Using financial transaction data to analyse, detect and disrupt technologically facilitated crime
Technologically facilitated crime is particularly difficult to detect and disrupt using traditional policing methodologies. This chapter considers how financial transactions data may be used in isolation, and when joined with criminal history and demographic data, to assist detection and disruption...
| 1. VerfasserIn: | |
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| Medienart: | Druck Aufsatz |
| Sprache: | Englisch |
| Veröffentlicht: |
2024
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| In: |
The crime data handbook
Jahr: 2024, Seiten: 245-259 |
| Verfügbarkeit prüfen: | HBZ Gateway |
| Schlagwörter: |
MARC
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| 520 | |a Technologically facilitated crime is particularly difficult to detect and disrupt using traditional policing methodologies. This chapter considers how financial transactions data may be used in isolation, and when joined with criminal history and demographic data, to assist detection and disruption of online crime types. We consider the use of these data, and analytical methodologies that are particularly suited to analyse online offending. We discuss the ways in which findings of these analyses can be implemented by law enforcement, financial services and internet service providers to detect offences. Using a case study involving the live streaming of child sexual abuse (CSA), we examine how multi-agency approaches, featuring law enforcement and the financial sector, may assist in the disruption of offences. Live streaming of CSA features the procurement and viewing of sexual abuse of children across the internet in real time, in exchange for money. The nature of this offence type results in barriers to monitoring by law enforcement, as there is little tangible evidence of the offence beyond a financial transaction, and locally stored metadata from the streaming session. This chapter explores how financial transaction data, joined with demographic and criminal history data, may significantly improve insight into the behaviour of these offenders. Joining these data may inform emerging detection methodologies, particularly in partnerships with financial sector organizations. Using financial transaction data provides an opportunity to disrupt a crime type that currently has limited prospects of detection with traditional methodologies. | ||
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