Identifying money laundering risk in the United Kingdom: observations from national risk assessments and a proposed alternative methodology
Two National Risk Assessments (NRA) of money laundering (ML) have now been published in the United Kingdom (see HM Treasury 2015 and 2017). While both represent an attempt to identify the risks of ML, there are limitations in relation to the conceptual framework and the methodology used. This paper...
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| Format: | Electronic Article |
| Language: | English |
| Published: |
2019
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| In: |
European journal on criminal policy and research
Year: 2019, Volume: 25, Issue: 1, Pages: 63-82 |
| Online Access: |
Presumably Free Access Volltext (Verlag) |
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| Check availability: | HBZ Gateway |
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MARC
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| 245 | 1 | 0 | |a Identifying money laundering risk in the United Kingdom |b observations from national risk assessments and a proposed alternative methodology |c Matt Hopkins, Nikki Shelton |
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| 520 | |a Two National Risk Assessments (NRA) of money laundering (ML) have now been published in the United Kingdom (see HM Treasury 2015 and 2017). While both represent an attempt to identify the risks of ML, there are limitations in relation to the conceptual framework and the methodology used. This paper reviews the UK NRAs and considers whether revision of the methodology employed could help to both remedy these limitations and generate more robust findings. Drawing upon the findings of the UK strand of project Identifying and Assessing the Risk of Money Laundering in Europe (IARM), it outlines how a composite ML risk indicator was developed through analysis of threats and vulnerabilities across 43 police areas. The findings demonstrate that risks are highest in the City of London and the Metropolitan Police area, which is largely explained by the presence of organised crime groups, connections to risky jurisdictions and the cash intensity of businesses. Although the findings should be treated with caution, it is posited that this methodology could be used to help future NRAs develop a more robust framework to understand ML risk and ultimately develop more effective preventative strategies. | ||
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