The dataset counts arrests, which is a record of what police did, and that ceiling sits over every figure in it. The under-30 share works out at 61.8 per cent, and 7,735 charges against 14,493 arrests means a little over half of arrests reached a charge [5][6]. What the counts cannot say is whether under-30s are over-represented, because the analysis reports numbers rather than rates against any population base [21].
The recruitment explanation is where the evidence changes type. Ryder attributes the age pattern to younger people increasingly being recruited as mules, on the strength of the interviews [11]. He is also explicit that no case-level student count exists in the data, and that the student picture comes from a clear view among officers who cite financial hardship, manipulation and limited awareness [8]. That is a mechanism with a plausible shape and no measured incidence attached to it. The thing it does not tell you is what fraction of those 8,956 arrests involved students of any nationality.
The regional table needs the same care. The seven forces named total 2,766 [13], but those are section 327 offences rather than arrests, so they do not sit inside the 14,493 [12][4]. They are also counts without a population base, which is why Leicestershire's 485 landing above the Metropolitan Police's 404 says as much about which forces are running mule-account work as about where laundering happens [12][21]. Mapston's own reading is that enforcement is "concentrated, uneven and resource-driven", and that the system handles simple cases better than complex financial crime [18].
The regulatory finding is the sturdiest part of the work. Higher education institutions sit outside the Money Laundering Regulations, so they carry no duty to report suspicions to the UK Financial Intelligence Unit at the National Crime Agency, and what reaches law enforcement from campuses is limited as a result [9]. The 2025 National Risk Assessment by the Home Office and HM Treasury had already named schools and universities as an emerging vulnerability, citing global reach, financial flows and weaker oversight [14], and the UK Fraud Strategy identifies mule networks as central to laundering fraud proceeds [15]. Banks appear here only as the channel, since a mule moves proceeds through an account [19]. No bank-side detection or reporting figure is given, so the compliance gap this research documents is the universities' one [22].
Ministry of Justice figures for the same period record 108 children and 420 people aged 18 to 24 convicted of money laundering offences [16]. Set 528 against 8,956 and you get 5.9 per cent [17], which is not a conversion rate: different age bands, different measures, different source. What it does bound is how much narrowing happens between an arrest logged by a force and a conviction recorded centrally.
So the measured product of this work is an age distribution in arrest records, and the recruitment account attached to it is testimony from the officers doing the work. The same legal boundary that keeps universities out of the reporting regime is what keeps that testimony from becoming a count.
