Anti-Money Laundering News and Information

HKMA Publishes Cases Studies on AML/CFT Regtech Adoption

New report details case studies and insights from banks that have implemented technologies to enhance the efficiency and effectiveness of their AML/CFT efforts.

The HKMA (Hong Kong Monetary Authority) has published a new report highlighting the opportunities that Regtech offers to transform the effectiveness and efficiency of AML/CFT efforts.

Following the first AML/CFT RegTech Forum in November 2019, the HKMA says it collaborated with Deloitte to follow up on progress made by three breakout groups of banks.

The report says 80 percent of Accelerator banks – those at an early stage of the adoption cycle – are now using or planning to use AML/CFT Regtech solutions.

Meanwhile, 77 percent of Enabler banks – those considering adopting machine learning – are now either using it, conducting proofs of concept, or have concrete plans to do so.

In addition, the HKMA completed work in 2020 with ten Collaborators banks – mature adopters and members of FMLIT – to build out a common set of fundamental requirements around data, analytics, information delivery, collaboration, and skills and expertise, which will form the basis for thematic work later this year.

The report details case studies and insights from banks that have implemented technologies to enhance the effectiveness and efficiency of AML/CFT efforts:

  • Bank A used network analytics and non traditional data elements such as IP addresses in investigations to identify networks of relationships between customers
  • Bank B used RPA (robotic process automation) in name screening and adverse media searches for correspondent banking profile reviews
  • Bank C used RPA within transaction monitoring alert investigation processes including data retrieval and routine analytic tasks
  • Bank D created an intermediary data repository that aggregated KYC, transactions and trade data, allowing AML/CFT specialists to perform proactive data analytics reviews by directly pulling data extracts from the repository
  • Bank E used machine learning for name screening to reduce the number of manual alert reviews by 35 percent across multiple jurisdictions
  • Bank F used network analytics for fraud and trade based money laundering investigations, allowing it to identify additional companies linked to known illegal activity

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The report also provides practical guidance to banks looking to advance their AML/CFT Regtech adoption efforts, grouped under five themes:

  • Getting started – common initial questions; possible ways to begin; good practices
  • Data and process readiness – key preparatory steps regarding data, processes and the use of network analytics
  • Third-party vendor relationships – how to identify and evaluate potential Regtech providers in a fast-developing field
  • People, talent and culture – necessary knowledge, skills and experience in implementation teams and the often misunderstood role of data scientists
  • Performance metrics and indicators – what success looks like

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“This report is the first time that we share comprehensive and practical, hands-on experience from banks that have actually implemented AML/CFT Regtech,” said HKMA chief Arthur Yuen.

“The case studies show the importance of early and continuing stakeholder buy-in; interdisciplinary adoption teams; forums to share views and experience; and being able to track and measure success.”

The HKMA encourages banks to draw reference from the report to inform their approach to adopting Regtech in their AML/CFT activities.

“The adoption of Regtech for AML/CFT will continue to be a strong focus in the HKMA’s supervisory engagement with the industry as well as individual banks,” Yuen said.

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For the Full Report (PDF): Press Here

For Inquiries, Services, Consulting nad Recommendations regarding AML/CTF Technologies – Contact us at info@i-aml.com

By Manesh Samtani, January 22, 2021, Published on Regulation Asia

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