networks exploit vulnerabilities in the global financial system, using it as a conduit to launder criminal proceeds. Law enforcement agencies, financial institutions, and regulatory organizations often scrutinize voluminous financial records for suspicious activities and criminal conduct as part of anti-money laundering investigations. However, such studies are narrowly focused on incidents and triggered by tip-offs rather than data mining insights. This research models cross-border financial flows using a network theoretic approach and proposes a symmetric-key encryption algorithm to preserve information privacy in multi-dimensional data sets. The newly developed tools will enable regulatory organizations, financial institutions, and law enforcement agencies to identify suspicious activity and criminal conduct in cross-border financial transactions. Anti-money laundering, which comprises laws, regulations, and procedures to combat money laundering, requires financial institutions to verify and identify their customers in various circumstances and monitor suspicious activity transactions. Instituting anti-money laundering laws and regulations in a country carries the benefit of creating a data-rich environment, thereby facilitating non-classical analytical strategies and tools. Graph theory offers an elegant way of representing cross-border payments/receipts between resident and non-resident parties (nodes), with links representing the parties' transactions. The network representations provide potent data mining tools, facilitating a better understanding of transactional patterns that may constitute suspicious transactions and criminal conduct. Using network science to analyze large and complex data sets to detect anomalies in the data set is fast becoming more important and exciting than merely learning about its structure. This research leverages advanced technology to construct and visualize the cross-border financial flows' network structure, using a directed and dual-weighted bipartite graph. Furthermore, the develops a centrality measure for the proposed cross-border financial flows network using a method based on matrix multiplication to answer the question, "Which resident/non-resident nodes are the most important in the cross-border financial flows network?" The answer to this question provides data mining insights about the network structure. The proposed network structure, centrality measure, and characterization using degree distributions can enable financial institutions and regulatory organizations to identify dominant nodes in complex multi-dimensional data sets. Most importantly, the results showed that the research provides transaction monitoring capabilities that allow the setting of customer segmentation criteria, complementing the built-in transaction-specific triggers methods for detecting suspicious activity transactions. Banking Sector Education and Training Authority (BANKSETA) UP Postgraduate Bursary Industrial and Systems Engineering PhD Unrestricted 2021-02-19T07:54:43Z 2021-02-19T07:54:43Z 2021-04-21 2021-02-18 Thesis Sekgoka, CP 2021, Modeling cross-border financial flows using a network theoretic approach, PhD Thesis, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/78773> A2021 http://hdl.handle.net/2263/78773 en_US © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Complex Networks Money Laundering Risk-based Approach Directed and Weighted Bipartite Graph Node Centrality Modeling cross-border financial flows using a network theoretic approach |
| title | Modeling cross-border financial flows using a network theoretic approach |
| title_full | Modeling cross-border financial flows using a network theoretic approach |
| title_fullStr | Modeling cross-border financial flows using a network theoretic approach |
| title_full_unstemmed | Modeling cross-border financial flows using a network theoretic approach |
| title_short | Modeling cross-border financial flows using a network theoretic approach |
| title_sort | modeling cross border financial flows using a network theoretic approach |
| topic | UCTD Complex Networks Money Laundering Risk-based Approach Directed and Weighted Bipartite Graph Node Centrality |
| url | http://hdl.handle.net/2263/78773 |