HK regulator launches AML Regtech Lab
The Hong Kong Monetary Authority (HKMA) today announced the launch of its Anti-Money Laundering (AML) Regtech Lab, in collaboration with Cyberport and supported by Deloitte, to further encourage the use of Regtech under the “Fintech 2025” strategy.
The Lab will strengthen banks’ capabilities to protect customers from fraud and financial crime losses, reduce risk displacement across the banking sector and raise the overall effectiveness of the AML ecosystem. This project focuses on using Network Analytics to address the risks of fraud-related mule accounts, enhancing data and information sharing through public-private partnership efforts in AML.
The first group of five banks will:
- use synthetic data to experiment with network diagrams for identifying suspected money mule;
- learn how to integrate alternative data (e.g. IP address) into more traditional data sets (e.g. transactional data) for analysis; and
- develop skills and capabilities to apply network analytics to identify hidden money laundering risks.
AMLab series is the next phase of the HKMA’s engagement with a wide range of banks to help inform decisions about Regtech adoption, building on the positive momentum since the AML/CFT Regtech Forum in 2019 as well as experience shared through AML/CFT Regtech: Case Studies and Insights issued in January 2021.
In particular the banking industry are making good progress in adopting AML Regtech:
- over 60% (120) of banks which had not started in 2019 have now introduced Regtech tools, such as Robotic Process Automation, Natural Language Processing and no-code workflow automation solutions, to optimize AML/CFT work and improve customer experience;
- 53 banks are using or exploring the use of alternative data and 70% of these banks have identified otherwise unknown unusual relationships and transactions as a result; and
- 19 banks are using or exploring network analytics.
AMLab series will provide a collaborative platform for ongoing peer group sharing of operational, hands-on experience of Regtech approaches, focusing on solutions such as machine learning in transaction monitoring process, low/no code workflow automation solutions, in addition to network analytics.