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- 2 to 15 July:<\/strong> 579 individuals connected to more than 1,469 illicit cases and around S$18 million (USD$14 M) in losses.<\/li>\n<\/ul>\n\n\n\n
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- 16 to 29 July:<\/strong> 255 people investigated with ties to 660+ cases and around S$5.6 million (USD$4.3 M) in losses.<\/li>\n<\/ul>\n\n\n\n
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- 30 July to 12 August:<\/strong> 270 scammers associated with 660 cases and S$5.4M million (USD$4.2 M) in losses.<\/li>\n<\/ul>\n\n\n\n
When we add those numbers up, that is over 3502 scam cases and $30 million in reported victim losses in just eight consecutive weeks. This suggests an industrialized enforcement cycle, rather than occasional police crackdowns.<\/p>\n\n\n\n
For compliance teams, the message is significant. Repeated enforcement at this scale can translate to greater scrutiny on firms to demonstrate that their fraud monitoring and Anti-Money Laundering (AML)<\/a> systems are capable of identifying scam related activity before losses escalate. It can also signal more stringent regulations and sharing of data intelligence.<\/p>\n\n\n\n
The Increasing Pervasion of Money Mules<\/h2>\n\n\n\n
Money muling refers to the act of transferring criminal money on behalf of others. Bad actors use emotional tactics, including online friendships, fake job offers, and romance, to get victims to launder cash from their bank accounts, often lying about the intention of the transaction.<\/p>\n\n\n\n
Are all of us susceptible to money mules? More often than we admit, anyone can be susceptible to becoming a money mule. This is especially true today, where we share our information freely on social media, job agencies, and dating applications. As such, criminals can exploit situational, financial, and behavioral vulnerabilities to target people.<\/p>\n\n\n\n
According to the Financial Conduct Authority (FCA), in 2024, the UK recorded over 225,000 people as victims<\/a> of money mules. In Singapore\u2019s case, just two months ago, 1,423 money mules, 1,439 SIM-card mules and 53 corporate mules had already been placed under its Facility Restriction Framework. <\/p>\n\n\n\n
Money muling, however, can act as just one enabling layer within a much broader network of scam infrastructure. So then, the question arises: How, and who, is accountable for dismantling this infrastructure before it hits victims?<\/p>\n\n\n\n
Who is Accountable for Stopping Scam Infrastructure?<\/h2>\n\n\n\n
The truth is, no single regulatory body or organization can stop scams on its own. Scam infrastructure can exist within banks, telecom companies, online platforms, and law enforcement. However, while responsibility is distributed, accountability to identify, prevent, and report scams is not absent. <\/p>\n\n\n\n
Businesses are increasingly expected to demonstrate that they have effective controls to detect fraud, safeguard the financial ecosystem, and share intelligence data with other organizations. The importance of information sharing is particularly clear in FCA findings.<\/p>\n\n\n\n
The FCA’s published data says 25 firms between 2022 and 2023 offboarded 194,084 money mules, but just 37%<\/a> were reported to the National Fraud Database. Additionally, while the cases met the required reporting standards, businesses chose not to submit their details for almost one-third of them.<\/p>\n\n\n\n
This is critical, as data sharing is crucial to disrupting the networks that move scam proceeds and acts as a strong barrier against financial crime. As such, establishing strong AML and Counter-Terrorism Financing (CTF) infrastructure is only one part of the solution.<\/p>\n\n\n\n
What Compliance Teams Must Act on Now?<\/h2>\n\n\n\n
Asking a business to strengthen AML controls can sound simple, but in reality, it can be complex, especially if you do not know where to start. This is particularly true in this case, where risk is fragmented across varying users and channels. The following questions can expose weaknesses in existing controls.<\/p>\n\n\n\n
1. Can we identify behavioral changes, not just large transactions?<\/h3>\n\n\n\n
Too much emphasis on transactional thresholds can lead to a narrow view of risks. Rather, businesses should also monitor behavioral deviations. This can include unusual transaction patterns, such as large incoming payments or increased transfer frequency. This enables businesses to identify and assess meaningful deviations and whether they are linked to suspicious activity.<\/p>\n\n\n\n
2. Can we detect infrastructure reuse?<\/h3>\n\n\n\n
Scam networks do not usually operate via a single platform, account, or channel. As such, businesses should look for recurring connections between suspicious devices<\/a>, email addresses, and phone numbers. These connections help firms detect networks of related activity, thus reducing false positives and focusing investigations on high-risk activities. <\/p>\n\n\n\n
3. Can fraud intelligence insight feed into risk scoring?<\/h3>\n\n\n\n
Fraud intelligence should not end at identification. Firms need strong integration feeds that link fraud insights to risk scoring and case management systems. For example, when account misuse is detected, automated workflows should determine if the individual will need re-verification, reported to authorities, or reviewed by senior management. This makes it hard for scam activity to hide within organizational silos.<\/p>\n\n\n\n
4. What happens after offboarding a high-risk customer?<\/h3>\n\n\n\n
Account closure does not signal the end of the risk management lifecycle. A suspected fraudster can easily open another account, use a different provider, or move activity to another channel. Instead, companies need to properly document, report, and share this information with external intelligence networks.<\/p>\n\n\n\n
![\"Fortify](\"https:\/\/www.complycube.com\/wp-content\/uploads\/2025\/04\/contact-complycube-award-winning-aml-and-kyc-1024x577.png\")
<\/a><\/figure>\n\n\n\n
- 30 July to 12 August:<\/strong> 270 scammers associated with 660 cases and S$5.4M million (USD$4.2 M) in losses.<\/li>\n<\/ul>\n\n\n\n
- 16 to 29 July:<\/strong> 255 people investigated with ties to 660+ cases and around S$5.6 million (USD$4.3 M) in losses.<\/li>\n<\/ul>\n\n\n\n