dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Shafiee Kamalabad, Mahdi | |
dc.contributor.author | Aken, Jelmer van | |
dc.date.accessioned | 2025-08-21T00:02:57Z | |
dc.date.available | 2025-08-21T00:02:57Z | |
dc.date.issued | 2025 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/49837 | |
dc.description.abstract | Shell companies are often used to hide ownership and facilitate illicit financial flows. The disclosure of the Offshore Leaks have shown highlighted how deeply embedded and widespread these opaque corporate setups can be. International efforts exist to enhance regulatory transparency and cooperation, but enforcement remains fragmented and inconsistent across jurisdictions. This thesis investigates the structural patterns of shell-like corporate entities under two regulatory environments: the Netherlands and offshore jurisdictions. Using motif analysis on large-scale corporate network data, it identifies recurring structures of nominee behavior and mailbox strategies that may be employed to obscure ownership. In both environments nominee-like motifs are found to be prevalent, whereas address clustering is generally more overrepresented in the Dutch network but underrepresented offshore. This contrast suggests greater centralization in conduit jurisdictions like the Netherlands, and deeper secrecy practices in traditional offshore havens. The findings emphasize the potential of motif-based approaches to illuminate hidden corporate behavior. Future research could extend this work by incorporating temporal dynamics and developing automated motif detection systems for risk identification. | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | Mapping Economic Crime: Analyzing Money Laundering Patterns using Network Science | |
dc.title | A Motif-Based Comparative Analysis of Shell Company Structures in National and Offshore Contexts | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.courseuu | Applied Data Science | |
dc.thesis.id | 52090 | |