Scameter: Hong Kong’s New Tool In Fight Against Fraud

October 12, 2022
The Hong Kong Monetary Authority has launched a new search engine tool that allows consumers to screen payees for connections to fraud cases prior to making a transaction.

The Hong Kong Monetary Authority (HKMA) has launched a new search engine tool that allows consumers to screen payees for connections to fraud cases prior to making a transaction.

Known as the Scameter, members of the public can use it to input bank account numbers, telephone numbers and other Faster Payment System (FPS) proxies.

The inputs are then checked against Hong Kong Police Force (HKPF) databases containing information on identifiers that have been previously linked to scams.

Likewise, bank staff and stored-value facilities (SVF), such as payment service providers (PSPs), can use the Scameter to inform their own anti-fraud policies, processes and decision-making.

The launch of the Scameter is part of a wider campaign to improve intelligence sharing between the central bank, commercial banks, the Hong Kong police and the public when it comes to fraud.

In a new presentation published last week, the HKMA said that the island has seen a “rapid increase” in fraud cases due to the increased availability of online financial services and a surge in the use of those services during the pandemic.

In the first half of 2022, according to the HKPF, fraud cases increased by 42 percent compared with a year ago, causing losses of about HK$2.1bn ($267m) from more than 12,000 cases.

Shopping fraud, employment fraud, investment fraud and telephone deception accounted for more than 60 percent of the total cases.

In response, the HKMA said it is working with banks, SVF licensees, local and global partners to introduce new preventative measures to tackle fraud.

As part of these efforts, the HKMA said it is using technology and data to help strengthen banks’ and SVF licensees’ ability to identify high-risk activity quickly, and to share their analysis and intelligence with the wider payments ecosystem.

“It is these partnerships between banks, SVF licensees, the HKMA and HKPF where we see the greatest value,” said Arthur Yuen, deputy chief executive of the HKMA.

FMLIT - A work in progress

The first initiative to bring these parties together is the Fraud and Money Laundering Intelligence Taskforce (FMLIT). Launched by the HKMA and HKPF in 2017, it now has 23 participating retail banks, including all virtual banks.

Via the FMLIT, intelligence sharing on specific fraud cases has helped banks identify more than 19,000 previously unknown mule accounts, leading to further action from law enforcement.

Best practices are also shared throughout the FMLIT network. These include HKMA guidelines on the use of non-traditional data, such as IP addresses, to spot fraud, and the introduction of 24/7 stop payment features by retail banks for when new intelligence on scams is received.

Similarly, when victims report to the HKPF that they have been defrauded, bank staff can offer immediate assistance in intercepting funds after receiving intelligence from the HKPF.

In 2021 alone, more than $2.2bn of criminal proceeds was stopped by banks using this system.

“Our guidance emphasises the need to be agile, leveraging advances in technology and data while constantly fine-tuning systems to respond as effectively as possible,” said Yuen.

The HKMA has also worked with the Hong Kong Association of Banks (HKAB) to update its practical guidance on fraud prevention and money laundering in a frequently asked questions (FAQs) format.

The AML Regtech Lab (AMLab)

In a related campaign, in November last year the HKMA launched its Anti-Money Laundering Regtech Lab (AMLab) series.

Under its "Fintech 2025" strategy to promote that "All banks go fintech", the HKMA launched the AMLab series in partnership with Cyberport, a tech-focused business park, and Deloitte.

As noted in the HKMA’s latest quarterly bulletin, the aim of the AMLab series is to help banks explore and adopt regtech solutions that can enhance their anti-money laundering/counter-terrorism financing (AML/CTF) activities.

“Adoption of such tools can strengthen banks’ capabilities to protect customers from losses due to fraud and other financial crime, reduce the displacement of risks to other institutions and raise the overall effectiveness of the AML ecosystem,” said the HKMA.

The AMLab series provides a collaborative platform for continuing peer-group sharing of operational, hands-on experience of regtech approaches, focusing on solutions such as network analytics and workflow automation.

Through the AMLab series, the HKMA is working with banking and fintech partners to strengthen the “gatekeeper” role of banks and encourage the wider use of data and technology to improve the efficiency and effectiveness of AML/CTF controls.

AMLab 1, for example, focuses on network analytics, a technology that has shown potential to help address the problem of fraud mule accounts.

It showed that network analytics could enhance data and information sharing through AML public-private partnerships, similar to the FMLIT.

For the first time, a group of five participating banks, with the assistance of data experts, used synthetic data to experiment with network diagrams to identify suspected money-mule accounts and learn how to integrate alternative data, such as IP addresses, into more traditional data sets, such as transactional data, for analysis.

This allowed the banks to develop skills and capabilities in applying network analytics to identify previously hidden money laundering risks.

AMLab 2, launched in July this year, focused on “enabling technologies”, such as robotic process automation, low-code/no-code platforms and visualisation tools designed to present complex data in a more digestible format.

“One aim of the AMLab series is to counteract the usual impression that regtech involves complex, expensive technologies requiring advanced coding and other skills and, therefore, is only for big banks with deep pockets that can afford teams of highly-skilled and scarce data scientists,” said the HKMA.

“While advanced data analytics techniques and data specialists have a definite role to play, there are other tools that AML/CTF specialists can use to support their work without acquiring advanced coding skills.”

As noted in a VIXIO feature on AML software published last month, such tools can often automate time-consuming routine monitoring tasks, freeing up specialist staff to focus on higher value-added tasks.

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