The Monetary Authority of Singapore (MAS) plans to launch a high-tech platform to help market participants share information on customers that exhibit major risk “red flags”, warning each other of potential criminal activity.
The public-private collaboration is underway between MAS, the Commercial Affairs Department and several major banks and is part of the Singaporean central bank’s tech-driven approach to combating money laundering and the financing of terrorism (CFT), a senior official at MAS said.
Financial firms in Singapore have been encouraged to adopt data analytics in a way that is commensurate with the risk profile of their business. In making this assessment, they need to consider three major elements, including customer, networks and systems. The public-private partnership addresses these issues from the system element perspective, according to MAS.
Firms are expected to know their customer better, proactively detect and assess changes in customers’ risk profile in a dynamic fashion from the customer perspective. Firms are also encouraged, at the network level, to identify and disrupt illicit fund flows between customers in a network. This will help banks take a more holistic approach, rather than a silo view of the customer’s behaviour and activities, said Loo Siew Yee, assistant managing director of policy, payments and financial crime at MAS.
“A long-standing obstacle to the effective detection of illicit financial flows lies in the limited channels that FIs have to alert each other. Criminals exploit this by layering transactions across multiple FIs to evade detection,” Loo said.
Systematic approach
At the system level, firms are being encouraged to amplify the effectiveness of data analytics through close collaboration between the public and private sectors, as well as within the industry, she said.
“We need to build defences at all levels and mobilising these three data analytics elements together will engender a paradigm shift in the disruption of financial crime,” Loo told the Wealth Management Institute Industry Forum on the Future of Anti-Money Laundering (AML) with Artificial Intelligence (AI) and Machine Learning, held late last week.
The Wealth Management Institute and Nanyang Technological University are conducting research to enable secure, privacy-protected sharing of intelligence across financial firms.
“This and other promising techniques, such as federated machine learning, could hold the key to analytics-driven collaboration on a large scale, which would enable us to be more effective at ferreting out criminal activity at the customer, bank and industry level,” Loo said.
Technology, while crucial, was only part of the equation, she said.
“Sharing of information needs to take place within a robust legal and technical framework, to maximise its effectiveness and address legitimate concerns about the loss of privacy and misuse or theft of data,” she said.
Risk detection via customer assessments
It is now possible to integrate and analyse changes in the customer’s behavioural, transactional and profile data holistically and in a more timely manner, via applying technology solutions.
This would then allow companies to assess and update the customer’s risk assessment on a more “real-time” basis, rather than performing customer reviews in a fixed cycle.
“This means that financial institutions are alerted to customers with potential higher risk concerns, based on various combinations of behavioural red flag indicators or features of typologies, to cater for evolving or emerging risks in different business segments,” Loo said. “Once alerted, financial institutions can then perform timely and effective reviews which are focused on addressing these concerns.”
This would allow the financial institutions to better focus its resources on the higher risk and higher impact cases, she said.
Traditionally, companies put in place “cycle-based” customer risk assessments may not be updated until the review itself is being conducted or there is adverse news or other overt risk trigger events.\
The shift from the cycle-based periodic risk assessment to this more dynamic approach requires deliberate efforts in system and process changes. “There needs to be sufficient validation of increased effectiveness over existing practices and also involves upskilling of staff to recognise, prioritise and act on the risk signals,” Loo said.
Banks have been making a sensible start in specific business segments, such as those exhibiting higher risks, including private banking, as well as small and medium-sized enterprises customers, with a view to scale up to other areas, she said.
Identify suspicious customers networks
Dynamic customer risk assessment is a significant improvement but more still needs to be done, Loo said. As criminals collude via sophisticated illicit financial networks, focusing on an entity-level assessment may “miss the forest for the trees” and fail to detect that seemingly innocuous transactions are part of a larger web of activity.
“Some firms have successfully applied network link analysis to detect and visualise connections among customers and their transaction flows with greater ease. This has proven particularly useful in the detection of networks of shell and front companies or nominees used to facilitate illicit activities,” she said.
In wealth management, such techniques have uncovered unusual links and transaction flows and concealment of true beneficial owners.
Financial firms are using network risk-scoring and centrality analysis to prioritise higher risk networks for review. The MAS is offering financial assistance schemes to help further data analytics development among companies to support them.
Data analytics for AML/CFT practice
Data analytics techniques will be a mainstay of the future AML/CFT landscape, Loo said. Companies should actively consider the training needs of their staff, which should not be just about hiring data scientists or engaging external solutions providers.
“Financial institutions should equip your AML/CFT professionals with the skills to make full use of these promising tools, to understand and properly apply the insights from data analytics and collaborate with analytics experts to develop successful AML/CFT solutions.”
Written by: Zeng Yixiang, Regulatory Intelligence Correspondent for Southeast Asia, Thomson Reuters
