Using data analytics to distinguish legitimate and illegitimate shell companies
Shell companies can be a legitimate entity but can also been used for illicit activities such as money laundering. Users of shell companies have included illegal arms dealers, drug cartels, terrorists and cyber-criminals, as well as legitimate businesses. To assist in distinguishing between legitima...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Journal of Economic Criminology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949791424000757 |
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| _version_ | 1841555649423474688 |
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| author | Milind Tiwari Adrian Gepp Kuldeep Kumar |
| author_facet | Milind Tiwari Adrian Gepp Kuldeep Kumar |
| author_sort | Milind Tiwari |
| collection | DOAJ |
| description | Shell companies can be a legitimate entity but can also been used for illicit activities such as money laundering. Users of shell companies have included illegal arms dealers, drug cartels, terrorists and cyber-criminals, as well as legitimate businesses. To assist in distinguishing between legitimate and illegitimate uses of shell companies, we develop a data-driven model to detect shell companies that are being used for money laundering. We use a hybrid approach combining graph analytics and supervised machine learning. The resulting detection models have an impressive classification accuracy ranging between 88.17 % and 97.85 %. We found no prior study that developed such models to detect illicit shell companies using publicly available information as done with our models. Beneficiaries of this work include government officials and compliance professionals, particularly accountants, tax officials and anti-corruption agencies. |
| format | Article |
| id | doaj-art-0437995c38404f8d83f018d53a029785 |
| institution | Kabale University |
| issn | 2949-7914 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Economic Criminology |
| spelling | doaj-art-0437995c38404f8d83f018d53a0297852025-01-08T04:53:53ZengElsevierJournal of Economic Criminology2949-79142025-03-017100123Using data analytics to distinguish legitimate and illegitimate shell companiesMilind Tiwari0Adrian Gepp1Kuldeep Kumar2Australia Graduate School of Policing and Security, Charles Sturt University, Barton, Australian Capital Territory 2660, Australia; Corresponding author.Bangor Business School, Bangor University, Bangor, Gwynedd LL572DG, United Kingdom; Centre for Data Analytics, Bond University, Queensland 4229, AustraliaCentre for Data Analytics, Bond University, Queensland 4229, AustraliaShell companies can be a legitimate entity but can also been used for illicit activities such as money laundering. Users of shell companies have included illegal arms dealers, drug cartels, terrorists and cyber-criminals, as well as legitimate businesses. To assist in distinguishing between legitimate and illegitimate uses of shell companies, we develop a data-driven model to detect shell companies that are being used for money laundering. We use a hybrid approach combining graph analytics and supervised machine learning. The resulting detection models have an impressive classification accuracy ranging between 88.17 % and 97.85 %. We found no prior study that developed such models to detect illicit shell companies using publicly available information as done with our models. Beneficiaries of this work include government officials and compliance professionals, particularly accountants, tax officials and anti-corruption agencies.http://www.sciencedirect.com/science/article/pii/S2949791424000757Money launderinggraph analyticsshell companyfinancial crimedata analytics |
| spellingShingle | Milind Tiwari Adrian Gepp Kuldeep Kumar Using data analytics to distinguish legitimate and illegitimate shell companies Journal of Economic Criminology Money laundering graph analytics shell company financial crime data analytics |
| title | Using data analytics to distinguish legitimate and illegitimate shell companies |
| title_full | Using data analytics to distinguish legitimate and illegitimate shell companies |
| title_fullStr | Using data analytics to distinguish legitimate and illegitimate shell companies |
| title_full_unstemmed | Using data analytics to distinguish legitimate and illegitimate shell companies |
| title_short | Using data analytics to distinguish legitimate and illegitimate shell companies |
| title_sort | using data analytics to distinguish legitimate and illegitimate shell companies |
| topic | Money laundering graph analytics shell company financial crime data analytics |
| url | http://www.sciencedirect.com/science/article/pii/S2949791424000757 |
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